Friday, December 31, 2021

University at Buffalo - Industrial Engineering Programs

21) University at Buffalo,  Buffalo, New York
BS Curriculum: - https://engineering.buffalo.edu/industrial-systems/academics/undergraduate/requirements-bs.html
MS Curriculum:- https://engineering.buffalo.edu/industrial-systems/academics/graduate/programs/PhD-MS-OR.html

Recent Dec 2021

ISE Newsletter

https://engineering.buffalo.edu/industrial-systems/news-events/newsletter.html

Rajan Batta (Associate Dean for Faculty Affairs) batta@buffalo.edu


University at Buffalo
Department of Industrial and Systems Engineering

https://engineering.buffalo.edu/industrial-systems/academics/undergraduate/requirements-bs.html

https://engineering.buffalo.edu/industrial-systems/academics/graduate/courses.html

Course Descriptions
IE 511 Social Network Behavior Analysis
IE 521 Sustainable Manufacturing
IE 501/502 Individual Problems (for MS/ME students)
IE 504 Facilities Design Facilities Design
IE 505 Production Planning and Control
IE 506 Computer Integrated Manufacturing
IE 507 Design and Analysis of Experiments
IE 508 Quality Assurance
IE 509 Six Sigma Quality
IE 512 Decision Analysis
IE 514 Revenue Management
IE 515 Transportation Analytics
EAS 521 Principles of Engineering Management I
EAS 522 Principles of Engineering Management II
IE 528 Decision Based System Design
IE 531 Human Factors Research Methodology
IE 532 Human Information Processing
IE 535 Human-Computer Interaction
IE 536 Work Physiology
IE 538 Human Factors and Ergonomics Lab
IE 541 Human Factors in Safety
IE 551 Simulation and Stochastic Models
IE 552 Information Fusion Systems and Applications
IE 553 High Level Information Fusion
IE 559/560 Thesis (for MS students)
IE 564 Lean Enterprise and Applications
IE 572 Linear Programming
IE 573 Discrete Optimization
IE 575 Stochastic Methods
IE 576 Applied Stochastic Processes
IE 582 Robotics
IE 585 Research in Healthcare
IE 632 Advanced Topics in Human Factors
IE 633 Cognitive Engineering
IE 635 Cognitive Modeling and its Applications in Intelligent System Design
IE 639 Special Topics: Field Research Methods in Occupational Ergonomics
IE 639 Special Topics: Innovations in Home Health
IE 640 Formal Methods for Reliable Human-Interactive Systems
IE 659/660 Dissertation
IE 670 Topics in Operations Research
IE 671 Nonlinear Programming
IE 675 Game Theory
IE 677 Network Optimization
IE 678 Urban Operations Research
IE 679 Multiple Criteria Decision Making
IE 680 Topics in Production Systems

Productivity Management: - Not in Curriculum



Ann Bisantz
Dean of Undergraduate Education; Professor
PhD, Georgia Institute of Technology - Cognitive Engineering; Human Factors in Health Care; Human Decision-making
323 Bell Hall, Phone: (716) 645-8989
bisantz  @buffalo.edu

Lora Cavuoto
Associate Professor and Director of Undergraduate Studies
PhD, Virginia Tech - Physical Ergonomics, Biomechanics of Obesity, Aging and Human Work Capacity, Occupational Safety and Health
324 Bell Hall
Phone: (716) 645-4696
Email: loracavu  @buffalo.edu

Li Lin
Professor and Director of Graduate Studies
PhD, Arizona State - Health systems, simulation, production systems
308B Bell Hall
Phone: (716) 645-4713
Email: indlin  @buffalo.edu

Victor Paquet
Professor and Chair
ScD University of Massachusetts, Lowell - Industrial Ergonomics, Musculoskeletal Epidemiology, Occupational Safety and Health
342 Bell Hall
Phone: (716) 645-4712
Email: vpaquet  @buffalo.edu

Created in 1946, the Department of Industrial and Systems Engineering is one of the founding departments of the School of Engineering and Applied Sciences at UB. We are committed to research and educational excellence across a broad range of industrial and systems engineering disciplines - including operations research, production systems, manufacturing, quality, simulation, and human factors.

What is Industrial and Systems Engineering?


Industrial engineers determine the most effective ways to use the basic factors of production—people, machines, materials, information, and energy—to make a product or provide a service.

"...They are concerned primarily with increasing productivity through the management of people, methods of business organization, and technology. To maximize efficiency, industrial engineers study product requirements carefully and then design manufacturing and information systems to meet those requirements with the help of mathematical methods and models. They develop management control systems to aid in financial planning and cost analysis, and they design production planning and control systems to coordinate activities and ensure product quality." 

https://engineering.buffalo.edu/industrial-systems/academics/undergraduate/why-ise.html







Wednesday, December 29, 2021

Cost Measurement in Manufacturing Execution System (MES)

 

A production process can be very expensive, that is why managing industrial costs is a key activity for every manufacturing company. The sum of direct costs of materials, employees and production costs is the industrial cost of manufacturing. Sistrade® offers a solution to record all the material costs, including indirect costs to calculate cost estimation closest to real.

Real job order cost

Work in process cost

Estimated/real cost comparison

Order profitability

Automatic update of standard costs

Distribution of indirect costs by manufacturing stage

Distribution of indirect costs by cost centres

https://www.sistrade.com/en/solutions/production-management/mes/

Design and application of dynamic cost control system based on MES
August 2003, Dongbei Daxue Xuebao/Journal of Northeastern University 24(8):719-722
Authors: W. Liu, Y.-G. Chu, T.-Y. Chai
https://www.researchgate.net/publication/289479763_Design_and_application_of_dynamic_cost_control_system_based_on_MES

Monday, December 27, 2021

Industrial Engineering Pictures

 

A picture is worth 1000 likes. 

1000+ Likes Picture.






1000+ Likes Picture.

Operation Process Chart - Recording and Analyzing It

Operation process charts records the core engineering activities in engineering processes. Improvement of engineering processes, engineering operations, and engineering elements is the core activity of industrial engineering. Industrial engineering is focused on cost reduction of products and processes through productivity improvement of engineering resources used in the processes.

Operation process chart must first be drawn for each engineering process and improved first by industrial engineers. Then the flow process chart showing transport, delays and longer storage are to be drawn in the flow process chart and in the flow process chart analysis, the focus can be the flow.

An operation process chart is a graphic representation of the points at which materials are introduced into the process, and of the sequence of material processing and inspections.  Material handling activities, especially between machines, work stations and inspection benches are not included in it.

It can have any  information considered desirable for analysis, such as time required and location.


PRINCIPLES AND PRACTICES FOR CONSTRUCTION OF OPERATION PROCESS CHARTS - ASME Standard, 21 May 1947.



Operation process charts are drawn on plain paper of sufficient size to accommodate the chart.

Identification Information on the Chart

The operation process chart should be identified by a title placed at the top of the chart. In the case of chart is to be folded for filing, the identification information should also be placed in such a position on the folded chart that it is visible for identification of the required chart.

At the top the words "Operation Process Chart" are written first.  The identifying information which is always necessary is as follows:

Process of the part or assembly charted
Specify Present Method or Proposed Method
Drawing number, part number, or other identifying number of the part or the assembly
Date Charted
Charted by


Additional information which will be useful includes:

Location: Plant/Building/Department
Chart Number
Sheet No. of Sheets
Approved by


Major Conventions


The sequence in which the events depicted on the chart must be performed is represented by the arrangement of process chart symbols on vertical flow lines. Material, either purchased and directly used or upon which work is performed during the process, is shown by  horizontal  flow lines. 


One of the parts going to make up the completed product is selected for charting first. Usually a chart of the most pleasing appearance will be obtained by choosing the component on which the greatest number of operations is performed. If the chart is to be used as a basis for laying out a progressive assembly line, the part having the greatest bulk to which the smaller parts are assembled would be chosen.

20 When the component which is to be charted first has been chosen, a horizontal material line is drawn in the upper right hand portion of the chart. A description of the material is recorded directly above this line. The description may be as complete as is deemed necessary. Usually a brief description, such as "20 ga. Steel Sheet" or "'/« in. Hex. Brass Bar" will suffice, since it is the purpose of the chart to give a picture of the process as a whole rather than the detailed specifications of the materials used. In order to identify the part itself, the name and identifying number are recorded in capital letters directly above the material description. (The details of the materials as well as various steps in the process are to be documented separately for detailed investigation.)


21 A vertical flow line is next drawn down from the right hand end of the horizontal material line. Approximately V* in. from the intersection of the horizontal material line and the vertical flow line, the symbol is drawn for the first operation or inspection which is performed. To the right of this symbol, a brief description of the event is recorded, such as "Bore,  chamfer, and cut off" or "Inspect material for defects." 

To the left of the symbol is recorded the time allowed for performing the required work (The time taken to perform the work during the observation also needs to be recorded!.). [Work measurement literature has not discussed this issue appropriately]

Other pertinent information which it is considered will add to the value of the chart, such as department in which the work is performed, male or female operator, cost center, machine number, or labor classification, is recorded to the right of the symbol below the description of the event.

22 This charting procedure is continued until another component joins the first. Then a material line is drawn to show the point at which the second component enters the process. If it is purchased material, a brief identification of the material, such as "Wing Nut No. 18023" or "X and Y Co. No. 80 Filter" is placed directly above the material line. If work has previously been done line is erected from the left hand end of the material line. The material from which the component was made and the operations and inspections performed on it are then charted following the conventions described above. This same procedure is repeated as each new component joins one which is being charted. As each component joins the one shown on a vertical flow line to its right, the charting of the events which occur to the combined com-[>onents is continued along the vertical flow line to the right. The final event which occurs to the completed apparatus will thus appear in the lower right hand portion of the chart.

23 Operations are numbered serially for identification and reference purposes in the order in which they are charted. The first operation is numbered 0-1, the second 0-2, and so on. When another component on
which work has previously been done joins the process, the operations performed upon it are numbered in the same series. They will be identified as 0-1, 0-2, 0-3, and 0-4. If a second component then joins the first, the first operation performed on the second component will be identified as 0-5. If two more operations are performed on the second component before it joins the first, they will be numbered 0-6 and 0-7. The first operation performed after the two components have come together would then be identified as 0-8. (This gives convention when the whole finished (assembled) product is charted).

24 An operation number once used is never repeated on the same chart. If after a chart has been completed, it becomes necessary to add an operation to the process be tween two operations, it is permissible to identify the new operation with the number of the preceding operation followed by the
subscript "a." Thus an operation inserted between 0-4 and 0-5 would be identified as 0-4a.

25 Inspections are numbered in the same manner in a series of their own. They are identified as INS-I, INS-2, and so on. 

OTHER CONVENTIONS



30 It sometimes happens that a part may follow two or more alternate courses during part of the process. For example, a partially processed part may be inspected at a certain point. If it is satisfactory in every respect, it may go directly to the assembly. If not, it may require one or more corrective operations, depending upon the nature of the defects.

31 When it is desired to portray a condition of this kind on an operation process chart, a horizontal line is drawn below the being at the intersection of the vertical flow line and the horizontal line. Vertical flow lines are then dropped from the horizontal line for each alternative which it is desired to show. If no operations or inspections are performed during one alternative, a vertical flow line only is shown. In all cases, operation and inspection symbols are added in the conventional manner. They are numbered
serially beginning with the first unused number in the operation or inspection series. The symbols on the flow line furthest to the left are numbered first, then those on the next flow line to the right, and so on until all have been numbered.

32 When all of the alternative paths have been charted, a horizontal line is drawn connecting the lower ends of all of the alternate flow lines. From the mid-point of this line, a vertical flow line is dropped and the balance of the process is charted in the conventional manner. 

33 In some cases, it will be found that the same component is used at two or more different points in the same process. If it is a purchased part, it may be shown in the conventional manner each time it enters the process. If it is a part upon which work has previously been done, however, it will add to
chart if the component is completely charted every time it enters the process, particularly if its own processing is extensive. To avoid unnecessary charting work, the second time a part is shown entering a process, it is represented by a horizontal material line above which is written the name of the part and a
reference to the operation numbers which show the processing it has undergone as "Hand wheel No. 851A, See 0-6 to 0-12 incl."

34 In general, an operation process chart should be so constructed that vertical flow lines and horizontal material lines do not cross. On charts of complicated processes, this is sometimes difficult to avoid. When it is necessary to cross a vertical flow line and a horizontal material line, a curved line is used at the crossing to show that no junction occurs there.

35 In some cases, the unit shown by the chart changes as the process progresses. The chart might start out showing the operations performed on a long bar. The bar might subsequently cut into short lengths so that the operations performed thereafter would apply to the short pieces rather than the long bar.
Whenever it is desired to show the unit which is being charted, it is the convention to break the vertical flow line by drawing two parallel horizontal lines about l1/* in- long and '/< in. apart centered with respect to the vertical flow line. Between these lines the unit which is to be followed during the subsequent operations and inspections is shown.

SUMMARY
36 When a proposed method is to be presented by an operation process chart, it is often desirable to show the advantages which it offers over the present method. This may be done by including with the information shown on the chart a summary of the important differences between the two methods.

37 This summary may take the form as given at the bottom of this page.

38 The summary should be placed in a prominent location on the chart. On a small 8Vs in. X 11 in. chart, it will usually be in the lower left hand corner. In the case of a folded chart, it will be on the outside when the chart is folded. It may also be desirable to show it on the inside

Disassembly

26 The conventions followed for portraying disassembly operations are quite similar to those used for assemblies. Material is represented as flowing from the process by a horizontal material line drawn to the right from the vertical flow line approximately 'A in. below the symbol for the disassembly operation. The name of the disassembled component is shown directly above the horizontal material line. The subsequent operations which are performed on the disassembled component, if any, are shown on a vertical flow line extending down from the right-hand end of the horizontal material line.

27 If the disassembled component is later reassembled to the part or assembly from which it was disassembled, that part or assembly is shown as feeding into the now line of the component. This practice moves the major vertical flow line always to the right. Thus, when disassembly operations are to be shown, the chart cannot be started in the upper right hand corner of the form, but must be started further to the left.

28 In numbering the operations, it is the practice to number the operations performed on the disassembled component after disassembly before numbering the operations on the part from which it was disassembled. Then if the part later rejoins the disassembled component, the conventional numbering practices may be followed. This practice also applies to inspections.



CONCLUSION

39 It is recognized that the above description of principles and practices for construction of operation process charts may not cover every conceivable situation which it may be desired to show. Probably at least 95 per cent of the situations which are ordinarily encountered in industry are covered, however. The balance may be charted satisfactorily by following the prescribed conventions as closely as possible, representing the unusual situations with the objective of clearness uppermost in mind. A process chart is a means to an end rather than an end in itself. If it performs its function and is reasonably clear to all who study it, it may be considered to be a satisfactory chart.


ASME (1947) Report Hathitrust Website page

https://babel.hathitrust.org/cgi/pt?id=mdp.39015039876274&view=1up&seq=6


Work Measurement for Recording Times in Process Charts is ignored in current work measurement literature.


Current work measurement is focused on measuring time for fixing standard time for the whole task. Even though element breakup of the task is discussed it is still to facilitate standard time determination for the whole task. Even the standard data chapter does not emphasize compulsory development of standard data for all common elements in the firm.

Analysis of the Operation Chart

Operation Analysis described by Maynard and Stegemerten is the detailed procedures to be applied to each processing operation (O) and inspection operation (INS). The work of the machine and the work of the man has to be recorded in detailed. For machine work operation analysis sheet or operation information sheet described by Maynard and Stegemerten can be used. For the analysis of Man Work, the Two handed process chart is the appropriate chart.


Information from DFMA materials has application in Operation Process Chart Analysis.

Engineering in Industrial Engineering -  Machine work study or machine effort improvement, value engineering and design for manufacturing and assembly are major engineering based IE methods. All are available as existing methods.

https://nptel.ac.in/content/storage2/courses/107103012/module1/lec1.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module1/lec2.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module1/lec3.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module1/lec4.pdf


https://nptel.ac.in/content/storage2/courses/107103012/module2/lec1.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module2/lec2.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module2/lec3.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module2/lec4.pdf

Powder Metallury

https://nptel.ac.in/content/storage2/courses/107103012/module2/lec5.pdf


Module 3 - Machining


Machining

Machining - General

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec1.pdf

Turning

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec2.pdf

Round Holes

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec3.pdf

Milling

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec4.pdf

Shaping, Planing and Slotting

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec5.pdf

Broaching

https://nptel.ac.in/content/storage2/courses/107103012/module3/lec6.pdf


Module 4 - Forming
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec1.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec2.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec3.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec4.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec5.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec6.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec7.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module4/lec8.pdf



Module 5 


https://nptel.ac.in/content/storage2/courses/107103012/module5/lec1.pdf


DESIGN FOR POLISHING AND PLATING


https://nptel.ac.in/content/storage2/courses/107103012/module5/lec2.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module5/lec3.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module5/lec4.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module5/lec5.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module5/lec6.pdf

https://nptel.ac.in/content/storage2/courses/107103012/module5/lec7.pdf



Module 6

https://nptel.ac.in/content/storage2/courses/107103012/module6/lec1.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module6/lec2.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module6/lec3.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module6/lec4.pdf


Module 7

https://nptel.ac.in/content/storage2/courses/107103012/module7/lec1.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module7/lec2.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module7/lec3.pdf


Module 8

https://nptel.ac.in/content/storage2/courses/107103012/module8/lec1.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module8/lec2.pdf
https://nptel.ac.in/content/storage2/courses/107103012/module8/lec3.pdf



You can access files from the FaceBook Group
Management and Industrial Engineering - Effectiveness and Efficiency
Public group - 737 members

Time Comparisons and Improvements: Allowed time is indicated in the charts and also the time being actually taken. Hence it is a point of further evaluation. Also based on benchmarking, best outside performance can be ascertained and then the operation can be evaluated for improvement.

New engineering developments

Mechanization and Automation Possibilities


UD. 27.12.2021
Pub 21.12/2021

Sunday, December 26, 2021

Productivity Management- Principle of Industrial Engineering



TAYLOR - NARAYANA RAO PRINCIPLES OF INDUSTRIAL ENGINEERING
https://www.proquest.com/docview/1951119980


18-Productivity Management



________________________

Every industrial engineer is a productivity manager. 

He has to learn complete management theory and its application in IE practice.
He has to plan for productivity and achieve productivity improvement year after year.

As a part of productivity management, he has to assess management actions of the organization for effect on productivity and has to recommend changes if they have an adverse effect on productivity or if there is scope for increasing productivity by modifying them.

Principles of Industrial Engineering - Presentation 


by Dr. K.V.S.S. Narayana Rao in the 2017Annual Conference of IISE (Institute of Industrial and Systems Engineering) at Pittsburgh, USA on 23 May 2017


________________________


________________________




Industrial Engineering of Management Process - Modifying management process to increase productivity

Productivity Management - Articles and Books


Productivity Management in Engineering Organizations - Online Book
https://nraoiekc.blogspot.com/2019/10/productivity-management-in-engineering.html

Elaborate Planning Organization - Need and Utility - F.W. Taylor
http://nraoiekc.blogspot.com/2013/08/elaborate-planning-organization-need.html


Innovation management is important for effective and successful industrial engineering practice.
Principles of Innovation
http://nraomtr.blogspot.com/2014/11/the-ten-principles-of-innovation.html


Principles of Industrial Engineering - Narayana Rao - Detailed List

Clicking on the link will take you to more detailed content on the principle


The full paper on the principles by Prof. K.V.S.S. Narayana Rao is now available for downloading from IISE 2017 Annual Conference Proceedings in Proquest Journal Base.

Updated on 26 Dec 2021,  10 November 2019,  25 May 2019, 28 June 2017

Thursday, December 23, 2021

Data Analytics for Product Design - Product Industrial Engineering

2021

Data science for engineering design: State of the art and future directions
Filippo Chiarello, Paola Belingheri, Gualtiero Fantoni
Computers in Industry Volume 129, August 2021, 103447.
Under a Creative Commons license

Panel Discussion: The Future of AI in Product Design
July 9, 2021
https://www.altair.com/newsroom/articles/panel-discussion-the-future-of-ai-in-product-design/

Insights vs Product vs Engineering Data Science, and how each provides value to your business
Published on April 22, 2021
Gordon S.
Head of Product

Analytics-to-Value: Digital analytics optimizing products and portfolios
April 21, 2021 | Article

https://amplitude.com/product-analytics

https://research.polyu.edu.hk/en/publications/unlocking-the-power-of-big-data-analytics-in-new-product-developm





2018

How predictive analytics can boost product development?

August 2018
In an analysis of more than 1,800 completed software projects,  only 30 percent of them met their original delivery deadline and one in five of these did so by removing or deferring feature content. The average overrun is around 25 percent of the originally planned schedule. The performance of a sample of over 1,600 integrated-circuit-design projects was even more telling. Over 80 percent of those projects were late, and the average overrun was nearly 30 percent. Moreover, those projects were almost as likely to suffer an 80 percent overrun as they were to finish on time.

Estimating resources is a problem.

Today, some companies are adopting a new approach, one that uses powerful data analysis and modeling techniques to bring new clarity to the estimation of project-resource requirements.

Product life-cycle fortune

How can machine learning tools help an everyday engineer turn into a product life-cycle fortune teller?

MINESET is a web-based client server that gives engineers the ability to visualize millions of records interactively. The information parsed by the software can be as diverse as a temperature reading or a GPS location.
https://www.engineering.com/DesignSoftware/DesignSoftwareArticles/ArticleID/15061/Machine-Learning-Analyzes-Design-Spaces-and-Big-Data.aspx

2016
How engineers will use big data in product design?
Big data is going to impact many industries, and product design is no exception.

Better-informed product development. How would the way you design products change if you could learn not only how customers are using them, but where they are having trouble with them and what features they are ignoring altogether?

That information is going to be available now.   Mechanical engineers have the opportunity for product insights through IoT-enabled devices as products can stream usage data back. A bike fork can capture force measurements. A utility cabinet can transmit internal temperature readings. The smart products will provide design engineering with practical information on how products are used in the field. Traditionally, engineers rely on marketers, customer visits to get information on product related issues.  But IoT devices could provide volumes of reliable feedback now which is very relevant for engineering decisions.
https://www.ptc.com/en/cad-software-blog/big-data-brings-big-changes-to-product-design


Ud. 23.12.2021
Pub 17.8.2019

Wednesday, December 22, 2021

Machine Tool Analytics - Analytics for Machine Tools


What is Manufacturing Analytics?

I have written about manufacturing analytics many times. You can read the overview post here “What is Manufacturing Analytics?” Below is a quick summary:

The goal of SensrTrx or any manufacturing analytics product is to increase capacity and throughput, doing more with the same resources. It does this by using machine and operator data to find and eliminate inefficiencies in their manufacturing process.

Manufacturing analytics systems should do 4 things well:

Acquire Data
Clean & Contextualize Data
Calculate Manufacturing KPIs
Produce Role-Based Visualizations & Dashboards
It must be able to do all these things, well, to produce the end goal of Producing More with the Same Resources (labor and equipment).


New collection of articles -  2021


Gartner Top 10 Data and Analytics Trends for 2021


How data analytics is redefining the metal cutting industry
May 8, 2020
https://hyperight.com/how-data-analytics-is-redefining-the-metal-cutting-industry/

Machine Learning Book

https://machinelearningbook.com/teaching-materials/

New collection of articles - 11 October 2020


https://aptitive.com/blog/predictive-analytics-manufacturing/

https://inventrax.com/manufacturing-execution-system.aspx

https://www.machinemetrics.com/blog/mes-vs-iiot-platform

Optimize Your Production Process with Manufacturing Execution Systems and FactoryTalk Analytics
https://www.rockwellautomation.com/en-in/company/events/webinars/optimize-your-production-process-with-manufacturing-execution-sy.html

https://www.lantek.com/ca/blog/advance-mes-analytics-from-machinery-to-the-brain-of-enterprise

Manufacturing with Intelligence: A Framework for System-Level Anomaly Prediction

December 28, 2018 | IoT Tech Expo 2019, Devices & Systems
https://innovate.ieee.org/innovation-spotlight/anomaly-prediction-IoT-sensors-data-mining-manufacturing/


We Turn Machine Tool  Data into Productivity - Says MT Analytics GmbH



MT Analytics offers Industry 4.0 solutions using data Analytics and a hardware Test Lab .

The MT Analytics GmbH is running a component test lab for machine tool and automotive components and provides services to their customers identifying and optimizing dynamical and geometrical problems of discrete manufacturing production systems. Beside MT Analytics GmbH provides Data Analytic Software solutions to optimize given NC Codes and to monitor the quality of parts as well as the condition of all machine tool components. Machine internal data are analyzed in real time by our algorithms that include our expert knowledge and the experience in testing, modeling and optimizing hardware.

Hardware Test Lab

The MT Analytics GmbH is running a Component Test Lab for machine tool and automotive components and provides services to their customers identifying and optimizing dynamical and geometrical problems of discrete manufacturing production systems

Data Analytics

The MT Analytics GmbH provides Data Analytic Software solutions to optimize given NC Codes and to monitor the quality of parts. Machine internal data are analyzed in real time by our algorithms that include our expert knowledge in testing, modeling and optimizing Hardware

http://www.mt-analytics.de/





SINUMERIK ONE – the No. 1 for machine tool users


SINUMERIK ONE convinces through the advantages that the digital twin brings to machine manufacturers and users. The virtual image becomes the reference variable for real action. Quality and speed in the production of the workpiece

Digital First with SINUMERIK ONE
SINUMERIK ONE enables a consistent “digital first” strategy. This means that key manufacturing processes (e.g. programming, job preparation or process optimization) are always simulated first using digital twins, in other words, on detailed virtual images of the controls and machining.

The benefits of the CNC control
Improved performance on the shop floor
SINUMERIK ONE is optimized for performance. An innovative system architecture means the system will impress with its excellent productivity. In the highly demanding area of mold making, in particular, double-digit productivity gains are a real possibility, depending on the machine. Innovative software functions leverage the potential of the latest processor technologies, so very different processing functions can be run in parallel without performance losses.
https://new.siemens.com/global/en/products/automation/systems/sinumerik-one/sinumerik-one-for-machine-users.html

2019

UMATI, THE UNIVERSAL MACHINE TOOL INTERFACE


“Umati” stands for “universal machine tool interface”. It is a standardized, open, flexible and secure interface that connects machine tools to higher-level IT systems in production environments (e.g. ERP, MES, or peripheral infrastructure like cloud storage).

Umati’s core feature is, indeed, standardized semantics, embedded in an information model based on the open communication standard OPC UA. The final aim linked to this aspect is to establish a worldwide standard for the connectivity of machine tools.

While the primary use of umati is to simplify the connection between machine tools and external IT systems, the main benefit consists of making data processing easier. If data are standardized on many machine tool interfaces, the monitoring of these data, for example, is simplified.

The project was initiated in 2017 by VDW, the German machine tools builders’ association. The machine tool manufacturing companies Chiron, DMG Mori, EMAG, Grob, Heller, Liebherr-Verzahntechnik, Trumpf and United Grinding have been collaborating to the initiative since the beginning. Since the AMB fair in 2018, the companies Georg Fischer Machining Solutions and Pfiffner have contributed as application partners. Such an impressive group is supported by the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) of the University of Stuttgart. The control suppliers Beckhoff, Bosch Rexroth, Fanuc, Heidenhain and Siemens have been included in the project from the beginning and provided the experience, knowhow and data necessary for the interface.

After two years of development, umati participated in EMO Hannover 2019, where 70 companies from ten countries have connected 110 machines and 28 value-added services in real-time.
https://www.cecimo.eu/news/cecimo-press-statement-cecimo-joins-umati-the-universal-machine-tool-interface/

SECO Tools selects MachineMetrics for Manufacturing Analytics
MachineMetrics / September 05, 2018
https://www.machinemetrics.com/blog/seco-tools-selects-machinemetrics-for-manufacturing-analytics

2/1/2019
The Starting Point for Machine Tool Monitoring: Data Analysis Is an Emotional Choice
https://www.mmsonline.com/blog/post/the-starting-point-for-machine-tool-monitoring-data-analysis-is-an-emotional-choice


2019 July
Condition Monitoring on CNC machines:

To enable continuous online monitoring,  P4A developed a standalone online monitoring system for a multinational manufacturer of automotive parts, which operates over 100 CNC cutting and milling machines at their plant in Belgium.

The data acquisition system installed consists of 10 vibration/temperature, 2 speed, 2 current and 2 voltage sensors all collecting real-time data directly available on P4A’s web-based asset data analytics platform.
https://performanceforassets.com/2019/01/17/predictive-analytics-for-cnc-machines/



Research Papers - Machine Tool Analytics - Analytics for Machine Tools


Open Access
Published: 25 July 2018

A big data analytics based machining optimisation approach

Wei Ji, Shubin Yin & Lihui Wang
Journal of Intelligent Manufacturing volume 30, pages1483–1495(2019)
https://link.springer.com/article/10.1007/s10845-018-1440-9


A Collection of Research Papers and the Content cited from them.


Akturk, M. S., & Avci, S. (1996). An integrated process planning approach for CNC machine tools. International Journal of Advanced Manufacturing Technology,12(3), 221–229. https://doi.org/10.1007/BF01351201.

Akturk and Avci (1996) presented a hierarchical method for a CNC machine tool. The mathematical models on system characterisation were established to minimise the total production cost.



Arnaiz-González, Á., Fernández-Valdivielso, A., Bustillo, A., & López de Lacalle, L. N. (2016). Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling. The International Journal of Advanced Manufacturing Technology,83(5), 847–859. https://doi.org/10.1007/s00170-015-7543-y.

Arnaiz-González et al. (2016) used artificial neural networks to predict dimensional error on inclined surfaces machined by ball end mill. Their results showed that radial basis functions can predict better than multilayer perceptron in all cases.

Bretthauer, K. M., & Cote, M. J. (1997). Nonlinear programming for multiperiod capacity planning in a manufacturing system. European Journal of Operational Research,96(1), 167–179. https://doi.org/10.1016/S0377-2217(96)00061-6.



Chen, C.-C., Chiang, K.-T., Chou, C.-C., & Liao, Y.-C. (2011). The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. International Journal of Advanced Manufacturing Technology,54(5–8), 465–478. https://doi.org/10.1007/s00170-010-2964-0.

Chen et al. (2011) proposed an experimental plan of a four-factor optimal design to obtain the optimal spindle speed, feed rate, cutting depth, and the status of lubrication concerning vibration and surface roughness in precision turning.


Chen, M. C., & Tseng, H. Y. (1998). Machining parameters selection for stock removal turning in process plans using a float encoding genetic algorithm. Journal of the Chinese Institute of Engineers,21(4), 493–506. https://doi.org/10.1080/02533839.1998.9670412.

Chen and Tseng (1998) introduced a float encoding GA into machining conditions selection.

Chua, M. S., Loh, H. T., & Wong, Y. S. (1991). Optimization of cutting conditions for multi-pass turning operations using sequential quadratic programming. Journal of Materials Processing Technology,28(1–2), 253–262. https://doi.org/10.1016/0924-0136(91)90224-3.

Chua et al. (1991) proposed a series of mathematical formulations to optimise the cutting conditions and to reduce the operation time.


de Lacalle, L. N. L., Lamikiz, A., Sánchez, J. A., & de Bustos, I. F. (2006). Recording of real cutting forces along the milling of complex parts. Mechatronics,16(1), 21–32. https://doi.org/10.1016/j.mechatronics.2005.09.001.

de Lacalle et al. (2006) developed methods that were used to detect potential milling problems associated with cutting force measurement, which demonstrated that the data in machining are abundantly enough to be used and mined. Therefore, big data analytics combined with hybrid algorithms shows potential for an integrated optimisation of machine tools, cutting tools and machining conditions.


Dereli, T., & Filiz, I. H. (2000). Allocating optimal index positions on tool magazines using genetic algorithms. Robotics and Autonomous Systems,33(2–3), 155–167. https://doi.org/10.1016/S0921-8890(00)00086-5.

Dereli and Filiz (2000) utilised a GA to obtain the optimal index positions on tool magazines.

Fernández-Valdivielso, A., López de Lacalle, L. N., Urbikain, G., & Rodriguez, A. (2015). Detecting the key geometrical features and grades of carbide inserts for the turning of nickel-based alloys concerning surface integrity. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science,230(20), 3725–3742. https://doi.org/10.1177/0954406215616145.

Fernández-Valdivielso et al. (2015) presented an experiment based method to seek common feature of cutting tool with best performance in machining of superalloys in terms of surface integrity.

Guo, Y. W., Mileham, A. R., Owen, G. W., Maropoulos, P. G., & Li, W. D. (2009). Operation sequencing optimization for five-axis prismatic parts using a p swarm optimization approach. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture,223(5), 485–497. https://doi.org/10.1243/09544054JEM1224.

Guo et al. (2009) used a particle swarm optimisation (PSO) approach to obtaining operation sequence.

Hinton, G. E. (2009). Deep belief networks. Scholarpedia,4(5), 5947.

A set of hypothetic data generated in-house, according to the real machining setups, are applied to training the DBN (Hinton 2009) model used to calculate the fitness of GA in  (Weiji et al 2019).

Hua, G. R., Zhou, X. H., & Ruan, X. Y. (2007). GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts. International Journal of Advanced Manufacturing Technology,33(5–6), 594–603. https://doi.org/10.1007/s00170-006-0477-7.

Hua et al. (2007) proposed a GA-based synthesis approach to archive machining scheme selection and operation sequencing optimisation for prismatic parts.

Jayabal, S., & Natarajan, U. (2010). Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using Nelder–Mead and genetic algorithm methods. International Journal of Advanced Manufacturing Technology,51(1–4), 371–381. https://doi.org/10.1007/s00170-010-2605-7.

Jayabal and Natarajan (2010) proposed a method of optimisation of thrust force, torque, and tool wear in drilling of coir fibre-reinforced composites, combining Nelder–Mead and GA methods. In their method, a nonlinear regression analysis was applied to establishing functions according to experimental data.

Ji, W., Shi, J., Liu, X., Wang, L., & Liang, S. Y. (2017). A novel approach of tool wear evaluation. Journal of Manufacturing Science and Engineering,139(September), 1–8. https://doi.org/10.1115/1.4037231.


Ji, W., & Wang, L. (2017a). Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems,43, 187–194. https://doi.org/10.1016/j.jmsy.2017.03.008.

Ji, W., & Wang, L. (2017b). Big data analytics based optimisation for enriched process planning: A methodology. Procedia CIRP,63, 161–166. https://doi.org/10.1016/j.procir.2017.03.090.

Big data analytics in machining was considered for scheduling  (Ji and Wang 2017a) and machining optimisation by a proposed enriched process planning method in the conceptual level (Ji and Wang 2017b).


Kondayya, D., & Krishna, A. G. (2012). An integrated evolutionary approach for modelling and optimisation of CNC end milling process. International Journal of Computer Integrated Manufacturing,25(11), 1069–1084. https://doi.org/10.1080/0951192X.2012.684718.

Kondayya and Krishna (2012) used a non-dominated sorting genetic algorithm-II (NSGA-II) to optimise the cutting parameters during a CNC end-milling process.

Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature,544(7648), 23–25. https://doi.org/10.1038/544023a.

Lenza Juergen , Thorsten Wuest, Engelbert Westkämper (2018),  Holistic approach to machine tool data analytics,Journal of Manufacturing Systems, Volume 48, Part C, July 2018, Pages 180-191
https://www.sciencedirect.com/science/article/abs/pii/S0278612518300360


The data of machine tools is coming from the same source – the machine tool controller and connected sensors to all departments.. We propose combining the tasks and bundling up analytics objectives across different departments and/or functions at the production line, factory or even the supply chain level.


Li, L., Deng, X., Zhao, J., Zhao, F., & Sutherland, J. W. (2018). Multi-objective optimization of tool path considering efficiency, energy-saving and carbon-emission for free-form surface milling. Journal of Cleaner Production,172, 3311–3322. https://doi.org/10.1016/j.jclepro.2017.07.219.

Li et al. (2018) proposed a multi-objective optimisation approach for tool path planning in freeform surface milling.

Li, L., Liu, F., Chen, B., & Li, C. B. (2015). Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. Journal of Intelligent Manufacturing,26(5), 891–898. https://doi.org/10.1007/s10845-013-0809-z.

Li et al.(2015) proposed a back propagation neural network model to predict the cutting parameters based on a set of mathematical objectives, e.g. machining time, energy consumption and surface roughness. Process planning was commonly treated as an NP-hard problem

Li, W. D., Ong, S. K., Lu, Y. Q., Nee, A. Y. C., Palade, V., Howlett, R. J., et al. (2003). A Tabu search-based optimization approach for process planning. Knowledge-Based Intellignet Information and Engineering Systems, Pt 2, Proceedings,2774, 1000–1007.

Tabu Search was applied to process planning, machining resource selection, setup plan determination and operation sequencing (Li et al. 2003).


Li, Z., Wang, Y., & Wang, K. (2017). A data-driven method based on deep belief networks for backlash error prediction in machining centers. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1380-9.

Li et al. (2017) presented a data-driven approach combined with Deep Belief Network (DBN) to predicting the backlash error in machining centre.



Lian, K. L., Zhang, C. Y., Shao, X. Y., & Gao, L. (2012). Optimization of process planning with various flexibilities using an imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology,59(5–8), 815–828. https://doi.org/10.1007/s00170-011-3527-8.

Lian et al. (2012) applied an imperialist competitive algorithm (ICA) to find promising solutions with a reasonable computational cost. Their cases illustrated that the ICA was more efficient and robust than GA, SA, TS and PSO.


Liang, Y. C., Lu, X., Li, W. D., & Wang, S. (2018). Cyber physical system and big data enabled energy efficient machining optimisation. Journal of Cleaner Production,187, 46–62. https://doi.org/10.1016/j.jclepro.2018.03.149.

Liang et al. (2018) proposed a novel Cyber Physical System (CPS) and big data enabled machining optimisation system to optimise the energy in machining processes.


Manupati, V. K., Chang, P. C., & Tiwari, M. K. (2016). Intelligent search techniques for network-based manufacturing systems: multi-objective formulation and solutions. International Journal of Computer Integrated Manufacturing,29(8), 850–869. https://doi.org/10.1080/0951192X.2015.1099073.

Manupati et al. (2016) proposed a modified block-based GA and modified NSGA to obtain the minimisation of both makespan and the variation of workload. A series of swarm intelligence (SI) based optimisation algorithms were applied to process planning.

Morad, N., & Zalzala, A. (1999). Genetic algorithms in integrated process planning and scheduling. Journal of Intelligent Manufacturing,10(2), 169–179. https://doi.org/10.1023/A:1008976720878.

Morad and Zalzala (1999) applied a GA to minimise the makespan, the total rejects and the total cost of production.

Petrovic, M., Mitic, M., Vukovic, N., & Miljkovic, Z. (2016). Chaotic p swarm optimization algorithm for flexible process planning. International Journal of Advanced Manufacturing Technology,85(9–12), 2535–2555. https://doi.org/10.1007/s00170-015-7991-4.

Petrovic et al. (2016) utilised PSO algorithm and chaos theory to optimise process plans, in which PSO was used in early stages of the optimisation process by implementing ten different chaotic maps that enlarged the search space and provided diversity.

Pour, M. (2018). Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-018-2070-2.

Pour (2018) proposed a hybrid algorithm based on time series analysis and wavelet transform to model surface roughness. Moreover, many efforts were also devoted to GA-based hybrid methods for optimisation of machining process.

Rowe, W. B., Li, Y., Mills, B., & Allanson, D. R. (1996). Application of intelligent CNC in grinding. Computers in Industry,31(1), 45–60. https://doi.org/10.1016/0166-3615(96)00036-X.

Rowe et al. (1996) reported an application of artificial intelligence in CNC grinding, including knowledge based and expert systems, fuzzy logic systems, and neural network systems. Within the context, setup time, process proving time and the extent of operator intervention could be improved.

Salehi, M., & Bahreininejad, A. (2011). Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. Journal of Intelligent Manufacturing,22(4), 643–652. https://doi.org/10.1007/s10845-010-0382-7.

A hybrid GA and intelligent search method was proposed by Salehi and Bahreininejad (2011), and it was applied to optimising machine tool, cutting tool and tool access direction for each operation.

Sardinas, R. Q., Santana, M. R., & Brindis, E. A. (2006). Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence,19(2), 127–133. https://doi.org/10.1016/j.engappai.2005.06.007.

Sardinas et al. (2006) proposed a GA-based multi-objective optimisation method to obtain the optimal cutting parameters during the turning process.

Shin, K. S., Park, J. O., & Kim, Y. K. (2011). Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Computers & Operations Research,38(3), 702–712. https://doi.org/10.1016/j.cor.2010.08.007.

Shin et al. (2011) introduced a multi-objective symbiotic evolutionary algorithm into flexible manufacturing system for solving process planning problems, where machine tool, sequence, and process are the three objectives.

Sluga, A., Jermol, M., Zupanic, D., & Mladenic, D. (1998). Machine learning approach to machinability analysis. Computers in Industry,37(3), 185–196. https://doi.org/10.1016/S0166-3615(98)00098-0.

Sluga et al. (1998) developed a decision tree based method to predict tool features, cutting geometry and cutting parameters a set of attribute values to improve and automate the tool selection and determination of cutting parameters.

Taiber, J. G. (1996). Optimization of process sequences considering prismatic workpieces. Advances in Engineering Software,25(1), 41–50. https://doi.org/10.1016/0965-9978(95)00084-4.

Taiber (1996) proposed a set of modified algorithms from the field of combinatorial search problems, gradient projection method named as von Rosen, branch and bound algorithm, and shortest common super sequence algorithm, etc. The method was to assist the human planner in fulfilling machine tool and cutter selection, determination of the setup and process sequence, definition of tool paths and optimisation of cutting parameters.

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2018.01.006.

Tao et al. (2018) shed light in a data-driven smart manufacturing framework. Recently, there have been many articles reporting big data analytics in machining.

Thimm, G., Britton, G. A., Whybrew, K., & Fok, S. C. (2001). Optimal process plans for manufacturing and tolerance charting. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture,215(8), 1099–1105. https://doi.org/10.1243/0954405011519024.

Thimm et al. (2001) proposed a datum hierarchy tree within graph theoretical approach to minimising machine and datum changes.

Tiwari, M. K., Dashora, Y., Kumar, S., & Shankar, R. (2006). Ant colony optimization to select the best process plan in an automated manufacturing environment. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture,220(9), 1457–1472. https://doi.org/10.1243/09544054JEM449.

Tiwari et al. (2006) presented an ant colony optimisation method to select the best process plan in an automated manufacturing environment.

Venkatesan, D., Kannan, K., & Saravanan, R. (2009). A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing and Applications,18(2), 135–140. https://doi.org/10.1007/s00521-007-0166-y.

Venkatesan et al. (2009) developed a GA-based optimisation of weights applied to ANN for obtaining the best machining operation regarding marginal amount of time saving.

Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., et al. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics,13(4), 2039–2047. https://doi.org/10.1109/TII.2017.2670505

Wang, L. (2009). Web-based decision making for collaborative manufacturing. International Journal of Computer Integrated Manufacturing,22(4), 334–344. https://doi.org/10.1080/09511920802014912.

Wang, L. (2013). Machine availability monitoring and machining process planning towards Cloud manufacturing. CIRP Journal of Manufacturing Science and Technology,6(4), 263–273. https://doi.org/10.1016/j.cirpj.2013.07.001.

The distribution profile is a key feature. Combining with web-based knowledge sharing, dynamic scheduling, real-time monitoring and remote control, DPP can be embedded into web-based environment, which is named Web-DPP (Weiji et al 2019) (Wang 2009, 2013).

Wang, L. (2014). Cyber manufacturing: Research and applications. In Proceedings of the TMCE (pp. 39–49). Budapest.

Towards the concept of cloud manufacturing, "a Cloud-DPP (Weiji et al 2019) was also developed as one of the applications of cyber-physical systems for more complex manufacturing environment (Wang 2014).

Wang, L., Feng, H.-Y., & Cai, N. (2003). Architecture design for distributed process planning. Journal of Manufacturing Systems,22(2), 99–115.

Distributed Process Planning (DPP) is used to divide the machining process planning into supervisory planning, execution control and operation planning (Wang et al. 2003).

In this design, the execution control module is placed in-between the supervisory planning and operation planning modules, and looks after jobs dispatching (in the unit of setups) based on up-to-date monitoring data, availability of machines and scheduling decisions (Wang and Shen 2003; Wang et al. 2003).

Wang, L., & Shen, W. (2003). DPP: An agent-based approach for distributed process planing. Journal of Intelligent Manufacturing,14, 429–439.

In this design, the execution control module is placed in-between the supervisory planning and operation planning modules, and looks after jobs dispatching (in the unit of setups) based on up-to-date monitoring data, availability of machines and scheduling decisions (Wang and Shen 2003; Wang et al. 2003).

Wen, X. Y., Li, X. Y., Gao, L., & Sang, H. Y. (2014). Honey bees mating optimization algorithm for process planning problem. Journal of Intelligent Manufacturing,25(3), 459–472. https://doi.org/10.1007/s10845-012-0696-8.

Wen et al. (2014) proposed a honey bees mating based optimisation algorithm to optimise the process planning problems. In addition, multi-objective optimisation was employed due to many limitations of single-objective optimisation methods in the real machining process.

Wong, T. N., Chan, L. C. F., & Lau, H. C. W. (2003). Machining process sequencing with fuzzy expert system and genetic algorithms. Engineering with Computers,19(2–3), 191–202. https://doi.org/10.1007/s00366-003-0260-4.

Wong et al. (2003) proposed a fuzzy expert system and GA to sequence machining process.

Xu, L. D., & Duan, L. (2018). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems,7575, 1–22. https://doi.org/10.1080/17517575.2018.1442934.

Xu and Duan (2018) pointed out that CPS and big data are two keys for Industry 4.0 in the near future.

Xu, X., Wang, L., & Newman, S. T. (2011). Computer-aided process planning—A critical review of recent developments and future trends. International Journal of Computer Integrated Manufacturing,24(1), 1–31. https://doi.org/10.1080/0951192x.2010.518632.

For cutting tool selection and machining conditions determination, two common approaches exist: (1) in most of reported process planning methods, cutting tool is regarded as a standard machining resource and its parametrical optimisation is not considered, and (2) machining conditions are optimised after tool selection (Xu et al. 2011). In this case, the three simultaneous decision processes in process planning are treated sequentially, hindering the loss of both machining accuracy and efficiency.

Yeo, S. H. (1995). A multipass optimization strategy for CNC lathe operations. International Journal of Production Economics,40(2–3), 209–218. https://doi.org/10.1016/0925-5273(95)00052-1.

Yeo (1995) developed a multi-pass optimisation method for a CNC lathe, in which near-optimal solutions were obtained.

Jihong Chen, , Kai Zhang, , Yuan Zhou,* , Yufei Liu, Lingfeng Li, Zheng Chen and Li Yin
Exploring the Development of Research, Technology and Business of Machine Tool Domain in
New-Generation Information Technology Environment Based on Machine Learning
Sustainability 2019, 11, 3316; doi:10.3390/su11123316
https://www.mdpi.com/2071-1050/11/12/3316/pdf


The practical exploitation of tacit machine tool intelligence
Jacob L. Hill, Paul W. Prickett, Roger I. Grosvenor & Gareth Hankins
The International Journal of Advanced Manufacturing Technology volume 104, pages1693–1707(2019)
https://link.springer.com/article/10.1007/s00170-019-03963-0

More References
1. Ji, Z.; Li, P.; Zhou, Y.; Wang, B.; Zang, J.; Liu, M. Toward New-Generation Intelligent Manufacturing. Engineering 2018, 4, 11–20.
2. Kang, H.S.; Lee, J.Y.; Choi, S.; Kim, H.; Park, J.H.; Son, J.Y.; Kim, B.H.; Noh, S.D. Smart Manufacturing: Past Research, Present Findings, and Future Directions. Int. J. Precis. Eng. Manuf. Green Technol. 2016, 3, 111–128.
3. Tao, F.; Qi, Q.L.; Liu, A.; Kusiak, A. Data-driven smart manufacturing. J. Manuf. Syst. 2018, 48, 157–169.
4. Liu, C.; Vengayil, H.; Zhong, R.Y.; Xu, X. A systematic development method for cyber-physical machine tools. J. Manuf. Syst. 2018, 48, 13–24.
5. Liu, C.; Xu, X. Cyber-Physical Machine Tool—The Era of Machine Tool 4.0. Proc. Cirp. 2017, 63, 70–75.
6. Zaeh, M.; Graetz, F.; Rashidy, H. An Approach to Simultaneous Development in Machine Tool Industry. In Proceedings of the 2003 Conference on the Modelling & Applied Simulation, Bergeggi, Italy, 2–4 October 2003; pp. 128–133.
7. Xu, X. Machine Tool 4.0 for the new era of manufacturing. Int. J. Adv. Manuf. Technol. 2017, 92, 1893–1900.

9. Zhou, L.R.; Li, J.F.; Li, F.Y.; Meng, Q.; Li, J.; Xu, X.S. Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. J. Clean. Prod. 2016, 112, 3721–3734.
10. Lenz, J.; Wuest, T.; Westkamper, E. Holistic approach to machine tool data analytics. J. Manuf. Syst. 2018, 48, 180–191.
11. Yang, H.L.; Chang, T.W.; Choi, Y. Exploring the Research Trend of Smart Factory with Topic Modeling. Sustainability 2018, 10, 2779.

16. Marzi, G.; Dabic, M.; Daim, T.; Garces, E. Product and process innovation in manufacturing firms: A 30-year bibliometric analysis. Scientometrics 2017, 113, 673–704.


21. Ernst, H. The use of patent data for technological forecasting: The diffusion of CNC-technology in the machine tool industry. Small Bus. Econ. 1997, 9, 361–381.
22. Yeo, W.; Kim, S.; Park, H.; Kang, J. A bibliometric method for measuring the degree of technological innovation. Technol. Forecast. Soc. Chang. 2015, 95, 152–162.
23. Jun, S. A Forecasting Model for Technological Trend Using Unsupervised Learning. In Database Theory and Application, Bio-Science and Bio-Technology; Springer: Berlin/Heidelberg, Germany, 2011; Volume 258, pp. 51–60.
24. Kulkarni, S.S.; Apte, U.M.; Evangelopoulos, N.E. The Use of Latent Semantic Analysis in Operations Management Research. Decis. Sci. 2014, 45, 971–994.

34. Tang, D. Algorithms for collision detection and avoidance for five-axis NC machining: A state of the art review. Comput. Aided Des. 2014, 51, 1–17.
35. Lauro, C.H.; Brandao, L.C.; Baldo, D.; Reis, R.A.; Davim, J.P. Monitoring and processing signal applied in machining processes—A review. Measurement 2014, 58, 73–86.
36. Cao, H.R.; Zhang, X.W.; Chen, X.F. The concept and progress of intelligent spindles: A review. Int. J. Mach. Tools Manuf. 2017, 112, 21–52.
37. Li, Y.; Zhao, W.H.; Lan, S.H.; Ni, J.; Wu, W.W.; Lu, B.H. A review on spindle thermal error compensation in machine tools. Int. J. Mach. Tools Manuf. 2015, 95, 20–38.
38. He, X.C. Recent development in reliability analysis of NC machine tools. Int. J. Adv. Manuf. Technol. 2016, 85, 115–131.
39. Gadalla, M.; Xue, D.Y. Recent advances in research on reconfigurable machine tools: A literature review. Int. J. Prod. Res. 2017, 55, 1440–1454.
40. Liu, C.; Xu, X.; Peng, Q.; Zhou, Z. MTConnect-based Cyber-Physical Machine Tool: A case study. Procedia CIRP 2018, 72, 492–497.

42. Kong, D.; Yuan, Z.; Liu, Y.; Lan, X. Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country. Technol. Forecast. Soc. Chang. 2017, 119, 80–97.
43. Xu, G.N.; Wu, Y.C.; Minshall, T.; Zhou, Y. Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China. Technol. Forecast. Soc. 2018, 136, 208–221.


Digital Twin for Machine Tools - How to Create?

Analytics requires creation of a digital twin full utlization of their capability. So apart from installing sensors that provide data, digital twin also needs to be created.

Some references for the creation of digital twin.

2017-07-17
How to create the perfect digital twin
https://www.sandvik.coromant.com/en-gb/news/pages/how-to-create-the-perfect-digital-twin.aspx


Characterising the Digital Twin: A systematic literature review
DavidJonesChrisSniderAydinNassehiJasonYonBenHicks
https://doi.org/10.1016/j.cirpj.2020.02.002
CIRP Journal of Manufacturing Science and Technology
Available online 9 March 2020
https://www.sciencedirect.com/science/article/pii/S1755581720300110

Digital Twin : A Comprehensive Overview
Delve into the Basic Concepts of Digital Twins
https://www.udemy.com/course/digital-twin-a-comprehensive-overview/


Updated 20 July 2021, 11 Oct 2020
Pub 3 May 2020









Sunday, December 19, 2021

DFMA for Turning

 2023 BEST E-Book on #IndustrialEngineering. 

INTRODUCTION TO MODERN INDUSTRIAL ENGINEERING.PRODUCT INDUSTRIAL ENGINEERING - FACILITIES INDUSTRIAL ENGINEERING - PROCESS INDUSTRIAL ENGINEERING.  Free Download.

https://academia.edu/103626052/INTRODUCTION_TO_MODERN_INDUSTRIAL_ENGINEERING_Version_3_0 



Design recommendations


 Standard tool geometry should be incorporated at diameter transitions, grooves and chamfer areas.

It is preferred to keep the parts as short as possible to minimize the work deflection from the cutting tool.

 Irregular and interrupted cutting actions are to be avoided.

 When casting or forgings are designed with large shoulders or other areas to be faced, the surface should be 2 to 30 from the plane normal to the axis of the part. It provides edge relief to the cutting tool.

Sharp corners are to be avoided. The radius should be large and conform to standard tool nose radius specification. If possible leave the radius dimension to the discretion of the manufacturer. Provision of sharp corner and burrs are hazardous to the function of the part. These can be minimized by putting chamfers or curved surfaces at the intersection of the other surfaces. 

Clamping and locating region should be free from parting line, draft angles and forging flash. 

In case the part is to be tracer-turned, the turned contour should be designed for easy tracing with a minimum number of changes of stylus and cutting tool. Creating grooves with parallel or steep sidewalls are not possible in one operation. Undercuts should also to be avoided.


Dimensional control in turning

Achieving close dimensional limits in turning operation are inversely related to the size and length of work piece. If the dimensions are high the variations are more. 

Various factors which affect the proper working of the machine are machine vibration, deflection, thermal distortion and wear of the functional part. Other factors on this line part deflection, tool wear, measuring-tool accuracy and operator skills are other factors. 

Surface finish  is dependent upon the above factors. Further, surface finish is also directly related to the feed rate. 



DFMA for Turning - Boothroyd - Dewhurst DFMA Software Case Study

https://www.dfma.com/support/Downloads/MachiningFull.pdf








Saturday, December 18, 2021

Time Study - Part 1- F.W. Taylor in Shop Management

When work is to be repeated many times, the time study should be minute and exact. Each job should be carefully subdivided into its elementary operations, and each of these unit times should receive the most thorough time study. In fixing the times for the tasks, and the piece work rates on jobs of this class, the job should be subdivided into a number of divisions, and a separate time and price assigned to each division rather than to assign a single time and price for the whole job. This should be done for several reasons, the most important of which is that the average workman, in order to maintain a rapid pace, should be given the opportunity of measuring his performance against the task set him at frequent intervals. Many men are incapable of looking very far ahead, but if they see a definite opportunity of earning so many cents by working hard for so many minutes, they will avail themselves of it.


As an illustration, the steel tires used on car wheels and locomotives were originally turned in the Midvale Steel Works on piece work, a single piece-work rate being paid for all of the work which could be done on a tire at a single setting. A fixed price was paid for this work, whether there was much or little metal to be removed, and on the average this price was fair to the men. The apparent advantage of fixing a fair average rate was, that it made rate-fixing exceedingly simple, and saved clerk work in the time, cost and record keeping.


A careful time study, however, convinced the writer that for the reasons given above most of the men failed to do their best. In place of the single rate and time for all of the work done at a setting, the writer subdivided tire-turning into a number of short operations, and fixed a proper time and price, varying for each small job, according to the amount of metal to be removed, and the hardness and diameter of the tire. The effect of this subdivision was to increase the output, with the same men, methods, and machines, at least thirty-three per cent.


As an illustration of the minuteness of this subdivision, an instruction card similar to the one used is reproduced in Figure 1 on the next page. (This card was about 7 inches long by 4 inches wide.)

[Transcriber's note -- Figure 1 not shown]

The cost of the additional clerk work involved in this change was so insignificant that it practically did not affect the problem. This principle of short tasks in tire turning was introduced by the writer in the Midvale Steel Works in 1883 and is still in full use there, having survived the test of over twenty years' trial with a change of management.

In another establishment a differential rate was applied to tire turning, with operations subdivided in this way, by adding fifteen per cent to the pay of each tire turner whenever his daily or weekly piece work earnings passed a given figure.


Shop Management

Next Topic

Bicylcle Ball Inspection Case Study - F.W. Taylor - As Described in Shop Management


Time Study - Part 2 - Shop Management 1903 Explanation by F.W. Taylor


Best Practices in Shop Management - 1903 - F.W. Taylor

Unfortunately there is no school of management. There is no single establishment where a relatively large part of the details of management can be seen, which represent the best of their kinds. The finest
developments are for the most part isolated, and in many cases almost buried with the mass of rubbish which surrounds them.

Among the many improvements for which the originators will probably never receive the credit which they deserve the following may be mentioned.

The remarkable system for analyzing all of the work upon new machines as the drawings arrived from the drafting-room and of directing the movement and grouping of the various parts as they progressed through the shop, which was developed and used for several years by Mr. Wm. II. Thorne, of Wm. Sellers & Co., of Philadelphia, while the company was under the general management of Mr. J. Sellers Bancroft. Unfortunately the full benefit of this method was never realized owing to the lack of the other functional elements which should have accompanied it.

And then the employment bureau which forms such an important element of the Western Electric Company in Chicago; the complete and effective system for managing the messenger boys introduced by Mr. Almon Emrie while superintendent of the Ingersoll Sargent Drill Company, of Easton, Pa.; the mnemonic system of order numbers invented by Mr. Oberlin Smith and amplified by Mr. Henry R. Towne, of The Yale & Towne Company, of Stamford, Conn.; and the system of inspection introduced by Mr. Chas. D. Rogers in the works of the American Screw Company, at Providence, R. I. and the many good points in the apprentice system developed by Mr. Vauclain, of the Baldwin Locomotive Works, of Philadelphia.

The card system of shop returns invented and introduced as a complete system by Captain Henry Metcalfe, U. S. A., in the government shops of the Frankford Arsenal represents another such distinct advance in the art of management. The writer appreciates the difficulty of this undertaking as he was at the same time engaged in the slow evolution of a similar system in the Midvale Steel Works, which, however, was the result of a gradual development instead of a complete, well thought out invention as was that of Captain Metcalfe.

The writer is indebted to most of these gentlemen and to many others, but most of all to the Midvale Steel Company, for elements of the system which he has described. The rapid and successful application of the general principles involved in any system will depend largely upon the adoption of those details which have been found in actual service to be most useful. There are many such elements which the writer feels should be described in minute detail. It would, however, be improper to burden this record with matters of such comparatively small importance.

F.W. Taylor, Shop Management