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 & Systemshttps://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 WangJournal 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
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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
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