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Advances in Additive Manufacturing of Polymer-Fused Deposition Modeling on Textiles: From 3D Printing to Innovative 4D Printing—A Review
by Edgar Adrian Franco UrquizaORCID
Advanced Manufacturing Department, Center for Engineering and Industrial Development, CIDESI-Airport, Carretera Estatal 200, km 23, Queretaro 76270, Mexico
DFMA MEETS DESIGN FOR ADDITIVE MANUFACTURING (DFAM)
http://www.qualifiedrapidproducts.com/?p=2006
Material Collected Before 24.8.2020
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Design for 3D Printing or Additive Manufacturing forms part of Product Industrial Engineering. Every engineering activity can have basic engineering content and industrial engineering content. Industrial engineers have to define their focus in an engineering activity. They have to first understand the basic engineering adequately and then contribute to their industrial engineering focus portion related to that engineering activity.
A framework for mapping design for additive manufacturing knowledge for industrial and product design
Patrick Pradel, Zicheng Zhu, Richard Bibb & James Moultrie
Journal of Engineering Design
Volume 29, 2018 - Issue 6, pages 291-326, Published online: 12 Jun 2018 https://www.tandfonline.com/doi/full/10.1080/09544828.2018.1483011
Framework Presented in the paper
Six main stages are indicated. Within each stage issues are highlighted. The framework is presented in a reverse starting with manufacturing and postprocessing.
Manufacture and Postprocessing
Support Removal
Finishing Processes
Reducing Unwanted Processing Defects
Process Planning and Optimization - Part Programming
Build Orientation
Support Optimization
Tool Path Optimization
Infill and Wall Thickness
Production Tolerances
Process Planning Optimization
Detail Design
Feature Size
Feature Shape
Eliminate Features Needing Support
Adding Excess Material to Enable Postprocessing Operations
Embodiment Design
Design for Component/Functional Integration
Hague, Campbell and Dickens (2003)
Zhou et al. (2014)
Schemelzle et al. (2016)
Pradel, Zhu et al (2018)
Design Ignoring Conventional Manufacturing Roles
Design of Functional Surfaces
Conceptual Design
Design and Feature Database
Biomimicry
New Design Opportunities Enabled by AM
Axiomatic Design Theory
Material and Process Selection
Approaches for Selecting Between AM and Conventional Materials and Processes
Approaches for Selecting Between AM Materials and Processes
IMMENSA TECHNOLOGY LABS - ADDITIVE MANUFACTURING PROCESS CONSULTANCY
Design for 3D Printing - Additive Manufacturing
Business Value Assessment - Business Case Development - Product Development
Solution Rollout - Supplier Identification and development
Lifecycle Management - Supply chain management https://www.immensalabs.com/3d-printing-is-the-foundation-of-industry-4-0/
Honeywell Group
85% Cost Reduction Due to Additive Manufacturing - $50,000 to $7,000.
10 sets of inlet booster rake for measuring air flow turbine engine test cells were made for $50,000 using a combination of welding, brazing, EDM, and other conventional medicines. The additive machining technology center made it for $7,000.
Huge Savings at Company Level - Honeywell Federal Manufacturing & Technologies
Honeywell Federal Manufacturing & Technologies has achieved huge cost reduction. As of FY 2018, they have printed more than 60,000 tooling fixtures for product testing and calculated $125 million in cost avoidance.
Design for Additive Manufacturing - Additive Manufacturing Industrial Engineering are Necessary for Effectiveness and Productivity
Designing for additive manufacturing
Tackling familiar challenges takes a new approach
Additive manufacturing frees engineers from conventional design constraints and solves problems around part consolidation, lightweighting, and performance enhancement. However, unlocking these new possibilities requires a different approach to product design.
A framework for mapping design for additive manufacturing knowledge for industrial and product design
Patrick Pradel, Zicheng Zhu, Richard Bibb & James Moultrie
Journal of Engineering Design
Volume 29, 2018 - Issue 6, pages 291-326, Published online: 12 Jun 2018 https://www.tandfonline.com/doi/full/10.1080/09544828.2018.1483011
How to get the most out of additive manufacturing
August 16, 2018
When evaluating Direct Digital Manufacturing for production, take a methodical analysis of cost, design, assembly, materials, and process to fully understand its benefits. Here are suggested tips. https://www.designworldonline.com/how-to-get-the-most-out-of-additive-manufacturing/
Advanced Design Applied to an Original Multi-Purpose Ventilator Achievable by Additive Manufacturing
Leonardo Frizziero * OrcID, Giampiero DonniciOrcID, Karim DhaiminiOrcID, Alfredo LiveraniOrcID and Gianni CaligianaOrcID
Department of Industrial Engineering, Alma Mater Studiorum University of Bologna, Viale Risorgimento, 2, I-40136 Bologna, Italy
Appl. Sci. 2018, 8(12), 2635 https://www.mdpi.com/2076-3417/8/12/2635
PDF available
Design for additive manufacturing. Guidelines and case studies for metal applications
Presentation held at The Cutting Edge, CMTS 2017, Canadian Manufacturing Technology Show, Toronto, 25 - 28 September 2017 http://publica.fraunhofer.de/documents/N-480000.html
pdf available
Design for manufacturing to design for Additive Manufacturing: Analysis of implications for design optimality and product sustainability
A.W.Gebisa, H.G.Lemu
Procedia Manufacturing
Volume 13, 2017, Pages 724-731
open access https://www.sciencedirect.com/science/article/pii/S2351978917307552
pdf available
Joran W. Booth; Jeffrey Alperovich; Tahira N. Reid; Karthik Ramani
The Design for Additive Manufacturing Worksheet (Conference)
Proc. ASME. 50190; Volume 7: 28th International Conference on Design Theory and Methodology, August 21, 2016 https://engineering.purdue.edu/cdesign/wp/the-design-for-additive-manufacturing-worksheet-conference/
The paper has to brief literature review and table on evolution of DFAM
DESIGN FOR ADDITIVE MANUFACTURING
The rapid growth of the additive manufacturing industry over the past few years has prompted the need to develop methods and processes that enable optimized AM-specific designs. Download this paper to learn about new strategies and best practices for designing AM parts. https://ewi.org/new-paper-design-for-additive-manufacturing/
pdf available
Design for Additive Manufacturing - Frontiers of Engineering
Apr 28, 2013 - Design for Additive Manufacturing. Opportunities Barriers and Democratization. Opportunities, Barriers, and Democratization. https://www.naefrontiers.org/File.aspx?id=39135
pdf available
Seepersaad presentation
Design for 3D Printing - Additive Manufacturing Google Books
Fabricated: The New World of 3D Printing
Front Cover
Hod Lipson, Melba Kurman
John Wiley & Sons, 22-Jan-2013 - Computers - 320 pages
Based on hundreds of hours of research and dozens of interviews with experts from a broad range of industries, Fabricated offers readers an informative, engaging and fast-paced introduction to 3D printing now and in the future. https://books.google.co.in/books?id=MpLXWHp-srIC
Design for 3D Printing: Scanning, Creating, Editing, Remixing, and Making in Three Dimensions
Samuel N. Bernier, Bertier Luyt, Tatiana Reinhard
Maker Media, Inc., 01-Oct-2015 - Computers - 160 pages https://books.google.co.in/books?id=29GqCgAAQBAJ
Evolution of design Guidelines for DFAM Redesign of a Component 10 years later
Page 3 of
Industrializing Additive Manufacturing - Proceedings of Additive Manufacturing in Products and Applications - AMPA2017
Mirko Meboldt, Christoph Klahn
Springer, 05-Sep-2017 - Technology & Engineering - 362 pages https://books.google.co.in/books?id=slU0DwAAQBAJ
Fusion 360 for Makers: Design Your Own Digital Models for 3D Printing and CNC Fabrication
Lydia Sloan Cline
Maker Media, Inc., 11-May-2018 - Computers - 304 pages https://books.google.co.in/books?id=NKxaDwAAQBAJ
Some Interesting Design Illustrations for Additive Manufacturing
The LPBF parameter space consists of laser power, scan speed, laser spot size, scanning strategy, feedstock, part geometry, and machine conditions. The selection of process parameters determines the resulting microstructure and component properties.
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Smart process mapping of powder bed fusion additively manufactured metallic wicks using surrogate modeling
Published: 06 March 2024
(2024)
Journal of Intelligent Manufacturing
Mohammad Borumand, Saideep Nannapaneni, Gurucharan Madiraddy, Michael P. Sealy, Sima Esfandiarpour Borujeni & Gisuk Hwang
Abstract
Powder bed fusion is an innovative additive manufacturing (AM) technique to achieve metallic wick structures for efficient two-phase thermal management systems. However, a technical challenge lies in the lack of standard process maps as it currently relies on an expensive trial and error approach. In this study, five types of surrogate models for classification analysis (i.e., naïve Bayes, logistic regression, random forest, support vector machine, and Gaussian process classification) were constructed and compared to efficiently unlock the relations between five process parameters (i.e., laser power, scan speed, hatch spacing, spot diameter, and effective laser energy) and wick manufacturability. The models were trained using data from a total of 187 AM wick manufacturability experiments. Using four process parameter (PP) model (five PP model without effective laser energy), the Gaussian process classification (GPC) showed the maximum median prediction accuracy (PA) of 93%, while it further improved to 99.7% using support vector machine (SVM) and five process parameter model. Also, the median PAs of the SVM and GPC remains above 98.5% with only 60% of the total experimental data using five PP model. The sensitivity analysis showed that the hatch spacing was the most sensitive parameter for the wick manufacturability using four PP model, while the effective laser energy is the most sensitive one using five PP model. This study provides insights into the smart selection of optimal process parameters for the desired metallic AM wicks.
Unlock the relations between five process parameters (i.e., laser power, scan speed, hatch spacing, spot diameter, and effective laser energy) and wick manufacturability.
Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression
Naol Dessalegn Dejene, Hirpa G. Lemu & Endalkachew Mosisa Gutema
Open access
The International Journal of Advanced Manufacturing Technology
Volume 133, pages 5611–5625, (2024)
You have full access to this open access article
Abstract
Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.
A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process
Published: 23 August 2022
Volume 34, pages 3341–3363, (2023)
Journal of Intelligent Manufacturing
Zhaochen Gu, Shashank Sharma, Daniel A. Riley, Mangesh V. Pantawane, Sameehan S. Joshi, Song Fu & Narendra B. Dahotre
Abstract
The primary bottlenecks faced by the laser powder bed fusion (LPBF) process is the identification of optimal process parameters to obtain high density (> 99.8%) and a good surface finish (< 10 µm) in the fabricated components. Prediction of optimal process maps with the help of machine learning (ML) models is still challenging due to extensive training data, which proves to be expensive in additive manufacturing. In view of this, the present study employs six different supervised ML algorithms on a comparatively small data set of 33 experiments to predict relative density and surface roughness. It has been observed that input data (predictor) curation can increase the accuracy of the ML models even with a small data set. In the ML prediction model, the mean absolute percentage error (MAPE) was reduced by 30% (relative density) and 21.94% (surface roughness) with volumetric energy density as an input parameter instead of laser power, scanning speed, hatch space, and layer thickness. The choice of non-dimensional energy input as a universal predictor allows for an increase in training size and the translation capability of trained ML models from one machine/material combination to another. The ML model based on increased training data size (198 for relative density and 173 for surface roughness) procured from the material processed/fabricated on different LPBF machines showcased reasonable R2 values of 79.11% and 80.3% for relative density and surface roughness, respectively.
The LPBF parameter space consists of laser power, scan speed, laser spot size, scanning strategy, feedstock, part geometry, and machine conditions. The selection of process parameters determines the resulting microstructure and component properties.
Various libraries of process parameters for a given machine and material have been determined through physical testing by AM suppliers or individual laboratories. An integrated computational materials engineering (ICME) approach reduces the amount of physical testing and informs design engineers about detrimental performance expected for specific process parameters.
Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2019 Symposium (2020)
3D Systems' Sterolithography Apparatus
Stratasys' PolyJet
3D Systems' MultiJet Printing System
EnvisionTec's Perfactory®
RegenHU's 3D Bioprinting
Rapid Freeze Prototyping
Optomec's Aerosol Jet Systems
Two-Photon Polymerisation
3DCeram's Ceramic Parts
Other Liquid-Based AM Systems
Solid-Based Additive Manufacturing Systems
Stratasys' Fused Deposition Modelling
Mcor Technologies' Selective Deposition Lamination
Sciaky's Electron Beam Additive Manufacturing
Fabrisonic's Ultrasonic Additive Manufacturing
Other Solid-Based AM Systems
Powder-Based Additive Manufacturing Systems
3D Systems' SLS
SLM Solutions' Selective Laser Melting
3D Systems' CJP Technology
BeAM's LMD Systems
Arcam's Electron Beam Melting
DMG MORI's Hybrid AM
ExOne's Digital Part Materialisation
HP's Multi Jet Fusion™
Other Powder-Based AM Systems
DFAM is to be viewed as a new design approach: a framework for the composition of a part or an assembly, and the application of specific design tools geared toward AM.
By focusing on how a product should function rather than how it’s made, companies drive innovation. The decision can be to use a traditional process like CNC machining, AM, or a hybrid of both. The best solution is derived from an unencumbered analysis for the desired function.Direct Digital Manufacturing (DDM) has proven especially useful in many situations, whether as an end-use solution or as means to augment existing processes.
