การใช้เครื่องมือการทำการตัดสินใจแบบหลายปัจจัยในการแก้ปัญหาทิศทางการผลิตชิ้นงานจากการผลิตแบบดั้งเดิมและแบบพิมพ์สามมิติ (Multi-Criteria Decision Analysis-based Orientation Selection Problem for Integrated 3D Printing and Subtractive Manufacturing)
DOI: 10.14416/j.ind.tech.2020.03.002
Keywords:
Three-Dimensional Printing, Orientation Selection Problem, Multi-Criteria Decision Analysis, Data Envelopment Analysis, Analytic Hierarchy Process, Linear NormalizationAbstract
This study examines the orientation selection problem for the three-dimensional printing (3DP) and subtractive manufacturing technologies by comparing the 3D Fused Deposition Modeling (FDM), the 3D Stereolithography (SLA), and the lathe machine for assembly part production. Whereas materials used for 3D FDM are Acrylonitrile-Butadiene-Styrene (ABS) and Polylactic Acid (PLA) thermoplastics and material used for 3D SLA is resin; the material used for lathe machine is solid ABS. The orientation directions analyzed for 3DP assembly parts are from 0 degrees, 45 degrees, 90 degrees, and 180 degrees, respectively. Data are then collected for the comparative study from various criteria, which are consumed material, production time, production cost, support material, production accuracy, assembly, and surface quality. Then, we use the multi-criteria decision analysis (MCDA) techniques by starting with the Data Envelopment Analysis (DEA) to analyze the relative efficiency of each manufacturing technology group and each material type. Then, multiple factors are analyzed using the Analytic Hierarchy Process (AHP) to assess the criteria weight based on the preference of a decision-maker. The Linear Normalization analysis is then later used to find the best alternative of orientation direction. The results show that production orientation with 0 degrees provides the best orientation alternative superior in terms of minimal consumed material, production cost, and support material. In addition, dissimilar technologies are found to affect production criteria and preferences from decision-makers are also found to impact how the production alternatives are ranked.
References
[2] K. Wisetla, W. Chanthakhot, N. Wattanasaeng, and K. Ransikarbum, Trends in Research and Development of 3D Printing Technology in Thailand, Proceedings of Industrial Engineering Network, 2018, 1635 – 1639. (in Thai)
[3] K. Wisetla, N. Wattanasaeng, W. Chanthakhot, and K. Ransikarbum, Decisions to Find the Efficiency of the 3D Printing Process using FDM based on Data Envelopment Analysis Technique, Proceedings of The 12th Ubon Ratchathani University Conference, 2018, 25 – 35. (in Thai)
[4] K. Ransikarbum, Teaching Materials for Advanced Manufacturing, Department of Industrial Engineering, Ubon Ratchathani University, 2018, pp. 138. (in Thai)
[5] https://hbr.org/2015/05/the-3-d-printing-revolution (Accessed on 25 October 2019)
[6] A. Azari and S. Nikzad, The Evolution of Rapid Prototyping in Dentistry: A Review, Rapid Prototyping Journal, 2009, 15, 216 – 225.
[7] T.S. Douglas, Additive Manufacturing: From Implants to Organs, SAMJ: South African Medical Journal, 104, 2014, 408 – 409.
[8] K. Ransikarbum and N. Kim, Data Envelopment Analysis-based Multi-Criteria Decision Making for Part Orientation Selection in Fused Deposition Modeling, The 4th International Conference on Industrial Engineering and Applications (ICIEA 2017), Proceedings, 2017, 81 – 85.
[9] K. Ransikarbum and N. Kim, Multi-Criteria Selection Problem of Part Orientation in 3D Fused Deposition Modeling based on Analytic Hierarchy Process Model: A Case Study, The IEEE International Conference on Industrial Engineering and Engineering Management 2017 (IEEM 2017), Proceedings, 1455 –1459.
[10] B. Esmaeilian, S. Behdad, and B. Wang, The Evolution and Future of Manufacturing: A Review”, Journal of Manufacturing Systems, 2016, 39, 79 – 100.
[11] W.D. Cook, K. Tone, and J. Zhu, Data Envelopment Analysis: Prior to Choosing a Model, Omega, 2014, 44, 1 – 4.
[12] www.expertchoice.com (Accessed on 25 October 2019)
[13] http://dupress.com/articles/the-3d-opportunity-primer-the-basics-of-additive-manufacturing (Accessed on 25 October 2019)
[14] www.rsc.org/journals-books-databases (Accessed on 25 October 2019)
[15] T. Wohlers, Wohlers Report 2018, Wohlers Associates, Inc., CO, USA, 2018.
[16] K.V. Wong and A. Hernandez, A Review of Additive Manufacturing, ISRN Mechanical Engineering, 2012, Art. 208760, 1 – 10.
[17] Y. Zhang and A. Bernard, An Integrated Decision-Making Model for Multi-Attributes Decision-Making (MADM) Problems in Additive Manufacturing Process Planning, Rapid Prototyping Journal, 2014, 20, 377 – 389.
[18] Y. Zhang, A. Bernard, R.K. Gupta, and R. Harik, Feature based Building Orientation Optimization for Additive Manufacturing, Rapid Prototyping Journal, 2016, 22, 358 – 376.
[19] O.S. Vaidya and S. Kumar, Analytic Hierarchy Process: An Overview of Applications, European Journal of Operational Research, 2006, 169, 1 – 29.
[20] K. Ransikarbum and S.J. Mason, Goal Programming-based Post-Disaster Decision Making for Integrated Relief Distribution and Early-Stage Network Restoration, International Journal of Production Economics, 2016, 182, 324 – 341.
[21] K. Ransikarbum, S. Ha, J. Ma, and N. Kim, Multi-Objective Optimization Model for Production Planning of the Build Chamber Utilization in Fused Deposition Modeling, Journal of Manufacturing Systems, 2017, 43, 35 – 46.
[22] A.R. Ravindran, Operations Research and Management Science Handbook, CRC Press, Boca Raton, FL, USA, 2007.
[23] W. Ho, Integrated Analytic Hierarchy Process and Its Applications- A Literature Review, European Journal of Operational Research, 2008, 186, 211 – 228.
[24] A. Charnes, W.W. Cooper, and E. Rhodes, Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through, Management Science, 1981, 27(6), 668 – 697.
[25] R.D. Banker, A. Charnes, and W.W. Cooper, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 1984, 30(9), 1078 – 1092.
[26] T.L. Saaty, How to Make a Decision: The Analytic Hierarchy Process, European Journal of Operational Research, 1990, 48(1), 9 – 26.
