A model-based approach to refine process parameters in smart manufacturing

Concurrent Engineering Research and Applications

Volumn 23, Issue 4, 2015, Pages 365-376

  Kim, Duck B ^a   Denno, Peter O ^a   Jones, Albert T ^a  

^a NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

Author keywords

additive manufacturing; model based approach; optimization; predictive models; process parameters; smart manufacturing

Indexed keywords

3D PRINTERS; FLOW CONTROL; OPTIMIZATION;

ADDITIVE MANUFACTURING PROCESS; MODEL BASED APPROACH; MODEL-BASED ENGINEERING; PREDICTIVE MODELS; PROCESS IMPROVEMENT FRAMEWORKS; PROCESS PARAMETER OPTIMIZATION; PROCESS PARAMETERS; SMART MANUFACTURING;

MANUFACTURE;

EID: 84950313765     PISSN: 1063293X     EISSN: 15312003     Source Type: Journal    
DOI: 10.1177/1063293X15591038     Document Type: Article

References (26)

1. Alanen M, Porres I, (2004) A Relation between Context-Free Grammars and Meta Object Facility Metamodels. Finland: Turku Centre for Computer Science, Abo Akademi University.
2. Altintas Y, Kersting P, Biermann D, et al. (2014) Virtual process systems for part machining operations. CIRP Annals-Manufacturing Technology 63 (2): 585-605.
3. Baumers M, Tuck C, Wildman R, et al. (2012) Combined build-time, energy consumption and cost estimation for direct metal laser sintering. In: Proceedings of twenty third annual international solid freeform fabrication symposium-an additive manufacturing conference, Austin, TX, 6-8 August 2012, vol. 13, pp. 932-944.
4. Chandrasekaran M, Muralidhar M, Krishna CM, et al. (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. International Journal of Advanced Manufacturing Technology 46 (5-8): 445-464.
5. Charnes A, Cooper WW, (1977) Goal programming and multiple objective optimizations: part 1. European Journal of Operational Research 1 (1): 39-54.
6. Davis J, Edgar T, Porter J, et al. (2012) Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering 47: 145-156.
7. Evans A, France R, Lano K, et al. (1998) Developing the UML as a formal modelling notation. In: UML'98 beyond the notation: international workshop, Mulhouse. Available at: http://www.se-rwth.de/∼rumpe/publications/Developing-the-UML-as-a-Formal-Modelling-Notation.pdf
8. Evans J, Lindsay W, (2014) An Introduction to Six Sigma and Process Improvement. Boston, MA: Cengage Learning.
9. Gong H, Rafi K, Gu H, et al. (2014) Analysis of defect generation in Ti-6Al-4V parts made using powder bed fusion additive manufacturing processes. Additive Manufacturing 1-4: 87-98.
10. Huebscher MC, McCann JA, (2008) A survey of autonomic computing-degrees, models, and applications. ACM Computing Surveys (CSUR) 40 (3): 1-28 (article no. 7).
11. IBM ILOG CPLEX Optimization Studio (n.d.) Available at: http://www-01.ibm.com/software/integration/optimization/cplex-optimization-studio/
12. IEC 62264-2:2013 (2013) International standard: enterprise-control system integration-part 2 objects and attributes for enterprise-control system integration. (International Electrotechnical Commission, edition 2, 2013-06).
13. Liu L, Caldwell BS, Wang H, et al. (2014) A knowledge-centric CNC machine tool design and development process management framework. International Journal of Operational Research 52 (20): 6033-6051.
14. Moscato F, Vittorini V, Amato F, et al. (2012) Solution workflows for model-based analysis of complex systems. IEEE Transactions on Automation Science and Engineering 9 (1): 83-95.
15. Myers RH, Anderson-Cook CM, (2009) Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Hoboken, NJ: John Wiley & Sons.
16. Pascal VH, (1999) The OPL Optimization Programming Language. Cambridge, MA: MIT Press.
17. Pellet J, (2013) Lessons learned from chief executive's manufacturing summit. Chief Executive Magazine, July/August. Available at: http://www.rockwellautomation.com/global/news/releases/summit-lessons.page
18. Roy CJ, Oberkampf WL, (2011) A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Computer Methods in Applied Mechanics and Engineering 200 (25-28): 2131-2144.
19. Saaty TL, (2008) Decision making with the analytic hierarchy process. International Journal of Services Sciences 1 (1): 83-98.
20. Song B, Dong S, Zhang B, et al. (2012) Effects of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V. Materials & Design 35: 120-125.
21. Sun J, Yang Y, Wang D, (2013) Parametric optimization of selective laser melting for forming Ti6Al4V samples by Taguchi method. Optics & Laser Technology 49: 118-124.
22. Thijs L, Verhaeghe F, Craeghs T, et al. (2010) A study of the microstructural evolution during selective laser melting of Ti-6Al-4V. Acta Materialia 58 (9): 3303-3312.
23. Tolio T, Sacco M, Terkaj W, et al. (2013) Virtual factory: an integrated framework for manufacturing systems design and analysis. Procedia CIRP 7: 25-30.
24. Vijayaraghavan A, Dornfeld D, (2010) Automated energy monitoring of machine tools. CIRP Annals-Manufacturing Technology 59: 21-24.
25. Xu X, Lihui W, Stephen TN, (2011) Computer-aided process planning-a critical review of recent developments and future trends. International Journal of Computer Integrated Manufacturing 24 (1): 1-31.
26. Zhang X, Nassehi A, Safaieh M, et al. (2013) Process comprehension for shopfloor manufacturing knowledge reuse. International Journal of Operational Research 51 (23-24): 7405-7419.