Data-driven model for predicting machining cycle time in ultra-precision machining
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OpenAlex topics: Advanced machining processes and optimization · Manufacturing Process and Optimization · Engineering Technology and Methodologies
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References (26)
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1. Zhang SJ, To S, Wang SJ et al (2015) A review of surface roughness generation in ultra-precision machining. Int J Mach Tools Manuf 91:76–9510.1016/j.ijmachtools.2015.02.001 ↗
2. Lamikiz A, López de Lacalle LN, Celaya A (2009) Machine tool performance and precision. In: Lamikiz A (eds), Machine tools for high performance machining, Springer, London, pp 219–260
3. Yip WS, To S, Zhou H (2021) Current status, challenges and opportunities of sustainable ultra-precision manufacturing. J Intell Manuf 33:2193–220510.1007/s10845-021-01782-3 ↗
4. Precision engineering machines market report (2028) https://www.grandviewresearch.com/industry-analysis/precision-engineering-machines-market-report. Accessed 12 May 2023
5. Niazi A, Dai JS, Balabani S et al (2006) Product cost estimation: technique classification and methodology review. J Manuf Sci Eng 128:563–57510.1115/1.2137750 ↗
6. Monreal M, Rodriguez CA (2003) Influence of tool path strategy on the cycle time of high-speed milling. Comput Aided Des 35:395–40110.1016/S0010-4485(02)00060-X ↗
7. Chu HY, Ke D, Jun Y et al (2023) Flexible process planning based on predictive models for machining time and energy consumption. Int J Adv Manuf Technol 128(3/4):1763–178010.1007/s00170-023-12027-3 ↗
8. Abdul Kadir A, Xu X, Hämmerle E (2011) Virtual machine tools and virtual machining—a technological review. Robotics Comput Integr Manuf 27:494–50810.1016/j.rcim.2010.10.003 ↗
9. Petráček P, Fojtů P, Kozlok T et al (2022) Effect of CNC interpolator parameter settings on toolpath precision and quality in corner neighborhoods. Appl Sci 12:9496. https://doi.org/10.3390/app1219949610.3390/app12199496 ↗
10. Liu C, Li Y, Wang W et al (2013) A feature-based method for NC machining time estimation. Robotics Comput Integr Manuf 29:8–1410.1016/j.rcim.2012.10.001 ↗
11. Zhong WB, Luo XC, Chang WL et al (2019) Toolpath interpolation and smoothing for computer numerical control machining of freeform surfaces: a review. Int J Autom Comput 17:1–1610.1007/s11633-019-1190-y ↗
12. Fan W, Gao XS, Yan W et al (2012) Interpolation of parametric CNC machining path under confined jounce. Int J Adv Manuf Technol 62:719–73910.1007/s00170-011-3842-0 ↗
13. Tajima S, Sencer B (2019) Accurate real-time interpolation of 5-axis tool-paths with local corner smoothing. Int J Mach Tools Manuf 142:1–1510.1016/j.ijmachtools.2019.04.005 ↗
14. Tajima S, Sencer B (2017) Global tool-path smoothing for CNC machine tools with uninterrupted acceleration. Int J Mach Tools Manuf 121:81–9510.1016/j.ijmachtools.2017.03.002 ↗
15. Ward R, Sencer B, Jones B et al (2021) Accurate prediction of machining feedrate and cycle times considering interpolator dynamics. Int J Adv Manuf Technol 116:417–43810.1007/s00170-021-07211-2 ↗
16. Altintas Y, Brecher C, Weck M et al (2005) Virtual machine tool. CIRP Ann 54:115–13810.1016/S0007-8506(07)60022-5 ↗
17. Tang PY, Lin MT, Tsai MS et al (2022) Toolpath interpolation with novel corner smoothing technique. Robotics Comput Integr Manuf 78:102388. https://doi.org/10.1016/j.rcim.2022.10238810.1016/j.rcim.2022.102388 ↗
18. Oláh J, Aburumman N, Popp J et al (2020) Impact of Industry 4.0 on environmental sustainability. Sustainability 12:4674. https://doi.org/10.3390/su1211467410.3390/su12114674 ↗
19. Tao F, Qi Q, Liu A et al (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–16910.1016/j.jmsy.2018.01.006 ↗
20. Solomatine D, See LM, Abrahart RJ (2008) Data-driven modelling: concepts, approaches and experiences. In: Abrahart RJ, See LM, Solomatine DP (eds) Practical hydroinformatics: computational intelligence and technological developments in water applications, Springer, Berlin, pp 17–3010.1007/978-3-540-79881-1_2 ↗
21. Altintas Y, Tulsyan S (2015) Prediction of part machining cycle times via virtual CNC. CIRP Ann 64:361–36410.1016/j.cirp.2015.04.100 ↗
22. Endo M, Sencer B (2022) Accurate prediction of machining cycle times by data-driven modelling of NC system’s interpolation dynamics. CIRP Ann 71:405–40810.1016/j.cirp.2022.04.017 ↗
23. Sun C, Dominguez-Caballero J, Ward R et al (2022) Machining cycle time prediction: data-driven modelling of machine tool feedrate behavior with neural networks. Robotics Comput Integr Manuf 75:102293. https://doi.org/10.1016/j.rcim.2021.10229310.1016/j.rcim.2021.102293 ↗
24. Katal A, Singh N (2022) Artificial neural network: models, applications, and challenges. In: Tomar R, Hina MD, Zitouni R et al (eds) Innovative trends in computational intelligence, Springer International Publishing, Cham, pp 235–25710.1007/978-3-030-78284-9_11 ↗
25. Choi YK, Banerjee A, Lee JW (2007) Tool path generation for free form surfaces using Bézier curves/surfaces. Comput Ind Eng 52:486–50110.1016/j.cie.2007.02.004 ↗
26. Ma Q, Yu H (2023) Artificial intelligence-enabled mode-locked fiber laser: a review. Nanomanuf Metrol 6:36. https://doi.org/10.1007/s41871-023-00216-310.1007/s41871-023-00216-3 ↗