This study aims to optimize the uniformity of the temperature field during sintering to improve part performance. A temperature-field monitoring system is established based on an infrared thermal imager and the temperature field data obtained during the sintering of a part can be measured in real time. The relationship among the sintering temperature field, sintering process parameters, and part performance is established experimentally. Subsequently, a temperature field monitoring and analysis system is constructed, and various sintering temperaturefield control strategies are established for various part sizes. Finally, a dynamic control strategy for controlling the temperature field during sintering is proposed, experimentally validated, and fully integrated into a developed powder bed fusion (PBF) equipment. For eight-shaped standard parts, the range of sintering temperature field is optimized from 44.1 C to 19.7 C, whereas the tensile strength of the parts increased by 15.4%. For large-size H parts, localized over burning is eliminated and the final quality of the part is optimized. This strategy is critical for the optimization of the PBF process for large-sized parts, in particular in the large-sized die manufacturing industry, which offers promise in the optimization of part performance.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00317-y
Xiao-Kang Huang
,
Xiao-Yong Tian
,
Qi Zhong
,
Shun-Wen He
,
Chun-Bao Huo
,
Yi Cao
,
Zhi-Qiang Tong
,
Di-Chen Li
. Real-time process control of powder bed fusion by monitoring dynamic temperature field[J]. Advances in Manufacturing, 2020
, 8(3)
: 380
-391
.
DOI: 10.1007/s40436-020-00317-y
1. Giuseppe M, Roberta S, Alba S (2016) Reliability analysis of structures with interval uncertainties under stationary stochastic excitations. Comput Methods Appl Mech Eng 300:47-69
2. Wang Z, Liu J, Yu S (2019) Time-variant reliability prediction for dynamic systems using partial information. Reliab Eng Syst Saf. https://doi.org/10.1016/j.ress.2019.106756
3. Yu S, Wang Z (2019) A general decoupling approach for timeand space-variant system reliability-based design optimization. Comput Methods Appl Mech Eng 357:112608
4. Zhang J, Du X (2015) Time-dependent reliability analysis for function generation mechanisms with random joint clearances. Mech Mach Theory 92:184-199
5. Zhang D, Han X (2020) Kinematic reliability analysis of robotic manipulator. J Mech Des 142(4):044502. https://doi.org/10.1115/1.4044436
6. Wang Z, Cheng X, Liu J (2018) Time-variant concurrent reliability-based design optimization integrating experiment-based model validation. Struct Multidiscip Optim 57(4):1523-1531
7. Shi Y, Lu Z, Xu L et al (2019) An adaptive multiple-krigingsurrogate method for time-dependent reliability analysis. Appl Math Modell 70:545-571
8. Yu S, Wang Z (2018) A novel time-variant reliability analysis method based on failure processes decomposition for dynamic uncertain structures. J Mech Des 140(5):051401. https://doi.org/10.1115/1.4039387
9. Wang J, Zhang J, Du X (2011) Hybrid dimension reduction for mechanism reliability analysis with random joint clearances. Mech Mach Theory 46(10):1396-1410
10. Zhao Y, Ono T (2001) Moment methods for structural reliability. Struct Saf 23(1):47-75
11. Beck A, Melchers R (2004) On the ensemble crossing rate approach to time-variant reliability analysis of uncertain structures. Probab Eng Mech 19(1/2):9-19
12. Wang ZL, Wang ZH, Yu S et al (2019) Time-dependent concurrent reliability-based design optimization integrating the timevariant B-distance index. J Mech Des 141(9):091403. https://doi.org/10.1115/1.4043735
13. Rice S (1944) Mathematical analysis of random noise. Bell Syst Tech J 23(3):282-332
14. Andrieu-Renaud C, Sudret B, Lemaire M (2004) The PHI2 method:a way to compute time-variant reliability. Reliab Eng Syst Saf 84(1):75-86
15. Hu Z, Du XP (2013) Time-variant reliability analysis with joint up crossing rates. Struct Multidiscip Optim 48(5):893-907
16. Li J, Chen JB, Fan W (2007) The equivalent extreme-value event and evaluation of the structural system reliability. Struct Saf 29:112-131
17. Hu Z, Du XP (2013) A sampling approach to extreme value distribution for time-variant reliability analysis. J Mech Des 135(7):071003. https://doi.org/10.1115/1.4023925
18. Du XP (2014) Time-dependent mechanism reliability analysis with envelope functions and first-order approximation. J Mech Des 136(8):081010. https://doi.org/10.1115/1.4027636
19. Gholaminezhad I, Jamali A, Assimi H (2017) Multi-objective reliability-based robust design optimization of robot gripper mechanism with probabilistically uncertain parameters. Neural Comput Appl 28(1):659-670
20. Yu S, Wang Z, Meng D (2018) Time-variant reliability assessment for multiple failure modes and temporal parameters. Struct Multidiscip Optim 58(4):1705-1717
21. Moon MY, Choi KK, Cho H et al (2017) Reliability-based design optimization using confidence-based model validation for insufficient experimental data. J Mech Des 139(3):031404. https://doi.org/10.1115/1.4035679
22. Yang X, Liu Y, Mi C et al (2018) Active learning kriging model combining with kernel-density-estimation-based importance sampling method for the estimation of low failure probability. J Mech Des 140(5):051402. https://doi.org/10.1115/1.4039339
23. Yu S, Wang Z, Zhang K (2018) Sequential time-variant reliability analysis for the lower extremity exoskeleton under uncertainty. Reliab Eng Syst Saf 170:45-52
24. Aas K, Czado C, Frigessi A et al (2009) Pair-copula constructions of multiple dependence. Insur Math Econ 44(2):182-198
25. Papaefthymiou G, Kurowicka D (2009) Using copulas for modeling stochastic dependence in power system uncertainty analysis. IEEE Trans Power Syst 4(1):40-49
26. Sklar A (1973) Random variables, joint distribution functions, and copulas. Kybernetika 9(6):449-460
27. Wang Z, Wang Z, Yu S et al (2019) Time-dependent mechanism reliability analysis based on envelope function and vine-copula function. Mech Mach Theory 134:667-684
28. Zhang X, Wilson A (2017) System reliability and component importance under dependence:a copula approach. Technometrics 59(2):215-224
