Abstract
Selective laser melting (SLM) additive manufacturing overcomes the geometric limits of complex components produced with traditional subtractive methods, which has significant advantages in designing and manufacturing special-shaped components. However, due to the lack of adequate and effective process monitoring, it is difficult to ensure the reliability of as-built parts and the stability of the additive manufacturing process. Therefore, it is necessary to monitor the as-built part quality of the SLM process. An in situ quality monitoring method of acoustic emission (AE) based on machine learning and improved variational modal decomposition (VMD) is proposed in the present work. The VMD parameters are adjusted based on the whale optimization algorithm (WOA) and average energy entropy to realize the adaptive decomposition of the AE signals. Each sub-mode is evaluated according to the signal energy, the feature vector used for SLM printing quality prediction is extracted. Finally, the artificial neural network (ANN) and support vector machine (SVM) are employed for quality prediction. The improved VMD method is compared with empirical modal decomposition (EMD), aiming to verify the predictive validity of printing quality in the SLM process. The results show that predicting SLM printing quality based on improved VMD is better than the EMD method. Meanwhile, it is verified that online monitoring of SLM for improving printing quality can be achieved based on the AE technique.
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References
Wang D, Wang YM, Yang YQ, Liu JB, Xu ZL, Li S, Lin KJ, Zhang DY (2019) Research on design optimization and manufacturing of coating pipes for automobile seal based on selective laser melting. J Mater Process Technol 273:11627
Tabaie S, Aria FR, Flipo BCD, Jahazi M (2022) Dissimilar linear friction welding of selective laser melted Inconel 718 to forged Ni-based superalloy AD730™: Evolution of strengthening phases. J Mater Sci Technol 96:248–261
Ilyas H, Fatih Y, Ayhan E (2021) The effects of build orientation and hatch spacing on mechanical properties of medical Ti-6Al-4V alloy manufactured by selective laser melting. Mater Sci Eng, A 802:140649
Singla AK, Banerjee M, Sharma A, Singh J, Bansal A, Gupta MK, Khanna N, Shahi AS, Goyal DK (2021) Selective laser melting of Ti6Al4V alloy: process parameters, defects and post-treatments. J Manuf Process 64:161–187
Sun CQ, Wang WJ, Duan Y (2021) Characteristic and mechanism of crack initiation and early growth of an additively manufactured Ti-6Al-4V in very high cycle fatigue regime. Int J Mech Sci 205:106591
Chen DJ, Wang P, Pan R, Zha CQ, Fan JW, Kong S, Li J, Zeng ZQ (2021) Research on in situ monitoring of selective laser melting: a state of the art review. Int J Adv Manuf Technol 113(11–12):3121–3138
Lu QY, Nguyen NV, Hum AJW, Tran T, Wong CH (2020) Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography. Addit Manuf 35:101287
Lu QY, Nguyen NV, Hum AJW, Tran T, Wong CH (2019) Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density. J Mater Process Technol 271:520–531
Shmueli Y, Jiang JT, Zhou YC, Xue Y, Chang CC, Yuan GG, Satija SK, Lee S, Nam CY, Kim T, Marom G, Gersappe D, Rafailovich MH (2019) Simultaneous in situ X-ray scattering and infrared imaging of polymer extrusion in additive manufacturing. ACS Applied Polymer Materials 1(6):1559–1567
Hertleina N, Deshpandea S, Venugopala V, Kumarb M, Ananda S (2020) Prediction of selective laser melting part quality using hybrid Bayesian network. Addit Manuf 32:101089
Snow Z, Diehl B, Reutzel EW, Nassar N (2021) Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. J Manuf Syst 59:12–26
Yadav P, Rigo O, Arvieu C, Guen EL, Lacoste E (2021) Data treatment of in situ monitoring systems in selective laser melting machines. Adv Eng Mater 23(5):2001327
Wang HJ, Li B, Xuan FZ (2022) A dimensionally augmented and physics-informed machine learning for quality prediction of additively manufactured high-entropy alloy. J Mater Process Technol 307:117637
Mycroft W, Katzman M, Tammas-Williams S, Hernandez-Nava E, Panoutsos G, Todd I, Kadirkamanathan V (2020) A data-driven approach for predicting printability in metal additive manufacturing processes. J Intell Manuf 31(7):1–13
Barrionuevo GO, Grez JAR, Walczak M, Betancourt CA (2021) Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting. Int J Adv Manuf Technol 113(1–2):419–433
Chen YY, Wang HZ, Wu Y, Wang HW (2020) Predicting the printability in selective laser melting with a supervised machine learning method. Materials 13(22):5063
Rankouhia B, Jahanib S, Pfefferkorna FE, Thomaac DJ (2021) Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters. Addit Manuf 38:101836
Xu WL, Zhang J, Li XH, Yuan SX, Ma GB, Xue ZX, Jing XC, Cao JC (2022) Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface. NDT and E Int 125:102548
Takuma M, Hisada S, Saitoh K, Takahashi Y, Kobayashi Y, Kadono A, Murata A, Iwata S, Sasaki T (2014) Acoustic emission measurement by fiber bragg grating glued to cylindrical sensor holder. Adv Mater Sci Eng 1:1–12
Shevchik SA, Kenel C, Leinenbach C, Wasmer K (2018) Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit Manuf 21:598–604
Wasmer K, Quang TL, Meylan B, Shevchik SA (2019) In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. J Mater Eng Perform 28(2):666–672
Hossain MS, Hossein T (2020) In situ process monitoring for additive manufacturing through acoustic techniques. J Mater Eng Perform 29(10):6249–6262
Hossain MS, Hossein T (2021) In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network. Int J Adv Manuf Technol 116(11–12):1–16
Qi XB, Chen GF, Li Y, Cheng X, Li CP (2019) Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 5(4):721–729
Shevchik SA, Masinelli G, Kenel C, Leinenbach C, Wasmer K (2019) Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Trans Industr Inf 15(9):5194–5203
Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544
Diao X, Jiang JC, Shen GD, Chi ZZ, Wang ZR, Ni L, Mebarki A, Bian HT, Hao YM (2020) An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines. Mech Syst Signal Process 143:106787
Li ZP, Chen JL, Zi YY, Pan J (2017) Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive. Mech Syst Signal Process 85:512–529
Wang JY, Li JG, Wang HT, Guo LX (2021) Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition. J Low Freq Noise V A 40(1):332–346
Seyedali M, Andrew L (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Liu LS, Chen LQ, Wang ZL, Liu DT (2021) Early fault detection of planetary gearbox based on acoustic emission and improved variational mode decomposition. IEEE Sens J 21(2):1735–1745
Wang HJ, Li B, Xuan FZ (2022) Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features. Int J Fatigue 164:107147
Funding
This research work is sponsored by the National Natural Science Foundation of China (No. 52175140, No. 51835003), International Collaboration Program from Science and Technology Commission of Shanghai Municipality in China (No. 19110712500), Natural Science Foundation of Shanghai in China (No. 20ZR1414000), and the Fundamental Research Funds for the Central Universities in China.
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Haijie Wang: Investigation, Methodology, Writing-original draft, Validation. Bo Li: Investigation, Methodology, Revising draft, Supervision, Funding acquisition, Validation. Fuzhen Xuan: Conceptualization, Resources, Project administration.
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Wang, H., Li, B. & Xuan, FZ. Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition. Int J Adv Manuf Technol 122, 2277–2292 (2022). https://doi.org/10.1007/s00170-022-10032-6
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DOI: https://doi.org/10.1007/s00170-022-10032-6