TY - JOUR
T1 - Bayesian neural networks modeling for tool wear prediction in milling Al 6061 T6 under MQL conditions
AU - Airao, Jay
AU - Gupta, Abhishek
AU - Nirala, Chandrakant K.
AU - Hsue, Albert Wen Jeng
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
PY - 2024/11
Y1 - 2024/11
N2 - The integration of artificial intelligence, machine learning, and deep learning algorithms into machining processes has made them more intelligent, significantly reducing costs, improving production rates, and enhancing product quality by accurately predicting machining responses. In this study, a Bayesian neural network (BNN) is employed to predict tool wear during the milling of Al6061 T6 alloy, showcasing the novelty of BNN in handling uncertainty and providing reliable predictions. Milling experiments were conducted at three different spindle speeds under dry, flood, and minimum quantity lubrication (MQL) strategies. Machinability was evaluated by considering tool wear, milling forces, and surface quality. Unique to this study is the use of force and current signals as input to the BNN model, capturing real-time data to estimate tool wear. The signals were trained and tested to predict tool wear under varying cutting conditions. The results indicated that tool wear in dry conditions was primarily due to adhesion, leading to higher milling forces and poorer surface quality. In comparison, the wet and MQL conditions resulted in 11–21% and 9–13% less tool wear, respectively, than dry conditions, alongside improved surface roughness and reduced machining forces. The BNN model demonstrated its ability to avoid overfitting, providing highly accurate predictions with an error margin of 2–15% when compared to experimental results. Unlike conventional models, the BNN accounts for prediction uncertainty, making it more robust and reliable across different datasets. Thus, the proposed BNN model proves its effectiveness and generalizability in predicting tool wear under various machining conditions, setting a new benchmark for the application of artificial intelligence in machining processes.
AB - The integration of artificial intelligence, machine learning, and deep learning algorithms into machining processes has made them more intelligent, significantly reducing costs, improving production rates, and enhancing product quality by accurately predicting machining responses. In this study, a Bayesian neural network (BNN) is employed to predict tool wear during the milling of Al6061 T6 alloy, showcasing the novelty of BNN in handling uncertainty and providing reliable predictions. Milling experiments were conducted at three different spindle speeds under dry, flood, and minimum quantity lubrication (MQL) strategies. Machinability was evaluated by considering tool wear, milling forces, and surface quality. Unique to this study is the use of force and current signals as input to the BNN model, capturing real-time data to estimate tool wear. The signals were trained and tested to predict tool wear under varying cutting conditions. The results indicated that tool wear in dry conditions was primarily due to adhesion, leading to higher milling forces and poorer surface quality. In comparison, the wet and MQL conditions resulted in 11–21% and 9–13% less tool wear, respectively, than dry conditions, alongside improved surface roughness and reduced machining forces. The BNN model demonstrated its ability to avoid overfitting, providing highly accurate predictions with an error margin of 2–15% when compared to experimental results. Unlike conventional models, the BNN accounts for prediction uncertainty, making it more robust and reliable across different datasets. Thus, the proposed BNN model proves its effectiveness and generalizability in predicting tool wear under various machining conditions, setting a new benchmark for the application of artificial intelligence in machining processes.
KW - Al 6061 T6
KW - Bayesian neural network
KW - End milling
KW - Machine learning
KW - MQL
KW - Tool wear
UR - http://www.scopus.com/inward/record.url?scp=85206992095&partnerID=8YFLogxK
U2 - 10.1007/s00170-024-14678-2
DO - 10.1007/s00170-024-14678-2
M3 - Journal article
AN - SCOPUS:85206992095
SN - 0268-3768
VL - 135
SP - 2777
EP - 2788
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
IS - 5-6
ER -