A smart tool wear prediction model in drilling of woven composites

• Published in: International journal of advanced manufacturing technology 2020-10, Vol.110 (11-12), p.2881-2892
• Main Authors: Hegab, H., Hassan, M., Rawat, S., Sadek, A., Attia, H.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; CAE) and Design; Composite structures; Computer-Aided Engineering (CAD; Condition monitoring; Cutting tools; Cutting wear; Damage; Drilling; Engineering; Fiber pullout; Gaussian process; Industrial and Production Engineering; Lead time; Learning theory; Machine learning; Machine tools; Mechanical Engineering; Media Management; Model accuracy; Original Article; Prediction models; Profitability; Regression analysis; Support vector machines; Tool wear; Woven composites
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-020-06049-4

Undetected tool wear during drilling of woven composites can cause laminate damage and fiber pull-out and fuzzing, causing subsurface damage. This diminishes the life of the produced part under fatigue loads. Thus, the producing of proper and reliable holes in woven composites requires accurate monitoring of the cutting tool wear level to safeguard the machined parts and increase process productivity and profitability. Available tool condition monitoring (TCM) systems mainly require long development lead time and extensive experimental efforts to predict the tool wear within predefined values of cutting conditions. The changes in these values require system relearning. Therefore, developing of a smart generalized TCM system that can accurately predict tool wear based on unlearned data during drilling of woven composite plates is crucial. In this work, an attempt was presented and discussed to predict the tool wear in drilling of woven composite plates at different and wide range of cutting conditions based on the drilling forces using biased learning data. A generalized heuristic model was proposed to accurately predict tool wear value. The performance of the proposed model was benchmarked with respect to four machine learning techniques namely regression tree, support vector machine (SVM), Gaussian process regression (GPR), and artificial neural network (ANN). Extensive experimental validation tests have showed that the GPR model has offered the lowest prediction error based on a reduced biased learning dataset, which represents 50% reduction in learning efforts compared with available literature. However, the developed heuristic model showed a comparable accuracy using significantly less learning efforts.