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.

机织复合材料钻孔过程中未检测到的工具磨损会导致层压板损坏以及纤维拔出和起毛，从而导致表面下损坏。这会缩短所产生零件在疲劳载荷下的寿命。因此，要在机织复合材料中产生适当而可靠的孔，就需要对切削工具的磨损程度进行精确监控，以保护加工零件并提高加工生产率和利润率。可用的刀具状态监测（TCM）系统主要需要较长的开发准备时间和大量的实验工作，才能在预定的切削条件值内预测刀具磨损。这些值的更改需要系统重新学习。因此，开发一种智能的通用TCM系统至关重要，该系统能够在编织复合板钻孔过程中基于未学习的数据准确地预测工具磨损。在这项工作中，提出并讨论了使用偏向学习数据基于钻孔力来预测在不同和广泛切削条件下编织复合板钻孔时的工具磨损的尝试。提出了一种广义启发式模型来准确预测刀具磨损值。相对于四种机器学习技术（即回归树，支持向量机（SVM），高斯过程回归（GPR）和人工神经网络（ANN））对所提出模型的性能进行了基准测试。大量的实验验证测试表明，GPR模型基于减少的偏向学习数据集提供了最低的预测误差，与现有文献相比，这意味着学习努力减少了50％。然而，