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A smart tool wear prediction model in drilling of woven composites

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Abstract

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.

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Abbreviations

VB:

Flank tool wear

CFRP:

Carbon fiber–reinforced polymer

GFRP:

Glass fiber–reinforced polymer

ANFIS:

Adaptive network–fuzzy systems

SVM:

Support vector machine

GPR:

Gaussian process regression

ANN:

Artificial neural network

Fz:

Thrust force

Fc:

Cutting force

ΔFzi :

The change in the thrust force at fresh tool (i = 0) and after number of cuts i

ΔFci :

The change in the cutting force at fresh tool (i = 0) and after number of cuts i

a and b:

Trained constants

a’ and b’:

Predicted constants

WMSE:

Weighted mean squared error

MLP:

Multilayer perceptron

RMSE:

Root mean square error

R 2 :

R squared

MAE:

Mean absolute error

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Authors and Affiliations

  1. Mechanical Engineering Department, McGill University, Montreal, QC, Canada

    H. Hegab, S. Rawat & H. Attia

  2. Aerospace Manufacturing Technologies Centre, National Research Council Canada, Montreal, QC, Canada

    H. Hegab, A. Sadek & H. Attia

  3. Aerospace Manufacturing Technologies Centre, National Research Council Canada, Ottawa, Ontario, Canada

    M. Hassan

Authors
  1. H. Hegab
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  2. M. Hassan
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  3. S. Rawat
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  4. A. Sadek
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  5. H. Attia
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Correspondence to H. Hegab.

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Appendix

Appendix

Table 3 Tool wear prediction results (training and validation stages) using fine tree, ANN, GPR, SVM, HA and measured data
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Hegab, H., Hassan, M., Rawat, S. et al. A smart tool wear prediction model in drilling of woven composites. Int J Adv Manuf Technol 110, 2881–2892 (2020). https://doi.org/10.1007/s00170-020-06049-4

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