The effect of High Speed Machining on the Crater Wear Behaviour of PCBN Tools in Hard Turning.

Gordon, Seamus; Phelan, Pat; Lahiff, C.

doi:10.1016/j.promfg.2020.01.076

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…Production companies are attempting to boost product quality as well as to reduce operating costs. Online real-time control and monitoring of drilling processes was proposed as an effective method to minimize manufacturing costs [1]. The drilling process is one of the most used processes in manufacturing across various industries such as automotive and aerospace sectors [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Alajmi

Almeshal

2020

Materials

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Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R2 = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R2 = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

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Section: Introductionmentioning

confidence: 99%

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Alajmi

Almeshal

2020

Materials

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“…They concluded that, when using low content CBN tools (lower than 50% in weight), adhesion is negligible, and the effect of diffusive wear is reduced due to the high thermal stability of TiC binder phase. The effects of CBN content on crater wear behavior has been also analyzed by Gordon et al [23] when machining AISI 4340 steel. They discovered that at cutting speeds over 300 m/min, the dominant wear mechanism for high content CBN is the plastic deformation of the tool, with a reduction of the abrasive-diffusive contribution and a decrease of the crater depth with high speeds.…”

Section: Introductionmentioning

confidence: 99%

Development and implementation of crater and flank tool wear model for hard turning simulations

Cappellini

Abeni

2022

Int J Adv Manuf Technol

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This paper concerns the tool wear in hard turning of AISI 52100 hardened steel by means of PCBN tools. The purposes of this work are the development of a tool wear model and its implementation in a FEM-based procedure for predicting crater and flank wear progression during machining operations for studying the influence of tool wear on the process in terms of tool geometry modifications and stress variation on the tool. The developed tool wear model, able to update the geometry of the worn tool as a function of the wear rate, has been implemented in the utilized Deform 2D FEM software. This new analytical model differs from the already proposed methods of existing research, since it concerns both crater and flank wear evaluation. The validation of the model has been achieved by the comparison between experimental and simulated wear parameters. For doing this, an extended experimental campaign has been accomplished. The comparison results have shown good agreement. Once validated, the FEM strategy has been utilized for examining the influence of tool wear on the effective rake angle and the related tool stresses, individuating the excessive positive rake angle value as the final tool breakage mechanism.

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“…They concluded that when using low content CBN tools (lower than 50% in weight), adhesion is negligible, and the effect of diffusive wear is reduced due to the high thermal stability of TiC binder phase. The effects of CBN content on crater wear behavior has been also analyzed by Gordon et al [20] when machining AISI 4340 steel. They discovered that at cutting speeds over 300 m/min the dominant wear mechanism for high content CBN is the plastic deformation of the tool, reducing the abrasive-diffusive contribution and decreasing the crater depth with high speeds.…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of Crater and Flank Tool Wear Model for Hard Turning Simulations

Cappellini

Abeni

2021

Preprint

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This paper concerns the tool wear in hard turning of AISI 52100 hardened steel by means of PCBN tools. The purposes of this work are the development of a tool wear model and its implementation in a FEM-based procedure for predicting crater and flank wear progression during machining operations for studying the influence of tool wear on the process in terms of tool geometry modifications and stress variation on the tool. Deform 2D FEM software has been utilized to simulate the orthogonal cutting process and the tool wear model has been implemented into the software by means of a dedicated subroutine able to estimate the wear rate and to update the geometry of the worn tool. Previous performed research showed the employment of analytical models for the evaluation of crater wear of flank wear separately, and FEM models only for the crater depth evolution without pointing their attention on the behavior of flank width. A new analytical model, concerning both crater and flank wear, has been proposed and validated by the authors. The validation of the model has been achieved by the comparison between experimental and simulated wear parameters. For doing this, an extended experimental campaign has been accomplished. The comparison results have shown good agreement. Once validated, the FEM strategy has been utilized for examining the influence of tool wear on the effective rake angle and the related tool stresses, individuating the excessive positive rake angle value as the final tool breakage mechanism.

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The effect of High Speed Machining on the Crater Wear Behaviour of PCBN Tools in Hard Turning.

Cited by 13 publications

References 17 publications

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Development and implementation of crater and flank tool wear model for hard turning simulations

Development and Implementation of Crater and Flank Tool Wear Model for Hard Turning Simulations

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