Development of a tool wear-monitoring system for hard turning

Scheffer, Cornie; Kratz, Hubert; Heyns, P.S.; Klocke, Fritz

doi:10.1016/s0890-6955(03)00110-x

Cited by 136 publications

(79 citation statements)

References 7 publications

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“…On the basis of these signals the diagnostic features have been defined using the multivariable auto regression model VAR (1). The selection process following the generation of these features was responsible for selecting the most significant ones, which were used as the input attributes to the classification system.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

et al. 2017

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Abstract. The paper presents an on-line automatic system for recognition of the drill condition in a laminated chipboard drilling process. Two states of the drill are considered: the sharp enough (still able to drill holes acceptable for processing quality) and worn out (excessive drill wear, not satisfactory from the quality point of view of the process). The automatic system requires defining the diagnostic features, which are used as the input attributes to the classifier. The features have been generated from 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. The statistical parameters defined on the basis of the auto regression model of these signals have been used as the diagnostic features. The sequential step-wise feature selection is applied for choosing the most discriminative set of features. The final step of recognition is done by support vector machine classifier working in leave one out mode. The results of numerical experiments have confirmed good quality of the proposed diagnostic system.

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Section: Discussionmentioning

confidence: 99%

“…Use Tool Condition Monitoring (TCM) is an important automatic system to improve the quality and productivity of machine tools without interrupting normal operations [1,2,3]. TCM is aiming in detecting or predicting the tool failure.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

et al. 2017

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“…The answer is send to the machine tool system for execution of the received order; x Uses of artificial intelligence by means of neural networks/ fuzzy logic increase the accuracy and reliability of the system. From the earlier researches, one can notice that the main parameters used to monitor the tool wear are the cutting forces, vibrations, acoustic emission and the power consumed [8].…”

Section: Monitoring Of Cutting Tool Statementioning

confidence: 99%

Researches regarding cutting tool condition monitoring

2017

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Abstract. The paper main purpose is monitoring of tool wear in metal cutting using neural networks due to their ability of learning and adapting their self, based on experiments. Monitoring the cutting process is difficult to perform on-line because of the complexity of tool wear process, which is the most important parameter that defines the tool state at a certain moment. Most of the researches appraise the tool wear by indirect factors such as forces, consumed power, vibrations or the surface quality. In this case, it is important to combine many factors for increasing the accuracy of tool wear prediction and establish the admissible size of wear. For this, paper both the theoretical data obtained from FEM analyze and experimental ones are used and compared in order to appreciate the reliability of the results.

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“…Statistical features were the same as for forces/currents. However, for the features from the frequency domain, the energy in frequency ranges has been used (Scheffer et al 2003)…”

Section: Signal Processing and Extraction Of Featuresmentioning

confidence: 99%

Tool wear estimation using an analytic fuzzy classifier and support vector machines

et al. 2010

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A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.

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Development of a tool wear-monitoring system for hard turning

Cited by 136 publications

References 7 publications

Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

Researches regarding cutting tool condition monitoring

Tool wear estimation using an analytic fuzzy classifier and support vector machines

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