Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

Ong, Pauline; Lee, Woon Kiow; Lau, Raymond Jit Hoo

doi:10.1007/s00170-019-04020-6

Cited by 89 publications

(33 citation statements)

References 37 publications

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“…Multiple regression models were used to show the relationship between tool vibrations and tool wear. Ong et al [ 20 ] established a sensor system based on an artificial neural network and examined the effect of different levels of cutting speed, feed, depth of cut and machining time on surface roughness and tool wear. V B estimation was made using an artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

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Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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“…The second assumption is not necessary under the formulation that was given: any analytical law that describes the relationship between the covariate and the lifetime can be used. However, the right-hand side of Equation (13) must have a shape that is compatible with the comparison approach that was used on Equations (16) and (17).…”

Section: Analytical Expression Of the Mut And Data Transformationmentioning

confidence: 99%

“…The analytical developments using the logarithmic transformation lead from Equations (16) and (17) to respectively Equations (19) and (20), which can be analytically satisfied regardless the value of v c , and in practice through the fitting of the PH Model and the Weibull model to the survival baseline curve.…”

Section: Analytical Expression Of the Mut And Data Transformationmentioning

confidence: 99%

“…More recently, machine learning techniques were also presented to determine the tool wear evolution. For example, training artificial neural networks and variants in order to determine the tool wear from tool micrography [16,17], or to predict the tool wear from condition monitoring variables such as cutting forces [18]. F. W. Taylor presented a relationship between the lifetime of a cutting tool (T) and the cutting speed (v c ) [19], which has been extensively discussed and used in numerous studies [6,7,11,20,21].…”

Section: Introductionmentioning

confidence: 99%

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An Analytic Approach to the Cox Proportional Hazards Model for Estimating the Lifespan of Cutting Tools

Equeter¹,

Ducobu²,

Rivière-Lorphèvre³

et al. 2020

JMMP

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The machining industry raises an ever-growing concern for the significant cost of cutting tools in the production process of mechanical parts, with a focus on the replacement policy of these inserts. While an early maintenance induces lower tool return on investment, scraps and inherent costs stem from late replacement. The framework of this paper is the attempt to predict the tool inserts Mean Up Time, based solely on the value of a cutting parameter (the cutting speed in this particular turning application). More specifically, the use of the Cox Proportional Hazards (PH) Model for this prediction is demonstrated. The main contribution of this paper is the analytic approach that was conducted about the relevance on data transformation prior to using the Cox PH Model. It is shown that the logarithm of the cutting speed is analytically much more relevant in the prediction of the Mean Up Time through the Cox PH model than the raw cutting speed value. The paper also covers a numerical validation designed to show and discuss the benefits of this data transformation and the overall interest of the Cox PH model for the lifetime prognosis. This methodology, however, necessitates the knowledge of an analytical law linking the covariate to the Mean Up Time. It also shows how the necessary data for the numerical experiment was obtained through a gamma process simulating the degradation of cutting inserts. The results of this paper are expected to help manufacturers in the assessment of tool lifespan.

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“…Traditional tool wear monitoring methods are usually time-frequency analysis and multi-sensor fusion [7,8]. Among these various methods, vibration signals, cutting force signals and acoustic emission signals are widely used.…”

Section: Introductionmentioning

confidence: 99%

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Huang

et al. 2020

Sensors

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Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

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Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision

Cited by 89 publications

References 37 publications

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

An Analytic Approach to the Cox Proportional Hazards Model for Estimating the Lifespan of Cutting Tools

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

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