Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

Zhou, Yuqing; Sun, Bin; Sun, Weifang; Lei, Zhi

doi:10.1007/s10845-020-01663-1

Cited by 63 publications

(33 citation statements)

References 50 publications

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“…Step 2: Time domain, frequency domain, and time − frequency domain features are extracted from the original collected signal according to the definitions given in Table 1 [40,41], in which thee time − frequency domain features are the energy coefficients of the original collected signal by the ensemble empirical mode decomposition (EEMD) [42,43].…”

Section: Tool Wear Estimation Methods Based On the Gapso-enhanced Elmmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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Section: Tool Wear Estimation Methods Based On the Gapso-enhanced Elmmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

Self Cite

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“…Especially in the field of machining authors have penalized ML models for predicting wear states in the last decades. They focus on predicting wear-related quality features of machined parts [27,28], quantifying wear of machining tools (Shen et al 2020;Zhou et al 2020) or predicting tool life [31]. In contrast, ML models for blanking processes are mainly used to describe discrete fault states, while the prediction and quantification of wear conditions is hardly found in recent literature.…”

Section: Data-driven Monitoring Of Blanking Processesmentioning

confidence: 99%

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

2021

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In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.

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“…Tool wear is considered to be a key factor that dominates the surface quality and also a critical index to fulfill the accuracy requirements during the machining process [5]. Tool wear monitoring or estimating is usually divided into direct monitoring and indirect monitoring [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

Yu¹,

Wang

Zhang³

et al. 2021

Mathematical Problems in Engineering

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Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

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Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

Cited by 63 publications

References 50 publications

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

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