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How tool and equipment condition affect quality dimension in production?

Anomaly detection

Laser Doppler vibrometer

Condition monitoring

Condition-based maintenance

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Tool and equipment condition significantly impacts the quality and dimensional accuracy of production in manufacturing processes. Monitoring tool wear and breakage is crucial to prevent sudden failures, reduce operating costs, and avoid damage to workpieces. Various methods like vibration monitoring, acoustic emission analysis, and vision systems are employed to assess tool conditions and detect faults in equipment. For instance, vibration signatures are analyzed to identify causes of inaccuracies in machining processes, with increased vibration levels often indicating potential failures. Implementing condition-based maintenance strategies using technologies like Mel-frequency cepstral coefficients and support vector regression can effectively monitor tool wear and predict remaining useful life, ensuring high-quality production with minimal downtime.

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Proceedings Article•DOI Tool condition monitoring based on mel-frequency cepstral coefficients and support vector regression Tarak Benkedjouh, Noureddine Zerhouni, Said Rechak - Show less +2 more 01 Oct 2017 3 Citations	Tool condition monitoring using MFCC and SVR helps assess tool wear, ensuring high-quality production by maintaining equipment in operational condition, reducing machine downtime, and improving reliability.
Open access An investigation of tool condition monitoring Pratesh Jayaswal, Nidhi Gupta - Show less +1 more 01 Jan 2012 2 Citations	Tool condition monitoring through vibration analysis helps detect faults in machine tools, influencing dimensional accuracy and surface finish in production by revealing causes of inaccuracy and faulty components.
Open access 환경영향을 최소화한 비전 시스템을 이용한 미세공구의 상태 감시 기술 김선호, 백운보 - Show less +1 more 01 Oct 2012 2 Citations	Tool conditions directly impact product quality and manufacturing productivity. Monitoring tools using vision systems minimizes external influences, enhancing micro machining quality with small diameter tools.
Open access•Journal Article•DOI Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling 01 Jan 2023-Journal of Advanced Mechanical Design Systems and Manufacturing	Tool condition monitoring using acoustic emission signals in small hole drilling helps prevent sudden tool failure, ensuring dimensional accuracy and surface quality in production processes.
Proceedings Article•DOI LabVIEW based tool condition monitoring and control for CNC lathe based on parameter analysis Sanket N. Bhagat, Sanjay L. Nalbalwar - Show less +1 more 20 May 2016 7 Citations	Tool condition directly impacts surface finish and part dimensions in production. Monitoring tool wear is crucial for maintaining quality and reliability in manufacturing processes.

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How does tool condition monitoring works?5 answersTool condition monitoring works by collecting sensor signals during the machining process and analyzing them to determine the state of the tool. Various methods are used for this purpose, including deep learning-based approaches and signal processing techniques. In deep learning-based methods, acoustic signals are collected using industrial microphones, and deep feature extraction and pattern recognition are performed using multi-scale convolutional recurrent neural networks. Signal processing techniques involve preprocessing the sensor data, extracting time domain and frequency domain features, and using machine learning models such as extreme random trees and relevance vector machines for tool wear monitoring. Additionally, a TCM-U2PL model, consisting of a teacher model and a student model, has been proposed to adaptively extract tool condition features and improve model performance using unlabeled data. Another approach involves analyzing acoustic emission signals emitted during the machining process using deep feature distribution modeling and anomaly detection methods.

What different methods are used in tool condition monitoring?4 answersDifferent methods used in tool condition monitoring include deep learning-based methods, acoustic emission (AE) signal analysis, and fractal analysis of spindle electric current signals. Deep learning-based methods involve using industrial microphones to collect acoustic signals during machining, extracting sensitive information using central fractal decomposition, and using multi-scale convolutional recurrent neural networks for deep feature extraction and pattern recognition. AE signal analysis involves analyzing the AE signals emitted during drilling processes using methods such as continuous wavelet transform (CWT) and anomaly detection based on deep feature distribution modeling (DDM) and convolutional autoencoders (CAE). Fractal analysis of spindle electric current signals is another approach, where fractal parameters are used to extract characteristic features of the signal for tool wear classification.

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