A comparative study of non-parametric spectral estimators for application in machine vibration analysis

Najafi, Babak; Hakim, Hossein

doi:10.1016/0888-3270(92)90049-o

Cited by 20 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been predicted that an accurate and reliable tool condition monitoring (TCM) system could result in cutting speed increases of 10-50%, a reduction in downtime by allowing it to be scheduled in advance [7], and an overall increase in savings of between 10% and 40% [8]!…”

mentioning

confidence: 99%

State-of-the-art methods and results in tool condition monitoring: a review

Rehorn

Jiang

Orban³

2005

Int J Adv Manuf Technol

337

118

View full text Add to dashboard Cite

This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research.

show abstract

mentioning

confidence: 99%

State-of-the-art methods and results in tool condition monitoring: a review

Rehorn

Jiang

Orban³

2005

Int J Adv Manuf Technol

337

118

View full text Add to dashboard Cite

show abstract

“…Second, replacing tools through piece and time methods cannot accurately determine the service life of the tools, which may lead to unnecessary tool waste and, more significantly, affect the quality of the products. Developing tool wear prediction technology can avoid tool damage and other problems, as well as helping to improve tool chip speed and leading to substantial savings in production costs [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion

Wang,

et al. 2024

Sensors

View full text Add to dashboard Cite

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

show abstract