A Survey on Artificial Intelligence-Based Modeling Techniques for High Speed Milling Processes

Torabi, Amin J.; Er, Meng Joo; Li, Xiang; Lim, Baek Keun; Zhai, Lian-Yin; Oentaryo, Richard J.; Peen, Gan Oon; Żurada, Jacek M.

doi:10.1109/jsyst.2013.2282479

Cited by 23 publications

(12 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the available clustering techniques, K-means, K-medoids, Gath-Geva, and FCM clustering techniques are investigated in our research [2], [3].…”

Section: Application Of Clustering Methods On Wavelet Featuresmentioning

confidence: 99%

“…The deliverables of these works were often demonstrated in the form of mathematical models or presented in the form of AI-based models, [2], [3]. A common goal was to accomplish better surface quality and an extended tool life.…”

mentioning

confidence: 99%

“…Hence, in our proposed model, the robustness of this layer to the tool replacement will protect the model from remarkable changes in the structure. Knowledge on the complexity of milling signal, time, and frequency features is used in many investigations [3], [9], [10]. In some studies, these features are incorporated with some other parameters from cutting conditions to make the model comprehend the tool wear and changes in the quality of the surface while the process is running.…”

mentioning

confidence: 99%

“…Time-frequency transformations such as the CWT and the DWT were also applied as feature extraction for milling signals [3]. It is reported that the distribution of energy patterns in the wavelet scales of the cutting signals is changing according to the gradual change in the tool condition [3], [10]- [13].…”

mentioning

confidence: 99%

“…It is reported that the distribution of energy patterns in the wavelet scales of the cutting signals is changing according to the gradual change in the tool condition [3], [10]- [13]. Usually, time-frequency features consist of high-dimensional vectors of data that can complicate the computations.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes

Torabi

et al. 2016

IEEE Systems Journal

Self Cite

View full text Add to dashboard Cite

Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.

show abstract

“…Among the available clustering techniques, K-means, K-medoids, Gath-Geva, and FCM clustering techniques are investigated in our research [2], [3].…”

Section: Application Of Clustering Methods On Wavelet Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations