Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection

Li, X.; Er, Meng Joo; Lim, Baek Keun; Zhou, Jian; Gan, Oon Peen; Rutkowski, Leszek

doi:10.1142/s0129065710002498

Cited by 59 publications

(12 citation statements)

References 67 publications

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“…Since the RMS value of frequency bands in an AE signal varies with the variations in tool conditions, it is applied as an indicator of tool wear and surface roughness [20].…”

Section: Application Of Clustering Methods On Wavelet Featuresmentioning

confidence: 99%

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

Torabi

et al. 2016

IEEE Systems Journal

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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.

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“…Since the RMS value of frequency bands in an AE signal varies with the variations in tool conditions, it is applied as an indicator of tool wear and surface roughness [20].…”

Section: Application Of Clustering Methods On Wavelet Featuresmentioning

confidence: 99%

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

Torabi

et al. 2016

IEEE Systems Journal

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“…In a seminal book, Adeli and Hung (1995) advocated and presented the synergistic integration of the three areas of computation intelligence: NN, FL, and GA and showed how such a multiparadigm approach can help solve complicated pattern recognition problems such as face recognition and engineering design. Since then many authors have followed their multiparadigm approach, but the great majority have focused on integration of just two, such as FL and NNs (Gonzalez‐Olvera et al, 2010; Li et al, 2010; Scherer, 2010; Theodoridis et al, 2010; Wang et al, 2010; Freitag et al, 2011) or FL and evolutionary computing (Iglesias et al, 2010; Patrinos et al, 2010). This work proposes an innovative model, called FALCON‐COST, for estimating semiconductor hookup construction project costs (Wang et al, 2012) using NN, FL, and GA. To systematically deal with a cost‐estimating environment involving limited and uncertain data, this proposed model integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy GA (fmGA), and three‐point cost estimation method.…”

Section: Introductionmentioning

confidence: 99%

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Hsiao

Wang

et al. 2012

Computer aided Civil Eng

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Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.

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“…Artificial neural networks [7][8][9] with their excellent learning algorithms such as Refs. 10 and 11 and fuzzy inference systems 1,12 with their knowledge representation ability are two commonly employed machine learning techniques for such problems.…”

Section: Introductionmentioning

confidence: 99%

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

Subramanian

Sundaram

2012

Int. J. Neur. Syst.

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We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

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Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection

Cited by 59 publications

References 67 publications

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

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

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Human Action Recognition Using Meta-Cognitive Neuro-Fuzzy Inference System

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