A Study on Chatter Prediction in High-Speed End Milling Process by Fuzzy Neural Netgwork

Hino, Junichi; Su, Chuanxin; Yoshimura, Teizo

doi:10.1299/jsmec.44.825

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Extensive research efforts have been devoted so far to develop sensors and methodologies for detection of chatter [12][13][14][15], and identification of chip forms [19,20]. It is already known that sensing cutting force signals from the cutting process is one of the most promising methods, and researches have been carried out from various points of views [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…It is known that the generation of chatter affects mostly the main force component [17,21], while the feed force component is most sensitive to chip breaking [18]. The artificial neural networks and clustering techniques have been employed by many researches [15,20]; however, the preliminary experiments for specific cases or different cutting conditions are always required to set up the database and train the system [15,20]. A simplified method for recognizing the cutting states has been proposed by utilizing the dynamic components of cutting forces [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent identification of turning process based on pattern recognition of cutting states

Tangjitsitcharoen

Moriwaki

2007

Journal of Materials Processing Technology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent identification of turning process based on pattern recognition of cutting states

Tangjitsitcharoen

Moriwaki

2007

Journal of Materials Processing Technology

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“…Thus, it is important to observe the size of the FNN when pruning. A reasonable size of the FNN is obtained using the Vapnik-Chervonenkis (VC) dimension (9) , which is the measure of the generalization capacity for neural networks (21), (22) . The upper and lower bounds of the VC dimension are defined as…”

Section: Pruningmentioning

confidence: 99%

“…However, many problems still must be solved, such as the determination of the stable region of high-speed milling. Many researchers have studied chatter vibration in mechanical machining (1) - (9) . Merritt analyzed the mechanism of selfexcited machine tool chatter in turning and developed a chatter theory.…”

Section: Introductionmentioning

confidence: 99%

Chatter Prediction in End Milling by FNN Model with Pruning

Hino

Okubo

Yoshimura

2006

JSME Int. J., Ser. C

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This paper is concerned with a study of chatter prediction in high-speed end milling operations. Chatter vibration occurring in mechanical machining gives rise to poor surface finish and dimensional inaccuracy in machined parts, reduction of tool life, and even damages machine tools. Various studies of its prediction and avoidance have been carried out over the last several decades. The purpose of this study is to develop an expert system for predicting chatter vibrations in high-speed end milling using wavelet transform and fuzzy neural network models with pruning. The FNN model employed here uses a pruning process which reduces a neural network to its most effective size. The amount of learning for convergence of a pruned network is reduced in comparison with an initial network. The proposed method is applied to a jig grinding machine, and the results demonstrate the effectiveness of the chatter prediction procedure.

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“…The cutting force signals have been carried out from the other points of views [8][9][10]. The analysis of cutting force, has been presented in recent papers to detect cutting states [11,12]. The artificial neutral networks and clustering techniques have been employed by many researches but the preliminary experiments for specific cases are always required to train the system [13].…”

Section: Introductionmentioning

confidence: 99%

In-Process Chatter Detection in Surface Grinding

Tangjitsitcharoen

Senjuntichai

2015

MATEC Web of Conferences

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Abstract. The chatter causes the poor surface finish during the surface grinding. It is therefore necessary to monitor the chatter during the process. Hence, this research has proposed the in-process chatter detection in the surface grinding process by utilizing the dynamic cutting forces. The ratios of the average variances of three dynamic cutting forces have been adopted and applied to identify the chatter during the surface grinding process to eliminate the effects of the cutting conditions. The effects of the cutting conditions on the chatter are also studied and analyzed. The algorithm has been proposed to detect the chatter regardless of the cutting conditions. The verification of the proposed system has been proved through another experiment by using the new cutting conditions. The experimental results have run satisfaction. It is understood that the chatter can be avoided during the in-process surface grinding even though the cutting conditions are changed.

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A Study on Chatter Prediction in High-Speed End Milling Process by Fuzzy Neural Netgwork

Cited by 4 publications

References 7 publications

Intelligent identification of turning process based on pattern recognition of cutting states

Intelligent identification of turning process based on pattern recognition of cutting states

Chatter Prediction in End Milling by FNN Model with Pruning

In-Process Chatter Detection in Surface Grinding

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