Multimilling-Insert Wear Assessment Using Non-Linear Virtual Sensor, Time-Frequency Distribution and Neural Networks

Li, C. James; Tzeng, Tzong-Chyi

doi:10.1006/mssp.1999.1282

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…The quantitative methods were developed with consideration of a single objective, such as minimization of cost or maximization of profit. For the process of the single objective optimization, several different techniques were proposed, such as the differential calculus [10,11], regression analysis [12,13], geometric and stochastic programming [14], genetic algorithm [15], computer simulating [16] and modeling with ANNs [17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

Aykut

Gölcü

Semiz

et al. 2007

Journal of Materials Processing Technology

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Section: Introductionmentioning

confidence: 99%

Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

Aykut

Gölcü

Semiz

et al. 2007

Journal of Materials Processing Technology

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“…Experiments were carried out by machining Inconel 718, which is a “difficult-to-cut material”. Since there is a high rate of tool wear for the machining of Inconel 718, many studies 25–30 were conducted on the optimization of machining parameters for extended tool life. In this work, a systematic procedure was followed in the milling of Inconel 718 using a particular combination of cutting parameters.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…6 Several different TCM algorithms or methods were proposed in the context of sensor techniques and decision mechanisms. 4–30 These methods can be categorized into two groups.…”

Section: Introductionmentioning

confidence: 99%

“…27 In milling processes, wear progression can be different 28 and sensor placement is more complicated owing to the rotation of the milling tools. 29 Numerous machine learning algorithms, such as widely used artificial neural networks (ANN), and fuzzy expert system have already been investigated 7,10–12,15–29 for tool wear prediction. In addition to these schemes, support vector machines (SVMs) are also an effective method used in the decision making components of TCM systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm

Kaya

Oysu

Ertunç

et al. 2012

Proceedings of the Institution of Mechanical Engineers, Part B:

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In machining systems, the quality of the manufactured part is directly related to the condition of the tool used. Sharp tools are mostly used on the final machining pass to obtain enhanced dimensional accuracy and surface smoothness. Worn tools on the other hand are typically used for coarse machining. The operator usually makes tool assignments based on his experience, the wear levels of the tools and the type of machining task. However, this kind of operator judgment is bound to errors and may not be reliable in processes requiring high precision. Therefore, a tool condition monitoring system is highly desirable to achieve the best results in machining quality. In this study, three-axis cutting forces, torque, three-axis accelerometer and acoustic emission signals were analyzed and used for the development of an online tool condition monitoring system. Various time domain and statistical features extracted from these signals were used to train support vector machine models in a binary decision tree, which was used to predict the condition of the cutting tool. The genetic algorithm was employed for reducing the dimensionality of the feature set by selecting the features that correlates best with the tool condition. Nine experiments were carried out at different cutting conditions. Experimental results demonstrated the efficacy of the proposed scheme. The classification rates for the tool condition monitoring system before and after inclusion of the genetic algorithm step were determined as 89% and 100%, respectively.

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“…It has been investigated for active noise and vibration control (Petersen et al 2008), industrial process control (Cheng et al 2004), building operation optimization (Ploennigs et al 2011), lead-through robot programming (Ragaglia et al 2016), product quality of hydrodesulfurization (HDS) (Shokri et al 2015), and tool condition monitoring (Bustillo et al 2011;Li and Tzeng 2000). Data-driven virtual sensing techniques are favorable by fusing the extracted features from noisy online measurements to infer the difficult-tomeasure parameters based on artificial intelligence models (Gelman et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

et al. 2017

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Vibration and oil debris analysis are widely used in gearbox condition monitoring as the typical indirect and direct sensing techniques. However, they have their own advantages and disadvantages. To better utilize the sensing information and overcome its shortcomings, this paper presents a virtual sensing technique based on artificial intelligence by fusing low-cost online vibration measurements to derive a gearbox condition indictor, and its performance is comparable to the costly offline oil debris measurements. Firstly, the representative features are extracted from the noisy vibration measurements to characterize the gearbox degradation conditions. However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process. A new nonlinear feature selection and fusion method, named kernel factor analysis, is proposed to mitigate the aforementioned challenge. Then the virtual sensing model is constructed by incorporating the fused vibration features and offline oil debris measurements based on support vector regression. The developed virtual sensing technique is experimentally evaluated in spiral bevel gear wear tests, and the results show that the developed kernel factor analysis method outperforms the state-of-the-art feature selection techniques in terms of virtual sensing model accuracy.

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Multimilling-Insert Wear Assessment Using Non-Linear Virtual Sensor, Time-Frequency Distribution and Neural Networks

Cited by 11 publications

References 35 publications

Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

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