Tool breakage diagnosis in face milling by support vector machine

Hsueh, Yao-Wen; Yang, Chan-Yun

doi:10.1016/j.jmatprotec.2008.01.033

Cited by 35 publications

(15 citation statements)

References 18 publications

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“…Neural networks were also used for an intelligent prediction of milling strategies particularly in commercially available CAD/CAM systems [8]. Regarding tool wear estimation and tool breakage detection, Dong et al [9] used the Bayesian multilayer perceptrons and Bayesian support vector machines for tool wear estimation, while Hsueh and Yang [10] used the support vector machines (SVM) methodology for tool breakage detection in modeling the face milling process precisely. Čuš and Župerl developed a system for monitoring tool condition in real time based on a neural decision system and Adaptive Neuro-Fuzzy Inference System (ANFIS) [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Influence of Cutting Parameters on the Surface Roughness, Tool Wear and Cutting Force in Face Milling in Off-Line Process Control

Bajić¹,

Celent²,

Jozić³

2012

SV-JME

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Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on surface roughness, tool wear and cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define a model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have a relative prediction error below 10%. The research has shown that when the training dataset is small neural network modeling methodologies are comparable with regression analysis methodology and can even offer better results, in which case an average relative error of 3.35%. Advantages of off-line process control which utilizes process models by using these two modeling methodologies are explained in theory.

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Section: Introductionmentioning

confidence: 99%

Modeling of the Influence of Cutting Parameters on the Surface Roughness, Tool Wear and Cutting Force in Face Milling in Off-Line Process Control

Bajić¹,

Celent²,

Jozić³

2012

SV-JME

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“…SVM- and SVR-based artificial intelligence have been documented in various engineering disciplines. 37,38…”

Section: Methodsmentioning

confidence: 99%

Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

Mondal

Mandal

Dey

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Burrs are undesirable materials beyond the work piece surface during drilling or other machining processes, thus this should be as less as possible during manufacturing. The experimental study has been conducted according to the full factorial design method. A total of 27 experiments were conducted by drilling on an Aluminum 6061T6 plate by choosing three factors and three levels of process parameters like drill diameter, point angle and spindle speed. In this research article, two predictive models, namely, adaptive neuro-fuzzy inference system and support vector regression, are developed using experimental data to estimate burr height and burr thickness. Then, these predictive models have been used to find out optimum process parameters for minimum burr height and burr thickness using genetic algorithm. It has been found that both the models are able to predict burr size and thickness with good accuracy, while the adaptive neuro-fuzzy inference system performs better than support vector regression.

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“…Application of SVM exist in fault localization, although it is not as common as BN and ANN [25]. The technique was used by Hsueg & Yang [26] to diagnose tool breakage fault in a face milling process under varying cutting conditions. Kumar et al [27] created a MapReduce framework for automatic diagnosis for cloud based manufacturing using SVM as the classification algorithm and validated this with a case study of fault diagnosis using the steel plate manufacturing data available on UCI Machine Learning Repository [28].…”

Section: Support Vector Machinementioning

confidence: 99%

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Ademujimi

Brundage

Prabhu

2017

IFIP Advances in Information and Communication Technology

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Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

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Tool breakage diagnosis in face milling by support vector machine

Cited by 35 publications

References 18 publications

Modeling of the Influence of Cutting Parameters on the Surface Roughness, Tool Wear and Cutting Force in Face Milling in Off-Line Process Control

Modeling of the Influence of Cutting Parameters on the Surface Roughness, Tool Wear and Cutting Force in Face Milling in Off-Line Process Control

Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

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