Vibration-based fault diagnosis of a face milling tool using empirical mode decomposition features and artificial neural network

Madhusudana, C K; Kumar, Harish; Narendranath, S.

doi:10.1784/204764219826793802

Cited by 3 publications

(1 citation statement)

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“…The vibrations can be reduced by balancing rotatory elements, decreasing the clearance in guide-ways, securing support for the job, the proper relief in sharp tools, good cutting velocity for the cutter and workpiece, using the proper coolant/cutting lubricant, cutting at higher speeds with smaller cutters or using solid carbide cutters with randomized flutes, etc. [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Belief Network for Tool Faults Recognition

Kale

Wahul

Patange³

et al. 2023

Sensors

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The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.

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Section: Introductionmentioning

confidence: 99%