Development of Deep Belief Network for Tool Faults Recognition

Abstract

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.

Investigating the Detection Capability of Acoustic Emission Monitoring to Identify Imperfections Produced by the Metal Active Gas (MAG) Welding Process

“…There are a few sources, such as Madhvacharyula et al [30], Kanungo et al [31], and Kale et al [32], which have started reporting machine learning techniques applied to detected AE sources that can indicate welding defects. Moreover, Kanungo et al's work [31] used cluster k-means analysis to show the more significant features from several AE parameters, namely peak amplitude, kurtosis, energy, and the number of counts.…”

“…However, specific to the setup in terms of material inserts and MAG processes, the authors of this paper believe this to be a first. Kale et al [32] used a number of machine learning techniques to separate the anomalies, which is promising considering that future work associated with this research is intended to pursue future ideas, correlating automatically welding defects using AE sensors (both contact and none contact) along with DSP to discriminate between an acceptable and non-acceptable weld. Work presented in a paper by Pietrzakand Wolkiewicz [34] identified that STFT analysis of vibration signals allowed for the differentiation between a good machine cutting tool and machine cutting tools with five different faulty conditions.…”

“…Similar to this work, refs. [32,34] used deep belief networks to differentiate between different states. Vibration-extracted signals use similar technologies to AE, which again reinforces why STFTs should be used over other techniques.…”

Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

“…During the training step, some undirected weights and biases exist between the visible and hidden layers. Energy function is used for defining the joint distribution function of each layer as follows 35 : where and represent the i th visible and j th hidden layers binary state, and defines the partition function attained by the probable pair’s total for the layers.…”

Indentation Reverse Algorithm of Mechanical Response for Elastoplastic Coatings Based on LSTM Deep Learning

“…In light of the aforementioned challenges in the analysis process, machine learning methods are more advantageous than FE simulation at handling nonlinear relationships of data. Machine learning techniques are utilized in industrial manufacturing due to their outstanding capabilities for data analysis [ 14 , 15 ]. The machine learning approach has gained popularity in material inspection analysis because of its low development cost, short development cycle, and excellent predictive performance when dealing with large amounts of data [ 16 ].…”