Supervision of Milling Tool Inserts Using Conventional and Artificial Intelligence Approach: A Review

Due to continuous cutting tool usage,tool supervision is essential for improving the metal cutting industry.In the metal removal process tool,supervision is carried out either by an operator or online tool supervision.Tool super-vision helps to understand tool condition,dimensional accuracy,and surface superiority.For downtime in the metal cutting industry,the main reasons are tool breakage and excessive wear,so it is necessary to supervise tool which gives better tool life and enhance productivity.This paper presents different conventional and artificial intelligence techniques for tool supervision in the processing procedures that have been depicted in writing.

Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road.Therefore,monitoring the condition of the brake components is inevitable.The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches.The vibration signals were captured using an accelerometer sensor under a various fault condition.The acquired vibration signals were processed for extracting meaningful information as features.The condition of the brake system can be predicted using a feature based machine learning approach through the extracted features.This study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and wavelets.Comparative results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.

Brake Fault Diagnosis Through Machine Learning Approaches–A Review

Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. To identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis areused in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

The regulation of tyre pressure is treated as a significant aspect of‘tyre maintenance’in the domain of autotronics.The manual supervision of a tyre pressure is typically an ignored task by most of the users.The existing instru-mental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires reg-ular manual conduct.Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition.In this perspective,the Machine Learning(ML)approach acts appropriate as it exhi-bits comparison of specific performance in the past with present,intended for predicting the same in near future.The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle.In order to examine the vibration response of a wheel hub,the in-house design&development of DAQ(Data Acquisition System)is described.Micro Electro-Mechanical Scheme(MEMS)built accelerometer is incorporated with open source hardware and software to collect and store the data.This framework is easy to develop,monitor and can be retrofitted in two wheeled vehicle.For various pressure conditions,the change in response of wheel hub vibration with respect to time is collected.The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters.The classification of different conditions of tyre pressure is carried out using ML classifiers.

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools.Any variation in tool state affects the quality of a finished job and causes disturbances.So,a tool monitoring scheme(TMS)for categorization and supervision of failures has become the utmost priority.To respond,traditional TMS followed by the machine learning(ML)analysis is advocated in this paper.Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation.In the current study,investigation on the single point cutting tool is carried out while turning a stainless steel(SS)workpeice on the manual lathe trainer.The vibrations developed during this activity are examined for failure-free and various failure states of a tool.The statistical modeling is then incorporated to trace vital signs from vibration signals.The multiple-binary-rule-based model for categorization is designed using the decision tree.Lastly,various tree-based algorithms are used for the categorization of tool conditions.The Random Forest offered the highest classification accuracy,i.e.,92.6%.