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The cutting tool condition drives the economy of machining processes in manufacturing industry. The failures in cutting tool are unbearable and affect the drive of machine tool which reduces life. Hence it necessitates reducing power consumption using monitoring cutting tool condition and hence requires an efficient supervision to monitor and predict faults. Simply stated, the condition which curtails cutting tool life highlighted before it turns into a tool wear, breakage and failure. This ensures optimized and effective use of a cutting tool, saves maintenance/repair time, enhances constancy in a process etc. The recent development in Machine Learning (ML) and its applicability for condition monitoring approach has drawn attention of researchers. Machine learning examines existing and past indications to predict conditions in future. This paper presents machine learning based condition monitoring of milling cutter of vertical machining centre (VMC). The vibration signals acquisition of 4 insert milling cutter is carried out with healthy and various fault conditions. The Visual Basic (VB) code and script is used to extract statistical features and decision tree algorithm is used to select relevant features. The different conditions of milling cutter are classified using tree family classifiers. The effort made in this work is to check applicability of ML approach for milling cutter fault diagnosis for reducing power consumption of drive of machine tool.

With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.

The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.