In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

The resources of the earth are being consumed day by day with the increasing population and necessities of humankind in many areas, such as industrial applications and basic needs in houses, workplaces and transportation. As a consequence, careful usage of the energy sources and the conversed energy is of great importance in order to obtain sustainable development. Machining operations have a large percentage of all manufacturing methods in terms of depleted energy which gives them a high potential for reducing the total energy consumption. The approaches handled in the literature for the minimization of the consumed energy in the machining industry were considered in this study. While several machinability characteristics under different machining processes were investigated broadly in the context of composites and superalloys, the comparison of these systems has been given cursory attention in the current literature, specifically for cutting energy saving. The overall performance of these group material systems utilizing widely in numerous significant industrial areas supplies important signs about manufacturing costs, service conditions and environmental impacts. It is highly crucial to monitor the indicators of energy-saving phenomena of the machined parts since the mechanisms behind the energy consumption of these systems is very complex and dynamic owing to different process-induced variables. This well-organized review paper distinguishes itself from previous studies in this field since the comprehensive literature survey paves the way for diverse approaches that regard energy saving, especially for composites and superalloys under different machining operations. This overview paper aims to contribute to the current literature by highlighting the effects of the state-of-the-art approaches in reducing energy consumption in the machining of industrially important materials. This study can also establish a framework in the context of the process-property interactions to comprehend the influence of energy-saving mechanisms through machining in a system of interest.

With the advent of industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like data analytics, cloud computing, Internet of things, machine learning (ML), and artificial intelligence. 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 the effective use of the cutting tool and reduce maintenance times. This article presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data are collected using an in-house designed and developed data acquisition (DAQ) module setup on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter that gives the best model was found out to be “linear,” achieving an accuracy of 93.3%. This study 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 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.

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.