Aluminium 6061 (Al6061) is a widely used material for various industrial applications due to low density and high strength. Nevertheless, the conventional machining operations are not the best choice for the machining purposes. Therefore, amongst all the non-conventional machining operations, electric discharge machining (EDM) is opted to carry out the research due to its wide ability to cut the materials. But the high electrode wear rate (EWR) and high dimensional inaccuracy or overcut (OC) of EDM limit its usage. Consequently, nanopowder is added to the dielectric medium to address the abovementioned issues. Nanopowder mixed EDM (NPMEDM) process is a complex process in terms of performance predictability for different materials. Similarly, the interactions between the process parameters such as peak current (I_p), spark voltage (S_v), pulse on time (P_on) and powder concentration (C_p) in dielectric enhance the parametric sensitivity. In addition, the cryogenic treatment (CT) of electrodes makes the process complex limiting conventional simulation approaches for modelling inter-relationships. An alternative approach requires experimental exploration and systematic investigation to model EWR and overcutting problems of EDM. Thus, artificial neural networks (ANNs) are used for predictive modelling of the process which are integrated with multi-objective genetic algorithm (MOGA) for parametric optimization. The approach uses experimental data based on response surface methodology (RSM) design of experiments. Moreover, the process physics is thoroughly discussed with parametric effect analysis supported with evidence of microscopic images, scanning electron microscopy (SEM) and 3D surface topographic images. Based on multi-dimensional optimization results, the NT brass electrode showed an improvement of 65.02% in EWR and 59.73% in OC using deionized water. However, CT brass electrode showed 78.41% reduction in EWR and 67.79% improved dimensional accuracy in deionized water. In addition to that, CT brass electrode gave 27.69% less EWR and 81.40% improved OC in deionized water compared to kerosene oil.

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Val_ext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (C_p) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (S_c). However, the highest % significance in SR is given by S_c (36.86 %), followed by C_p (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.

In the era of industry 4.0, digitalization and smart operation of industrial systems contribute to higher productivity, improved quality, and efficient resource utilization for industrial operations and processes. However, artificial intelligence (AI)–based modelling and optimization analysis following a generic analysis framework is lacking in literature for the manufacturing sector thereby impeding the inclusion of AI for its potential application's domain. Herein, a comprehensive and generic analysis framework is presented depicting the key stages involved for carrying out the AI-based modelling and optimization analysis for the manufacturing system. The suggested AI framework is put into practice on wire electric discharge machining (WEDM) system, and the cutting speed of WEDM is adjusted for the stainless cladding steel material. Artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) are three AI modelling techniques that are trained with meticulous hyperparameter tuning. A better-performing model is chosen once the trained AI models have undergone the external validation test to investigate their prediction performance. The sensitivity analysis on the developed AI model is performed and it is found that pulse on time (P_on) is the noteworthy factor affecting the cutting speed of WEDM having the percentage significance value of 26.6 followed by the D_w and LTSS, with the percentage significance value of 17.3 and 16.7 respectively. The parametric optimization incorporating the AI model is conducted and the results pertain to the cutting speed are 27.3% higher than the maximum value of cutting speed achieved for WEDM. The cutting speed performance optimization is realized following the proposed AI-based analysis framework that can be applied, in general, to other manufacturing systems therefore unlocking the potential of AI to contribute to industry 4.0 for the smart operation of manufacturing systems.

Titanium and its alloys are belonging to the class of biomedical materials and have amazing characteristic features, including high corrosion protection, incredible wear resistance, superb hardness, and extraordinary bio-degradability and activity. Due to these remarkable qualities, titanium materials are considerably treated in medical applications like bone implantation, surgical devices and prosthesis, dental implants, and fracture bone fixation by screws, plates, nails and abutments. However, there is a property of titanium alloys that it holds layers of oxygen over the surface, but the atmospheric reactions/changes at extremely high temperature alter the morphology of the films, resulting in the loss of biological performance. In order to cover up this barrier, surface treatments are performed through different mechanical, chemical, and physical means. The treatments over the surface enhance the micro-features, improve the biocompatibility, and extend the life of coatings on titanium substrate. These modifications allow the various titanium alloys to be principally used in medical and dental applications. Considering the significance and applications of these alloys, this article is mainly conducted to scrutinize the various aspects, that is, biomedical applications and surface modification techniques, of titanium-based materials.