The emerging demands of industry for developing the novel materials with superior mechanical properties have successfully resulted in the development of distinct materials such as Al-matrix composites. Among these composites, newly developed Al6061-7.5% SiC holds promising mechanical characteristics. But, the SiC reinforcement in the Al-matrix makes the machining of this composite challenging, thus posing a serious concern regarding its effective utilization. In this research, high-speed wire electric discharge machining (WEDM) was employed for the precise machining of a squeeze casted Al6061-7.5% SiC composite. The cutting performance of the WEDM was assessed in terms of roughness (S_R), cutting rate (C_s) and kerf width (K_W). Experimentation was performed according to the response surface methodology. The experimental findings were thoroughly investigated using statistical, optical and scanning electron microscopic (SEM) analyses. It has been revealed that the voltage is most influential/contributing parameter (having a percentage contribution of 25%) for controlling theS_Rduring WEDM of Al6061-7.5% SiC composite, whereas for theC_SandK_W, pulse and current are the major contributing control variables with percentage contributions of 90% and 84%, respectively. At low magnitude of both current and voltages, the surface quality is improved up to 33.3%. The SEM and optical microscopic evidences reveal shallow craters, small size melt re-deposits and micro globules on the machined surface at lower settings of both the said variables. Contrarily, for achieving higher cutting speed, high values of current and voltage along with low pulse are deemed essential. In case ofK_W, low magnitude of current and voltage along with smaller pulse yields 20% reduction in the kerf width. The analyses revealed the conflicting nature of the studied output responses (S_R,C_sandK_W). Therefore, multi-objective genetic algorithm (MOGA) was used to find a parametric combination. The best combination of WEDM input parameters found is current = 3 A, voltage = 84.999 V and pulse = 10 mu. This combination gives a minimumS_Rof 5.775 μm with aK_Wof 0.3111 mm at aC_Sof 5.885 mm/min. The suitability of the MOGA-proposed parametric combination was witnessed through confirmation trials. Furthermore, the parametric effects have also been mathematically quantified with respect to the defined machinability parameters.

Machining operations are very common for the production of auto parts, i.e., connecting rods, crankshafts, etc. In machining, the use of cutting oil is very necessary, but it leads to higher machining costs and environmental problems. About 17% of the cost of any product is associated with cutting fluid, and about 80% of skin diseases are due to mist and fumes generated by cutting oils. Environmental legislation and operators’ safety demand the minimal use of cutting fluid and proper disposal of used cutting oil. The disposal cost is huge, about two times higher than the machining cost. To improve occupational health and safety and the reduction of product costs, companies are moving towards sustainable manufacturing. Therefore, this review article emphasizes the sustainable machining aspects of steel by employing techniques that require the minimal use of cutting oils, i.e., minimum quantity lubrication, and other efficient techniques like cryogenic cooling, dry cutting, solid lubricants, air/vapor/gas cooling, and cryogenic treatment. Cryogenic treatment on tools and the use of vegetable oils or biodegradable oils instead of mineral oils are used as primary techniques to enhance the overall part quality, which leads to longer tool life with no negative impacts on the environment. To further help the manufacturing community in progressing towards industry 4.0 and obtaining net-zero emissions, in this paper, we present a comprehensive review of the recent, state of the art sustainable techniques used for machining steel materials/components by which the industry can massively improve their product quality and production.

Titanium alloys, especially Ti-6Al-4V, which is considered a difficult-to-cut material, bears numerous applications in aerospace and biomedical sectors. The criticality of the accurate formation of the machined cavity for the said applications and properties of Ti-6Al-4V accentuated the use of electric discharge machining (EDM). However, the issues of lower material removal rate (MRR) and tool wear (TWR) discouraged the use of EDM. These inherent issues hold a pivotal role regarding the sustainable machining of Ti-alloy. Therefore, in this research the potentiality of kerosene-based dielectric, having graphene nanoparticles, is comprehensively examined for the sustainable EDM of Ti-6Al-4V, which was not focused upon yet. Experimentation was performed under Taguchi’s design (L18) with three types of electrodes, namely Aluminum, Brass and Copper. In total, 36 experiments were conducted, of which 18 were with graphene-mixed dielectric and the remaining were with kerosene. Experimental results reveal that the brass electrode with negative tool polarity yields higher MRR for both types of dielectrics. The maximum MRR (7.602 mm³/min) achieved with graphene mixed dielectric is 64.5% greater as compared to that obtained with kerosene (4.621 mm³/min). Moreover, the minimum TWR obtained for graphene-based dielectric, i.e., 0.17 mg/min is approximately 1.5 times less than that achieved with kerosene.

Stainless steel (SS 304) is commonly employed in industrial applications due to its considerable corrosion resistance, thermal resistance, and ductility. Most of its intended applications require the formation of complex profiles, which justify the use of wire electrical discharge machining (WEDM). However, its high thermal resistance imposes a limitation on acquiring adequate surface topography because of the high surface tension of the melt pool, which leads to the formation of spherical modules; ultimately, this compromises the surface quality. Furthermore, the stochastic nature of the process makes it difficult to optimize its performance, especially if more than one conflicting response is involved, such as high cutting speed with low surface roughness and kerf width. Therefore, this study aimed to comprehensively investigate the interaction of SS 304 and WEDM, with a prior focus on simultaneously optimizing all the conflicting responses using the Taguchi-based grey relational approach. Analysis of variance (ANOVA) revealed that the current was the most significant parameter for cutting speed and kerf, whereas roughness, voltage (45%), drum speed (25.8%), and nozzle offset distance (~21%) were major contributing factors. SEM micrographs showed that optimal settings not only ensured simultaneous optimization of the conflicting responses but also reduced the number and size of spherical modules.

The collagen hydrolysate, a proteinic biopeptide, is used for various key functionalities in humans and animals. Numerous reviews explained either individually or a few of following aspects: types, processes, properties, and applications. In the recent developments, various biological, biochemical, and biomedical functionalities are achieved in five aspects: process, type, species, disease, receptors. The receptors are rarely addressed in the past which are an essential stimulus to activate various biomedical and biological activities in the metabolic system of humans and animals. Furthermore, a systematic segregation of the recent developments regarding the five main aspects is not yet reported. This review presents various biological, biochemical, and biomedical functionalities achieved for each of the beforementioned five aspects using a systematic approach. The review proposes a novel three-level hierarchy that aims to associate a specific functionality to a particular aspect and its subcategory. The hierarchy also highlights various key research novelties in a categorical manner that will contribute to future research.

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.