The micro-machining of electrically inert, chemically inert, hard to machine materials like glass and ceramic, can be done efficiently by a hybrid non-conventional micro-machining technique, i.e., electro-chemical discharge machining (ECDM). All the experiments, in this present work, have been conducted by an indigenously designed and developed ECDM set up. The power circuit unit is designed in such a manner that it can operates on two different power supply mode, i.e., pulse DC power supply and pure DC power supply. The current research work examined the influence of pulsating DC and pure DC power supply on the erosion of tapered tool end made of stainless steel and cupper during micro-machining of ceramics. The authors have demonstrated the optical images of the machined tool tip end and the images of sparking in both mode of power supply unit during machining. The simulated and isotherm images of surface temperature for both types of power supply have also been carried out in COMSOL multi-physics 5.6. Apart from the factors like tool–work piece gap, and tool surface texture, concentration of electrolyte during micro-machining, the factors like nature of pulse wave supply, electrical and thermal conductivity of tool also have significant role in tool wear during machining. The experimental results and the simulated images reveal that the pulse DC power supply generates periodic direct current, resulting smooth and controlled heat flux generation, whereas the pure DC supply continuous direct current, leading to generation of uninterrupted heat flux. Due to this uninterrupted heat flux, more heat supply to the work-piece, hence the temperature of the tool surface increases and more material removed from the tool surface.

Electrochemical discharge machining (ECDM) is a novel non-conventional processing method that entails high-temperature melting and enhanced chemical etching facilitated by substantial electrical energy discharge. The present research article presents experimental findings on the effects of tool rotation and tool–workpiece (T–W) gap on the geometric characteristics of drill holes formed in a zirconia workpiece during the ECDM process. In addition, the influence of a one-micron-thick platinum-plated tool is also used to analyze the surface texture of the micro-hole formed in the ceramic. The effect of various process parameters is explored, including applied voltage range (90–110 V), electrolyte concentration (25%–35%), lower pulse frequencies (10–30 kHz), tool rotation speeds (10–50 rpm), and T–W gap (0–30 µm). The discharge-affected zone on the workpiece, due to sparking, and the surface topography of the machined zone have been studied using a scanning electron microscope. The difference in shape of the heat flux projected on the machining zone and the temperature distribution on the workpiece, with and without tool rotation, has been generated by the thermo-gun. The topographical study of the machined surface and the temperature distribution reveals the potential use of this indigenously designed and developed ECDM setup for machining on ceramics in the micron regime. Furthermore, the statistical analysis using the Taguchi L27 design, S/N ratio, analysis of variance, and regression modeling revealed that applied voltage is the most dominant parameter, contributing nearly 48% to material removal rate and 28% to radial overcut, with significant effects at a 95% confidence level.

In the era of rapid industrialization, sustainability is treated as an important strategy in supply chain operations in order to safeguard environmental and societal responsibility. However, the implementation of sustainability depends on multiple factors influenced by human behavior, geographical location, regulatory bodies, and the social and economic conditions of the region. Further, the influence of sustainability factors changes across industries due to changes in organizational culture, market competition and emerging technology. The current study presents a comparative study of the drivers and barriers in sustainable supply chain management (SSCM) among Indian manufacturing industries. The intuitionistic fuzzy decision making trial and evolution laboratory (IF-DEMATEL) method has been adopted to set up a structural model and identify the influential factors responsible for the sustainability implementation of different manufacturing industries. The bootstrap resampling technique is used to conduct the internal validation of the model. The findings reveal that top management commitment, government regulations, financial investment, and innovative technology have emerged as the most influential factors for the industry types considered in the study to adopt sustainability in their supply chain. These results provide valuable insights for managers and policymakers in fostering sustainability in supply chains, with implications for future strategies in the Indian context.

Although several past studies have addressed sustainable transportation issues within the supply chain, many critical real-world factors such as time-dependent vehicle speeds, actual route distances, and load-dependent engine efficiency have not been sufficiently addressed. In order to address these limitations, present study proposes a sustainable transportation network that simultaneously optimizes economic, environmental, and social objectives. A capacitated vehicle routing and scheduling problem is formulated, incorporating heterogeneous vehicles, hard time windows, and multiple time periods. An improved Ant Colony Optimization algorithm is developed with 2-optimal local search for enhanced route quality and convergence. Pareto-optimal solutions are ranked using the technique for order preference by similarity to ideal solution. Partial rank correlation coefficient and regression analysis are conducted to interpret variable influence, offering insights into input-output relationships. A real-world case study from a rural logistics company demonstrates the practical applicability and effectiveness of the proposed framework in solving complex sustainable transportation problems.

Rapid industrialization necessitates the utmost balance among economic, environmental, and social performance of manufacturing industries for long-term sustainability. The equity among the performance criteria can be maintained through the adoption of sustainability in the industries’ supply chain. As mining industry is viewed as one of the most polluting industries, it becomes apparent to rectify mining activities through the integration of sustainability into its supply chain. However, the sustainability drive must not compromise long-term economic performance. To address this issue, a case-based study is attempted in the present work to identify and analyze the drivers and barriers responsible for implementing sustainability in the supply chain of Indian mining industries. An integrated approach of the fuzzy analytical hierarchy process (F-AHP) and fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) has been proposed to determine the priority and interdependency between the sustainability implementation factors. Also, sensitivity analysis has been carried out to understand the factors significantly influencing system sustainability. The study reveals that “environmental certification and government regulation” and “lack of regulations on sustainability”’ are the most crucial driver and barrier, respectively, to adopt sustainability in the Indian mining industry. The findings help policy planners provide a framework for promoting sustainable practices. Also, the study provides a robust methodology that can be applied to similar industries interested in enhancing sustainability adoption.

Micro drilling on Ni-based superalloy is a challenging process due to the material properties, operating conditions, low thermal conductivity and high quality requirements. Incoloy 825 belongs to the family of Ni-based superalloy and is widely used in containers for acid production, pickling operations, nuclear fuel reprocessing, handling of radioactive wastes, chemical processing, oil and gas recovery. Mechanical micro drilling of these alloys has found application in the manufacture of cast turbine blades. The study focuses on the micro drilling of Incoloy 825 alloy under MQL (minimum quantity lubrication) cutting condition. In order to analyze micro drilling operation, an initial study has been undertaken aiming at investigating the influence of spindle speed (5000, 10000, 15000 and 20000 rpm) and feed (1, 2.5 and 4 mu m/rev) on thrust force and torque. The appropriate process parameters for 12 different through holes are used for each twist drill and pilot hole micro-drills. Constant depth of 1.25 mm and drill diameter of 400 mu m were considered throughout the experimentation. The effect of cutting parameters on entry and exit diameter of hole, taper angle of hole, circumferential deformation layer and white layer has been analysed. Comparative study has been carried out using twist drill and pilot hole micro-drills. It is concluded that pilot hole micro-drill is preferable for micro drilling and the optimal results in the form of superior dimensional accuracy, minimum circumferential deformation layer, minimum torque and reasonably less value of thrust force can be accomplished under the feed of 4 mu m/rev and spindle speed of 20000 rpm.

The current research investigates the machinability of a novel Nimonic alloy through electro-discharge machining by assessing Material Removal Rate, Tool Wear Rate and Surface Roughness. The machining was conducted using plain dielectric and Graphene Oxide (GO) nanoparticles (5 g/l) mixed dielectric considering both Copper and Brass electrode. The novelity lies when machining with nano GO mixed dielectric. It was observed that the use of Copper electrode in machining of the alloy in nano GO mixed dielectric results in superior quality machining, demonstrating enhanced performance. The key findings include the identification of optimal parameters, where V-g of 70 V, T-on of 200 mu s, F-p of 0.5 kgf/mm(2) maximize MRR (16.231 mm(3)/min) and minimize TWR (0.0062 mm(3)/min) and SR (5.1423 mu m). The microstructural study of the machined surface and sustainability study along with the detailed comparative analysis of responses assures the superiority of machining in Nano GO mixed dielectric-Cu electrode environment.

Nickel-based superalloys have profound application in area of aerospace, automobile industry, power generation industry and marine industry due to their thermal stability at elevated temperature, corrosion resistance and enhanced mechanical strength. The properties of the alloys mainly depend upon the microstructure of the alloys, and the microstructure can be altered by following proper heat treatment route. The paper investigates about the effect of heat treatment on microstructure and mechanical properties of the alloy. Moreover, as the alloys are mostly hot deformed in their real-time applications, so it is utmost important to know the hot deformation behavior of alloy at different temperatures and strain rates. In the current research, a novel Nimonic alloy was fabricated taking unique combination of elements (Ni:60

Nickel-based superalloys have been used in aero-engines for turbine blades, vanes and other engine components due to their excellent high-temperature properties, such as creep resistance and fatigue properties. Various aero-engine components like cooling holes in vanes, nozzle guide vanes, seal slots, blisks, fir tree slots etc. are being machined by EDM/w-EDM (wire Electrical Discharge Machining) in high aspect drilling and complex shape generation. Hence, research is needed to improve the productivity, surface integrity and process capability. Generally EDM and w-EDM are employed to generate fine holes and contour respectively. Recast layer formation and micro-cracks in HAZ (Heat Affected Zone) are the most common hazardous phenomenon visible in cooling holes. But Hybrid multi-axis w-EDM helps in minimising the width of HAZ in aeroengine components. It helps in formation of recast layers less than 0.0004 of an inch and surface roughness in submicron range up to 0.5 micron. High-speed w-EDM is recommended by ADMAP-GAS project for the finishing operation in manufacturing of fir tree slots as it doesn't leave a burr. Heat shield holes are efficiently machined through ultrasonic vibration-assisted EDM. Repeatability, shape adherence and process capability in EDM is found to be better than any other machining processes of aero parts.

A superalloy is a high-performance alloy used for achieving stability and strength at elevated temperatures. The present work aims at investigating heat treatment characteristics of a single crystal Nickel-based superalloy used in aero turbine blades in order to check any improvement in property at high temperatures. In the current work, the alloy is subjected to heat treatment at various temperatures such as 950 degrees C, 1000 degrees C, 1100 degrees C, 1200 degrees C and 1300 degrees C. It is found that as aging temperature increases the gamma' precipitates diffuse between themselves and form larger cuboidal gamma' (Gamma prime) structures in the superalloy matrix. The larger gamma' phase indicates higher creep strength of the superalloy. Again, through electrical discharge machining, process parameters such as voltage gap (V-g), pulse on time (T-on) and flushing pressure (F-p) are chosen to determine the tool wear rate (TWR), material removal rate (MRR) and surface roughness (SR) of the superalloy. RSM (response surface methodology) along with GRA (grey-relational analysis) is adopted in order to design a model and optimize the process parameters. It is observed that V-g has the most significant effect on the machining process as a whole. It is experimentally validated that V-g of 80 V, T-on of 100 mu s, and F-p of 0.5758 kg/mm(2) bear the optimal values for the multi-responses. The predicted optimized results of TWR, MRR, SR and grey-relational grade are well fitted with optimized experimental values having a percentage of error <5%.

Optimization of machining parameters is usually carried out to enhance the performance of the machining processes. Generally, the design of experiments approach and multi-response optimization methods are applied to obtain the best parametric setting of a process. However, these methods result in local optimal solutions because the search is limited to discrete points. Therefore, classical optimization methods and metaheuristics are extensively used to obtain the optimized machining condition. Cuckoo search (CS), teaching learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA) algorithms have proved their efficiency for optimizing machining processes. Despite outperforming evolutionary and swarm intelligence techniques on unconstrained benchmark functions, few studies have used a gravitational search algorithm (GSA) to obtain the best parametric condition in a machining process. Therefore, the present study attempts to quantify the performance of GSA and chaotic GSA (CGSA) while determining the best parameters for a machining process. The present study adopts different cost functions involved in turning, wire electrical discharge machining (WEDM), and plasma-enhanced chemical vapor deposition (PECVD). A comparison of results obtained with TLBO and PSO indicates the superiority of TLBO by maintaining better mean and standard deviation values. However, CGSA performs significantly better than GSA and PSO. Friedman's test ranks TLBO as the best algorithm, followed by CGSA, PSO, and GSA.

The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.

Environmental concerns around the world necessitate manufacturing firms to embrace new practices that may lessen the negative environmental effect. These days, green supply chain management (GSCM) is gaining more popularity in the manufacturing sector from material acquisition to product delivery to the customers. Adoption of GSCM by Indian manufacturing firms is comparatively slow due to a lack of identification of different barriers and drivers of GSCM. To this end, the present study aims at the identification of barriers and drivers of GSCM, typical to the Indian scenario. Individual groups of six barriers and eight drivers have been identified for this research by industry experts. In addition, the study prioritizes these enabling factors using multi-criteria decision-making (MCDM) processes such as the analytic hierarchy process (AHP) approach and the technique for order of preference by similarity to ideal solution (TOPSIS) approach. In order to handle the ambiguity in decision-making, the data have been extracted from the experts using linguistic terms. A case of an Indian paper manufacturing firm is considered for this study. The fuzzy AHP (F_AHP) model indicates financial implication and lack of awareness/participation in GSCM as first and last ranked barriers, respectively, whereas the fuzzy TOPSIS (F_TOPSIS) model indicates economic consideration and customer, market and societal pressure as first and last ranked drivers, respectively. Finally, to encapsulate the robustness of the final result, sensitivity analysis has been carried out. This research will undoubtedly assist policymakers in developing policies that will facilitate GSCM implementation.

Purpose Fused filament fabrication (FFF) is a type of additive manufacturing (AM) based on materials extrusion. It is the most widely practiced AM route, especially used for polymer-based rapid prototyping and customized product fabrication in relation to aerospace, automotive, architecture, consumer goods and medical applications. During FFF, part quality (surface finish, dimensional accuracy and static mechanical strength) is greatly influenced by several process parameters. The paper aims to study FFF parametric influence on aforesaid part quality aspects. In addition, dynamic analysis of the FFF part is carried out.Design/methodology/approach Interpretive structural modelling is attempted to articulate interrelationships that exist amongst FFF parameters. Next, a few specimens are fabricated using acrylonitrile butadiene styrene plastic at varied build orientation and build style. Effects of build orientation and build style on part's ultimate tensile strength, flexure strength along with width build time are studied. Prototype beams (of different thickness) are fabricated by varying build style. Instrumental impact hammer Modal analysis is performed on the cantilever beams (cantilever support) to obtain the natural frequencies (first mode). Parametric influence on natural frequencies is also studied.Findings Static mechanical properties (tensile and flexure strength) are greatly influenced by build style and build orientation. Natural frequency (NF) of prototype beams is highly influenced by the build style and beam thickness.Originality/value FFF built parts when subjected to application, may have to face a variety of external dynamic loads. If frequency of induced vibration (due to external force) matches with NF of the component part, resonance is incurred. To avoid occurrence of resonance, operational frequency (frequency of externally applied forces) must be lower/ higher than the NF. Because NF depends on mass and stiffness, and boundary conditions, FFF parts produced through varying build style may definitely correspond to varied NF. This aspect is explained in this work.

The gravitational search algorithm (GSA) is one of the most promising algorithm in the physics-based metaheuristics category. However, GSA suffers from premature convergence due to rapid reduction in diversity, whereas a chaotic gravitational search algorithm (CGSA) can degrade the convergence speed and exploitation power. To address these issues, the current study proposes an algorithm that enhances the exploration capability of GSA using a disruption strategy with chaotic dynamics. If no significant change is observed in diversity values during the initial stages of the search process, disruption is performed using a sigmoid function. Then, the search process executes gradual exploitation using a sigmoid function without chaotic dynamics. The proposed algorithm is tested with GSA, CGSA, and PSO (particle swarm optimization) on 28 benchmark functions. It is observed that the algorithm outperforms GSA and PSO in 19 cases and CGSA in 20 cases. Diversity analysis shows that the algorithm generates superior exploration versus exploitation percentage with improved mean diversity values. To determine its robustness, the algorithm is applied to four unconstrained engineering problems. The results suggest that the algorithm can solve practical engineering problems in a reasonable number of iterations.