Robust supply chain network design that considers supply resiliency, plays vital role in supply chain risk management in dealing with various operational and disruption risks. This study developed a novel three-stage decision approach to consider two echelons robust and resilient supply chain networks. We present a mixed-integer non-linear programming model with two objective functions. The objectives are maximization of SCN profit and maximization of resiliency, where robustness, agility, leanness, flexibility, and integrity can be defined as the five resiliency criteria. Fuzzy Simultaneous Evaluation of Criteria and Alternatives (FSECA) and Simple Multi-Attribute Rating technique (SMART) have been used to obtain the supplier resiliency and weighted importance of resilience criteria. Then, a robust optimization model is built based on uncertainty parameters considering supplier resiliency. A Non-dominated Sorting Genetic Algorithm (NSGAII) and Multi Objective Particle Swarm optimization (MOPSO) were used to solve the robust model on a large scale. parameters calibrated by the Taguchi method and five metrics of performance evaluation were considered to compare the meta-heuristic algorithms. We demonstrate the proposed NSGAII algorithm over a competing method based on five performance metrics. The research findings reveal the optimal level of robust supply chain networks based on algorithm performance and Taguchi analyses. Moreover, the results indicate that when profit increases, resilience can increase simultaneously.

In the current decade, a number of industries have moved their attention towards emerging sustainable technologies in order to better support socio-economic and environmental considerations. The present research investigates a unique hybrid composite developed by the amalgamation of natural kenaf-coir fibers, with resin of epoxy, incorporated with titanium carbide (TiC) nanoparticles. This study also presents the development process involved in manufacturing the composites, along with mechanical testing and optimization of these composite samples. The nanofillers of TiC are utilized in wt. percentages of 0%, 3%, 4%, and 5%, while coir and kenaf fibers are incorporated at 0%, 3%, 4%, and 5% by weight, and the thickness of the samples is varied at 2, 3, 4, and 5mm. The mechanical attributes of composites are evaluated using a vacuum bag molding process, with subsequent testing and optimization performed through Taguchi and ANOVA analysis to discover the optimal sample combination. The findings indicate that the most effective composite formulation includes 4% TiC, 5% kenaf, 5% coir, and a thickness of 3 mm, which provides the highest tensile modulus and strength among all tested samples. The integration of kenaf fibers with coir fibers and TiCs as fillers significantly improves the tensile and flexural attributes of the hybrid composite in contrast to composites made with coir or kenaf fibers alone.

Recently, there has been an increase in the use of laser beam welding of mild steel in various industries, including petroleum refineries, power plants, pharmaceuticals, and even residential areas. This research paper focuses on studying the effects of laser welding process parameters, such as laser power and welding speed, on the tensile strength of welds. To do this, three types of laser beam oscillations (sinusoidal path, triangular path, and square path) were performed to weld 125mm x 60 and 1.8 thick sheets of ASTM A36 mild steel alloy. The researchers used statistical tools such as ANOVA to generate mathematical models and experimental designs using the Taguchi method. The results indicate that the optimal welded joint has good mechanical properties after laser welding. For ASTM A36 mild steel, the optimal parameters for laser welding are a laser power of 1800 W, a welding speed of 50 mm/s, and triangular welding mode.

The main goal of this research is to compare the various optimization strategies (Response Surface Methodology, Taguchi, and Teaching Learning Based Optimization) for orthogonal turning of Hard to Machine materials. The workpiece material in this work is Ti6Al4V alloys. After selecting cutting speeds in the High-Speed Machining range, orthogonal turning tests are performed on the material for a specific combination of machining parameters – Depth of Cut, Cutting Speed, and, Feed Rate. A Lathe Tool Dynamometer is used to record the cutting forces from the trials. After combining Johnson Cook Material and Damage models, a comprehensive Finite Element Model is created to model the Orthogonal Turning of Ti6Al4V alloys. Experiments conducted previously validate the developed model. Three different strategies, namely RSM, Taguchi, and TLBO, were used to optimise machining parameters for minimal Cutting Force. The approaches are compared for the best combination of machining parameters and the best Cutting Force value. Analysis of Variance is used to study the impact of machining factors on Cutting Force.

Silicon carbide (SiC) particulate impregnated Al 7075 matrix composite was fabricated by stir casting method and then heat treated to T6 condition. It was then machined with multiple layer of TiN coated tungsten carbide (WC) inserts in dry environment and pollution free Spray Impingement Cooling (SIC) environment to compare the machining performance. SIC environment presented better machining performance with respect to cutting tool temperature (T), average roughness of the machined surface (Ra) and tool flank wear (VBc). Quadratic response surface models were developed by computing the experimental data. Weighted Principal Component Analysis (WPCA) based Taguchi technique was adopted to optimize the multiple responses simultaneously, which resulted 40 m/min of cutting speed (V), 0.05 mm/rev of feed (f) and 0.2 mm of cutting depth (d) was the optimal combination of process parameters.

Despite its well-reported application in a few welding processes, the use of reinforcing powders in weld joints to improve weld integrity has not garnered ample research attention for Gas Metal Arc Welding (GMAW) process. In this study, the adoption of Titanium alloy powders as metallic reinforcement for mild steel butt welds was investigated. By adopting Taguchi’s L4 orthogonal array, process optimisation for titanium-reinforced mild steel butt welds were first carried out. In the second phase of welding, the optimum parameters were used to create and compare two sets of weldments; one set was reinforced with titanium alloy powder and the other set left unreinforced. It was observed that in the Weld Metal (WM) region, the titanium-reinforced samples had higher micro-hardness values than their unreinforced counterparts with an average of 285.62 HV and 211.6 HV respectively. However, there was no substantial improvement in the ultimate tensile strength of the mild steel butt welds due to titanium powder reinforcements. Interestingly, the formation of acicular ferrite microstructure was more prevalent in the titanium-reinforced weldments and this was attributed to the presence of titanium inclusions in the weld metal. This prevalence of acicular ferrite suggests improved toughness properties in the weld joint region. While the higher hardness values in the Weld Metal of the reinforced sample indicates improved wear resistance.

This work is focused on the influence of different friction stir welding (FSW) parameters on AA6082 T-6 and AA5083-O alloys welding quality, by using Taguchi, Grey Relational and Weight Method. Four welding parameters were investigated, namely tool rotation speed (TRS), welding speed (WS), tool pin profile (TPP) and shoulder diameter (SD). The optimized setting of these input parameters was investigated so that weld parts quality could be optimized. Analysis of variance (ANOVA) was used to investigate the effects of these welding process parameters on response variables, viz. elongation (EL) and ultimate tensile strength (UTS). Single response optimization was carried using Taguchi Technique while grey relational analysis (GRA) was used for simultaneous optimization of two responses. Once the optimal settings of control factors were identified, confirmation experiments were performed for the validation of results. In the multi-response optimization, TRS was found to have the maximum effect (57.9%), followed by WS, SD and TPP. Weight method was applied for providing the priority to the response (i.e. EL and UTS). The response with higher priority presented a weight equal to 0.7, while the lower priority given corresponds to a weight of 0.3.

Machining of hardened work materials with appropriate levels of process parameters is still a burning issue in manufacturing sectors and challenging. It is because of pressing demand of surface quality which adversely affected by evolution of tool wear. Therefore the present investigation is undertaken to make a decision on parametric optimization of multi-responses such as flank wear and surface roughness during machining hardened AISI 52100 steel (55±1) steel using mixed ceramic insert under dry environment through grey relational analysis combined with Taguchi approach. Also predicted mathematical models of 1st and 2nd order have been developed for responses and checked for its accuracy. Second order mathematical model presented higher R2 value and represents best fit of the model and adequate compared to first order model. Model indicates good correlations between the experimental and predicted results. The proposed grey-based Taguchi methodology has been proved to be efficient for solving multi-attribute decision making problem as a case study in hard machining environment.

The objective of the study is to assess the performance of multilayer coated carbide insert in the machining of hardened AISI D2 steel (53 HRC) using Taguchi design of experiment. The experiment was designed based on Taguchi L27 orthogonal array to predict surface roughness. The S/N ratio and optimum parametric condition are analysed. The analysis of variance has also been carried out to predict the significant factors affecting surface roughness. Based on Taguchi S/N ratio and ANOVA, feed is the most influencing parameter for surface roughness followed by cutting speed whereas depth of cut has least significant from the experiments. In regression model, the value of R2 being 0.98 indicates that 98 % of the total variations are explained by the model. It indicates that the developed model can be effectively used to predict the surface roughness on the machining of D2 steel with 95% confidence intervals.

The present study focused on the Taguchi experimental design technique of Friction Stir Welds of dissimilar aluminum alloys (AA2024-T6 and AA6351-T6) for tensile properties. Effect of process parameters, rotational speed, Traverse speed and axial force, on tensile strength was evaluated. Optimized welding conditions for maximize tensile strength were estimated in order to improve the productivity, weld quality. Non-linear regression mathematical model was developed to correlate the process parameters to tensile strength. The results were verified by conducting the confirmation tests at identified optimum conditions.