Injection moulding (IM) is a processing technique produced from polymeric products. Warpage defect (WD) is the defect that generally occurs during the IM process due to the inappropriate processing parameters of the melt temperature, mould surface temperature, packing pressure, injection pressure, and packing pressure time. This paper investigates the IM parameters that influence product warpage by combining the simulation, analysis of variance, signal-to-noise analysis, and Taguchi method. The simulation process was performed by Moldflow software. The product material is high-density polyethylene. The WD has been predicted and optimized to enhance product quality. Melt temperature and packing pressure time are the factors that acrimoniously influenced the warpage of the product. The results show that the packing pressure time and melt temperature have the highest effects on the WD by the contributions of 48.94% and 37.48%, respectively. The optimal IM parameters are scanned again with the WD abated at about 1.2%. The mathematical formula has been constructed to predict the WD with the reflection of acceptable values of 86.29%. The research hopes that the results have been applied to designing and fabricating the plastic product in the near future.

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

Stainless steel (SS) and Titanium alloy (Ti) are the most commonly used materials in many industrial fields such as the automotive and aerospace industry. Stainless steel has good corrosion resistance and titanium alloy has an extremely lightweight characteristic. The combination of both materials has become a tremendous innovation in the industrial sector. Resistance spot welding which has commonly applied in many industrial fields is a good consideration to join these two dissimilar materials due to the high efficiency that could be achieved by using this method. However, the way of joining these dissimilar materials should be carefully considered due to the significant difference in mechanical properties between SS and Ti. In the present study, 3 mm of SS316L and Ti6Al4V sheets were joint under the resistance spot welding method with an aluminum interlayer. The optimized welding parameters were provided under the Taguchi method L9 orthogonal array along with the mechanical properties’ investigation. The optimum welding parameters were 11 kA of weld current, 30 Cycles of welding time, and 5 kN of electrode force which produced 8.83 kN tensile-shear load of the joint. The mechanical structure analysis shows the different morphology between stainless steel and titanium interfaces and the intermetallic compound layer was formed on the SS/Al and Al/Ti interfaces. The EDX analysis shows the atomic diffusion-reaction on the application of aluminum as an interlayer on the SS/Ti joint.

Hub networks play a crucial role in optimizing transportation costs in air and road systems. Their main objective is to strategically locate hubs and allocate non-hub nodes within the network. The modular hub location problem is a specific area of hub network design that focuses on accurately calculating transportation costs, considering factors like trip numbers and capacity constraints in network routes. This study proposes a mixed-integer programming model to address the modular hub location problem with multiple allocations. It considers dependent and independent costs associated with vehicles per trip between hub network routes, considering specific vehicle capacities. Two datasets are utilized for validation: the CAB dataset representing 25 nodes of US airports and the TR dataset representing the Turkish transportation system with 81 nodes. To tackle the NP-hard nature of hub location models and the computational complexity of the proposed model, two solutions are developed. Firstly, a novel LP relaxation-based method using GAMS software provides near-optimal solutions for medium-sized instances. Additionally, a Genetic Algorithm (GA) implemented in MATLAB handles larger instances. The GA's efficiency is enhanced by tuning its parameters using the Taguchi method. Results analysis shows that both proposed algorithms yield high-quality solutions within significantly reduced timeframes compared to the CPLEX solver in GAMS software. The LP relaxation-based method performs well for medium-sized instances, while the GA approach is efficient for larger instances after parameter tuning with the Taguchi method.

Turning experiments were carried out on AA 7075/SiC composite workpiece in dry and spray cooling environments based on L16 Taguchi design of experiments. Multiple performance optimization of process parameters was performed using grey relational analysis. The performance characteristics considered were average surface roughness, cutting tool temperature and material removal rate. Uncoated carbide inserts were used for machining the workpiece in a high speed precision lathe. A grey relational grade obtained from grey relational analysis was used to optimize the process parameters. Optimal combination of process parameters was then determined by the Taguchi method using the grey relational grade as the performance index. Experimental results indicated that the turning in spray cooling environment was beneficial compared to that in dry environment for the quality response characteristics under consideration. Analysis of variance showed that feed was the most significant parameter for the multiple performance characteristics during turning in both the environments.

This paper shows that the Taguchi method of optimization can be easily used in optimizing the visual display unit (VDU) parameters for maximizing the readability on the laptop. Researchers use five factors, each at three levels which are font size, font style, line spacing, viewing distance and screen inclination angle with reading time in seconds (for reading about 300 words) as the response variable. Other parameters like text/background color combination (black/white), table height, illumination etc. were kept constant. Experiments were conducted by varying different parameters on the basis of L27 orthogonal array design. Results show that there is a 15.3% decrease in mean reading time when working at optimal condition. Results also show that factor A is the most important factor which is font size followed by factor C (line spacing), factor E (screen inclination angle), factor D (viewing distance) and factor B (the font style) which is the least important factor.

In the present work, a multi-response optimization method is used to optimize the machining parameters in turning of glass fiber reinforced polymer (GFRP) composites. Parameters like spindle speed (N), feed rate (f) and depth of cut (d) are taken to obtain the responses such as surface roughness (Ra) and material removal rate (MRR). Taguchi’s L9 orthogonal array has been used for machining the work-piece. Analysis of variance (ANOVA) has been carried out to check the significant process parameter in a single objective performance characteristic. The multiple performance characteristics have been analysed using Grey relational analysis and an appreciable result has been reported with this approach.

In recent years, efforts have been made to produce advanced composite materials in order to lessen environmental impact and to extent sustainability. Traditional materials are largely substituted by composites due to their greater properties like flexural strength, low thermal expansion and high strength. Numerous studies are present that show the process of composite materials reinforcement with natural fiber to improve mechanical and thermal properties. The vital aspect of exploitation of natural fiber in composites is associated with biodegradability. An extensive range of different natural fibers has been used for reinforcement till now. In present work, mechanical properties of jute fiber reinforced epoxy and polyester composites manufactured using Taguchi optimization method are investigated, experimentally. It was found that jute reinforced epoxy composite had better mechanical properties than jute polyester composite. Also, Epoxy- jute composite had lower erosion wear rate than polyester jute composites.

This paper discusses the application of the Taguchi method to optimize the machining parameters for machining of GFRP composite in drilling for individual responses such as thrust force and delamination factor. Moreover, a multi-response performance characteristic is used for optimization of process parameters with application of grey relational analysis. An orthogonal array (L9), grey relational generation, grey relational coefficient and grey – fuzzy grade obtained from the grey relational analysis applied as performance index to solve the optimization problem of drilling parameters for GFRP composite. Taguchi orthogonal array, the signal-to-noise ratio, and the analysis of variance are used to investigate the optimal levels of cutting parameters. The confirmation tests are conducted to verify the results and it is observed that grey-fuzzy approach is efficient in determining the optimal cutting parameters.

This study presents optimization of performance characteristics in unidirectional glass fiber reinforced plastic composites using Taguchi method and Grey relational analysis. Performance characteristics such as surface roughness and material removal rate are optimized during rough cutting operation. Process parameters including tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment and depth of cut are investigated using mixed L18 orthogonal array. Grey relation analysis is used to optimize the parameters and Principal Component Analysis is used to find the relative significance of performance characteristics. Depth of cut is the factor, which has great influence on surface roughness and material removal rate, followed by feed rate. The percentage contribution of depth of cut is 54.399% and feed rate is 5.355%.