Journal of Advanced Manufacturing Systems

This paper demonstrates an exclusive design methodology in Cellular Manufacturing (CM) considering machine usage percentage as ratio data. This research correctly emphasized the fundamental of ratio data and proposed a novel and precise mathematical formulation of the design problem. This multi-objective model carefully optimizes the total exceptional utilization (TEU), number of voids and total cell utilization (TCU). Due to the novelty in the model, a new data generation technique is proposed. The test datasets are obtained and tested using IBM CPLEX tool successfully. The contribution of this research is twofold. First, the ratio data concept is correctly emphasized and a precise mathematical model is developed. Second, since the model is new and datasets are not readily available, therefore a dedicated data generation model is proposed.

Traveling Wire Electro-Chemical Spark Machining (TW-ECSM) process is a new innovative thermal erosion-based machining process suitable for cutting electrically nonconductive materials using tool electrode in the form of wire. This article attempts experimental modeling of TW-ECSM process using a hybrid methodology comprising Taguchi methodology (TM) and response surface methodology (RSM). The experiments were carried out on borosilicate glass using L${}_{27}$ orthogonal array (OA) considering the input parameters like applied voltage, pulse on-time, pulse off-time, electrolyte concentration and wire feed velocity along with process performances such as material removal rate (MRR), surface roughness (R${}_{\text{a}})$ and kerf width (K${}_{\mathit{\text{w}}})$. The interaction influence of input parameters on process performances was also discussed. Further, multi-objective optimization (MOO) of response performances of TW-ECSM process is executed using a coupled approach of grey relational analysis (GRA) and principal component analysis (PCA). The optimal process parameter setting illustrates the improvement of MRR by 171%, diminution of R_a and K${}_{\mathit{\text{w}}}$ by 27% and 8% against the initial parameter settings. Moreover, irregular cutting of kerf width and surface characteristics were also scrutinized using scanning electron microscope (SEM).

This research paper recognizes the association between corporate social responsibility (CSR) practices and firm reputation. The researcher collected the data from 239 fast moving consumer goods enterprises located in the industrial area of Kuala Lumpur, Malaysia. The CSR practices are measured by recycling of products, green purchasing, environmental awareness/protection programs, and direct support to the community. This research adopts a simultaneous regression technique to test hypotheses. The results show that CSR practices have a strong positive relationship with enterprise reputation. Meanwhile, direct support to community and environmental protection programs have a greater positive effect on firms’ reputation as compared to green purchasing and recycling of products. In addition, CSR practices arouse sympathy in consumers’ minds, which translates into repeat buying of products.

E-jet is a novel microfabrication technology of producing high-resolution patterns for various application areas like printed electronics, biotechnology, etc. Judicious selection of the operating scenario can improve the quality of the fabrication performance of E-jet. The objective of this study is to experimentally evaluate different operating scenarios of the E-jet microfabrication process while considering the deposited droplet diameter and droplet ejection frequency as performance characteristics simultaneously. Experimentations were carried out on the developed E-jet setup according to the design of experiment technique considering nozzle stand-off height, applied voltage, and ink flow rate as process control parameters. The effect of each control parameter on the process response is investigated. The relative weight values of each performance characteristic or response variable are determined by principal component analysis, which makes the weight evaluation procedure more rigorous and eliminates the dependence on the practitioner’s judgment. A hybrid grey relational grade analysis and technique for order preference by similarity to ideal solution methodology is employed to evaluate the optimal operating scenario of E-jet. Both methodologies indicated a similar desirable operating condition for E-jet. Moreover, the variance study called analysis of variance is employed to discover the pattern in which the control parameters affect the fabrication process. The variance study suggests that the ink flow rate is the most dominant parameter in the experimental domain.

This research work explores the feasibility of drilling micro-holes on Ti6Al4V by applying a sequential electro-micro-machining process. A novel sequential combination of micro-electrochemical discharge machining ($\mu$ECDM) and micro-electro-chemical machining ($\mu$ECM) is applied for drilling micro-hole in titanium alloy (Ti6Al4V). The machining of titanium alloy by a $\mu$ECDM process is a challenging task and not reported yet. Therefore, the feasibility of machining of Ti6Al4V by $\mu$ECDM using various combinations of electrolytes has been studied. The best-suited electrolyte for drilling by $\mu$ECDM was selected by analyzing the hole depth, radial overcut, hole taper angle and the minimum time required to drill through holes in a 400$\mu$m thick sheet of titanium alloy. The $\mu$ECDM process produces micro-holes rapidly but it consists of recast layer, micro-cracks, and heat affected zone, etc. The surface characteristics of a $\mu$ECDMed hole were improved by applying $\mu$ECM process subsequent to the $\mu$ECDM process. The sequential combination of $\mu$ECDM shaping and $\mu$ECM finishing results in improved dimensional accuracy, machining depth, taper angle, and surface quality of the hole produced by sequential micro-machining process.

In this paper, a finite element analysis (FEA) has been used for the design of horn with different Bezier profiles for rotary ultrasonic machine (RUM). First, for validation, the results are compared with available literature. FEA-based MATLAB code is developed for finding the stress components, axial amplitude, and resonance frequency over the domain of horn with different Bezier profiles. In domain of horn with different Bezier profiles, the component of stresses is studied and found that the stress generated is within the endurance strength of the selected material. The amplification of the proposed horns is 24%, and 18% higher than the traditional linear Bezier horn with same length and diameters of surface end. For RUM, effect of inertia (self-weight or gravity of horn) on domain has been studied for horn with different Bezier profiles using FEA. The effect of tool shape and size on the stress distribution for horn have also been studied.

In this work, microwave-processed similar and dissimilar joints of SS304-SS304, SS304-SS316 and SS316-SS316 were fabricated using microwave hybrid heating technique. Workpieces were projected with electromagnetic energy radiations of a household microwave source with fixed frequency of 2.45GHz at 800W; about 99.8% pure Ni powder of different sizes 70nm, 20$\mu$m and 50$\mu$m was used as interface material between the welded joints. The experiments were carried out as per Taguchi philosophy of optimization-based L₉ orthogonal array. Workpiece materials (SS304-SS304, SS304-SS316, SS316-SS316), interface material (70nm, 20$\mu$m, 50$\mu$m) and process time (360, 390, 420s) were selected as three input process parameters. Physical characterization of the joints was performed through energy dispersive spectroscopy to find out the central elements by weight % present in the joining region of the joints, and the results were taken as output parameters. Optimization of elemental weight % in microwave-processed joints of SS304/SS316 was the prime output of this present work with investigation to the existence of Ni, Fe, Cr and C elements in the produced joints under the influence of different input parameters through microwave joining method.

One of the classical models for inventory control is economic production quantity (EPQ), which is widely used in industry. In this paper, an EPQ model with partial shortage is developed by considering the real world conditions, and costs related to the backorder demand are taken as fixed and time-dependent. In the proposed model, determination of the inventory cycle length, the length of positive inventory cycle and backordered demand rate are considered in shortage period. The aim of the presented research is to minimize the total inventory costs and the space required for storage products so that the stochastic and classic constraints including holding costs, lost sales, backorder, budget, total number of productions and average shortage times should be satisfied while optimizing the multi-objective problem. Presented model is a bi-objective nonlinear programming model. Then, to solve the proposed model, three multi-objective decision-making methods including Lp-metric, goal programming and goal attainment are used. Besides, numerical examples are executed in small, medium and large scales by use of GAMS software, and the performance of the methods is compared in terms of objective functions and required CPU time. Finally, sensitivity analysis is done to determine the effect of change in the main parameters of the model on the objective function value.

High Carbon High Chromium (or AISI D2) Steels, owing to the fine surface finish they produce upon grinding, find lot of applications in die casting. Machining parameters affect the surface finish significantly during the grinding operation. In this context, this work puts an effort to arrive at the optimum machining parameters relating to fine surface finish with minimum cutting force. The material removal caused by the abrasive grinding wheel makes the process a very complex and nonlinear machining operation. In many situations, traditional optimization techniques fail to provide realistic optimum conditions because of the associated complexity. In order to overcome this issue, particle swarm optimization (PSO) coupled with artificial neural network (ANN) is applied in this research work for parameter optimization with the objective of achieving minimum surface roughness and cutting force. The machining parameters selected for the investigation were table speed, cross feed and depth of cut and the responses were surface roughness and cutting force. ANNs, inspired from biological neural networks, are well capable of providing patterns, which are too complex in behavior. The ANN model developed was used as the fitness function for PSO to complete the optimization. Optimization was also carried out using conventional response surface methodology-genetic algorithm (RSM-GA) approach in which regression equation developed with RSM was considered as the fitness function for GA. Confirmatory experiments were conducted and the comparison showed that PSO coupled with ANN is a reliable tool for complex optimization problems.