Journal of Advanced Manufacturing Systems

Analysis of surface roughness, temperature and hardness of the finished surface is beneficial to retain the desired surface finish of the workpiece via the magnetic abrasive finishing (MAF) process. In this work, “change in roughness” ($\mathrm{\Delta }$Ra), “raise in temperature” ($\mathrm{\Delta }T$) and “change in hardness” ($\mathrm{\Delta }H$) were opted for the multi-objective meta-heuristic optimization to minimize their impact on the finished surface of mild steel. Taguchi $L_9$ orthogonal array was used to obtain the experimental data on mild steel. The desirability fitness function (DFF) was developed to convert multi-responses to a single response. Finally, particle swarm optimization (PSO) was used for a higher-level decision-making and a meta-heuristic optimization approach, i.e. desirability function analysis-based PSO (PSO-DFA), was developed to obtain the best performance condition for surface finish. The best optimal setting obtained by using PSO-DFA included a working gap of 1.5mm, an abrasive weight of 15g, a voltage of 6V and a rotational speed of 50rpm. This setting has been selected based on the highest PSO-DFA value predicted by the meta-heuristic optimization approach and improved by 38.20% in comparison with DFA, that shows a satisfactory performance.

The present research was focussed to investigate the influence of liquid-gaseous dielectric on debris formation in magnetic field-assisted electrical discharge machining. The air and argon gas were used to make two-phase dielectric fluids such as liquid-air and liquid-argon gas, respectively. Experimentation was conducted to compare the effect of different liquid-gaseous dielectric environments on debris formation. The morphology and diameter distribution of debris were analyzed. In addition, the effect on elemental analysis, phase transformation and magnetic property of debris was investigated. The experimental results showed the exothermic reaction due to air from liquid–air dielectric, and inert property of argon gas from liquid-gaseous dielectric affected the formation of debris. The XRD results confirmed the formation of oxides in the debris produced using liquid-air mixed dielectric. The oxide-free debris formed while using the argon gas. Low saturation of magnetization was found in the debris, formed in the liquid–air dielectric which showed the weak attraction toward magnet when compared with that in the liquid–argon gas mixed dielectric.

Metal cutting processes are associated with excessive forces, friction and heat generation due to continuous intensive contact between the active cutting tools and the work which degrades the machined surface quality. The hardened tool steel having excellent wear resistance property receives an extensive promotion, investigation and application in the die manufacturing industries. In this research, the machinability of hardened AISI D3 tool steel has been investigated using TiN-coated Al₂${\text{O}}_3+\mathrm{\text{Ti}}$(C,N) ceramic tool inserts as per Taguchi’s ${\text{L}}_{25}$ orthogonal design of experiments is in horizontal turning under the dry condition. The experimental dataset was used for the regression model development of primary process outputs as well as mean cutting force using the response surface methodology (RSM) which was found to be significant. The parametric interaction effects on each process output were studied along with with chip morphology in detail. The cutting force as well as material removal rate was found to be significantly influenced by depth of cut, whereas machined surface quality was primarily dependent on tool feed rate. The wider with less prominent saw tooth chips got changed to narrower saw-tooth form with intensive shear bands at higher feed rates. Finally, an attempt has been made to develop an optimal parametric setting using particle swarm optimization technique to achieve minimum surface roughness and maximum material removal rate at less cutting force. The optimal parametric setting was high cutting speed (308m/min), high tool feed rate (0.08mm/rev) with medium depth of cut (0.6mm) to achieve each objective. This evolutionary particle swarm optimization technique was found to be highly accurate (within 8% error) as per validation experiment.

This paper develops a new harmony search (HS) algorithm for the hybrid flow shop scheduling problem with multiprocessor tasks, which is known to be NP-hard in the strong sense. The goal is to minimize the makespan. Thus, the proposed HS starts with the application of an opposition based learning (OBL) method to increase the diversity level of the initial harmony memory (HM). Furthermore, we use novel improvisation rules of producing new harmonies based on crossover and mutation operators. Also, neighborhood techniques are employed to enhance the searching and improve the solution quality. The proposed HS is compared with three algorithms from the literature. The computational experiments demonstrate the effectiveness of the proposed HS algorithm in terms of makespan.

Nowadays, industries face very strong challenges because of the high competitiveness between them. In fact, they are required to offer products with high quality and minimum cost in the minimum time. Since most of the characteristics and costs of the product and its manufacturing process are fixed in the design phase, this paper is focused on this strategic phase. Indeed, a new integrated product design approach is presented. It considers at the same time design requirements, materials characteristics, manufacturing parameters and the assembly process specifications. The developed approach is quantitative. Actually, the decision making is based on all its steps on objective and subjective indicators. To validate the integrated approach, a case study on the Schrader Robot is developed. This application allows to choose the most appropriate materials, manufacturing processes and assembly solution of its different components.

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

Advanced forming technologies have been evolving at a rapid pace with the products applicability in the industrial fields of aerospace and automobile especially for the materials like aluminum and titanium alloys (light weight) and ultra-high strength steels. Innovative forming methods like hydroforming (tube and sheet) have been proposed for industries throughout the world. The ever-increasing needs of the automotive industry have made hydroforming technology an impetus one for the development and innovations. In this paper, the review on various developments towards lightweight materials for different applications is presented. The influencing process parameters considering the different characteristics of the tube and sheet hydroforming process have also been presented. General ideas and mechanical improvements in sheet and tube hydroforming are given late innovative work exercises. This review will help researchers and industrialists about the history, state of the art in hydroforming technologies of the lightweight materials.

The purpose of this paper is to provide a practical approach of prioritizing the most critical success factors influencing the supply chain performance in SMEs by using an interpretive structural modeling (ISM) approach. The MICMAC analysis has been also considered for identifying the factors having a high influential power. Our findings show that, on the one hand, the production system, flexibility, communication and planning tools, process strategy, environmental uncertainty and innovation are the major factors influencing the supply chain performance. On the other hand, relationships with customers and suppliers, marketing and quality are seen as unfavorable outcomes to the supply chain managers and practitioners. These findings may help the policy makers, managers, and practitioners in formulating the strategies and policies for implementation of effectiveness supply chain.

The effectiveness of weld joints primarily depends on the fusion of base metal, minimum heat-affected zone (HAZ) and lesser residual stresses. The severity of thermomechanical effects e.g. weld shrinkages and residual stresses is significantly minimized by narrow gap welding technique over the traditional welding. This work describes the welding of A333 Grade 3 steel pipes by the application of GMAW and PGMAW techniques. The analysis is made to capture the effects of groove designs on residual stress and transverse shrinkage. The process parameters used for the analysis are voltage, current and welding speed. In this work, narrow groove design using PGMAW process is capable of reducing the number of passes and area of weld deposit by 35–40% by volume. In PGMAW, decrement in residual stresses is observed with a narrow groove compared to conventional V groove technique. The results are validated by metallurgical and mechanical investigation of welded joints. This work will help other researchers to understand the effect of narrow gap welding using an optimum number of passes for thick pipes.

Electrochemical discharge machining (ECDM) has been developed as a hybrid and robust technology for machining non-conductive work material at a preferable removal rate. ECDM exhibits various applications in the micro-machining of these materials like nuclear, automotive, medical industries, etc. Due to some peculiar properties of nonconductive materials, for example, glass transparency, their utilization in MEMS applications are also very numerous. In the ECDM process, removal of material takes place primarily due to high-temperature thermal erosion and secondarily due to electrolyte chemical etching action. Many rigorous experimental studies have reported in the empirical estimation of the material removal rate (MRR) in the ECDM process. However, very few studies have reported in the modeling of the ECDM process for predicting material removal rate through single spark simulation. The present paper attempts to develop a transient thermal model based upon finite element modeling (FEM) to simulate a single spark in the ECDM process for obtaining temperature fields in the work material. The obtained temperature fields are further post-processed to predict the material removal rate. FEM results are compared with the previous simulated and experimental results to confirm the approach. Moreover, an experimental study is also performed to validate the developed thermal model and it was found to be in an acceptable range of the experimental results. Further, a parametric study revealed that MRR increases with the increase in applied voltage and electrolyte concentration during soda-lime glass machining with ECDM. The developed FEM-based transient thermal model can be successfully utilized for predicting the removal rate of nonconductive work material.

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.

With the development of electronics, mechanical automation, computer and other related disciplines, and the improvement of product efficiency and quality in modern industry, welding robots are born and play an increasingly important role in industrial production lines. 6R welding robot is most commonly used in industrial production lines, so the research on 6R welding robot has practical application values. In this paper, MS165 Yaskawa robot is selected as the target robot. SolidWorks software is used to establish the three-dimensional model of Yaskawa robot, which is imported into Adams. Dynamics analyses of the rigid–flexible coupling system of 6R spot welding robot are studied by powerful dynamic simulation functions of Adams. The maximum stress position of the spot-welding robot working under load is also studied, and the maximum stress curve is obtained.

Flux-assisted tungsten inert gas welding process, also known as activated tungsten inert gas (A-TIG) welding, is extensively used in order to improve the performance of the conventional TIG welding process. In this study, the orthogonal array Taguchi (OA-Taguchi) method, regression modeling, analysis of variance (ANOVA) and simulated annealing (SA) algorithm have been used to model and optimize the process responses in A-TIG welding process. Welding current (I), welding speed (S) and welding gap (G) have been considered as process input variables for fabricating AISI316L austenitic stainless steel specimens. Depth of penetration (DOP) and weld bead width (WBW) have been taken into account as the process responses. In this study, SiO₂, nano-particle has been considered as an activating flux. To gather required data for modeling, statistical analysis and optimization purposes, OA-Taguchi based on the design of experiments (DOE) has been employed. Then the process responses have been measured and their corresponding signal-to-noise (S/N) ratio values have been calculated. Different regression equations have been applied to model the responses. Based on the ANOVA results, the most fitted models have been selected as an authentic representative of the process responses. Furthermore, the welding current has been determined as the most important variable affecting DOP and WBW with 68% and 88% contributions, respectively. Next, the SA algorithm has been used to optimize the developed models in such a way that WBW is minimized and DOP is maximized. Finally, experimental performance evaluation tests have been carried out, based on which it can be concluded that the proposed procedure is quite efficient (with less than 4% error) in modeling and optimization of the A-TIG welding process.