Journal of Advanced Manufacturing Systems

The Green Supply Chain Management (GSCM) is a significantly important proposition to resolve issues and hurdles between the economic development of manufacturing enterprises and its commitment to environment and society, and it will also encourage and promote the degree of green environmental and eventually enhance the enterprises performance. This article collects 136 manufacturing companies of Guangzhou and Shenzhen provinces of China as the research samples, and structural equation model (SEM) is used to explore the relationship among environmental monitoring, environmental collaboration and enterprise’s performance. These findings show that the GSCM contains social environmental responsibility information publicly and the general public engagement, provides new power to achieve the win-win situation between financial and environmental performance through collaborated environmental management activities, environmental violations monitoring and other aspects performed by downstream and upstream companies.

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type $k$ are available for processing the specific subset ${{𝒪}}^k$ of the given operations. Each set ${{𝒪}}^k$ may be partitioned into subsets for their processing on the machines of type $k$. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set ${{𝒪}}^k$ to the machines of type $k$ and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

Friction at die work piece interface can be reduced by the presence of lubricant in the entry zone. As the metal–metal contact in the deformation zone is avoided, the deformation force is reduced and at the same time the product quality and die life are improved. So the prediction of minimum film thickness and its control through process variables is gaining interest in the industry. Two multi-objective nonlinear optimization problems are formulated in this study, one using the minimum thermal thickness and billet temperature at the work zone. And the other optimization problem is formulated by including the minimum isothermal thickness at entry zone and the billet temperature at the work zone. These two multi-objective optimization problems have been solved using particle swarm optimization algorithm (PSO). The key process variables are identified by coefficient of variance (COV) analysis. A sensitivity analysis on the process variables is performed to prioritize the process variables. A design procedure is explored to demonstrate the industrial application of this analysis.

CV. Jogja Transport is a company that distribute cakes “Sari Roti” in Yogyakarta, Indonesia. It has responsibility to distribute the cakes for every customer during the customers’ time windows. The distribution problem of CV. Jogja Transport belongs to Vehicle Routing Problem with Time Window (VRPTW). This paper tries to solve the problem of CV. Jogja Transport by proposing “cluster first route second” algorithm of simple heuristic method. Then the algorithm is combined with sweep algorithm for clustering the customers and Mixed Integer Linear Programming (MILP) to select the best route so that it can minimize the distance of each cluster. The result indicate that implementation of sweep algorithm and MILP can reduce the distances and the fuel up to 10.95% and the travel distance up to 2.60%.

Nowadays, it has become very difficult for the manufacturer to satisfy all its customers with satisfactory products, as they have different demands considering the responses. However, hard-to-machine materials are difficult to manufacture. This study explores the application of electro-discharge machining (EDM) of Ti–6Al–4V alloy with pulse duration (Ton), duty factor ($\tau )$, peak current (Ip) and gap voltage (Vg) as the control parameters using pure copper electrode. Machining effects are evaluated by performance characteristics including material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) by considering the multi-criteria preference of the customers that vary with the preference of responses. For the experiment, proper orthogonal arrays are found out using Taguchi methodology. The optimum parametric settings were obtained by utility-concept-based Taguchi method which were compared with desirability-approach-based Taguchi. It was found that utility-concept-based Taguchi optimization methodology results in more feasible parametric setting which has also been confirmed by the validation experiments. The optimum values of process variables obtained were pulse duration of 30$\mu$s, duty factor of 9%, peak current of 10A and gap voltage of 6V to achieve maximum MRR and lower TWR with better surface finish to satisfy multi-user criteria.

In order to meet with the requirements of equipment network integration, flexible customized assembly, lean logistics management and control of assembly process, an intelligent manufacturing model of sophisticated equipment assembly workshop is proposed in this paper. By modeling the requirement of intelligent manufacturing mode in sophisticated equipment assembly workshop, an intelligent manufacturing technology architecture system and its operation mode are built, which include six layers as user layer, core functional layer, system integration layer, intelligent perception layer, resource layer and basic support layer. The key technologies such as the intelligent perception technology of assembly resources by using intelligent information interaction device based on IPv6 and the abnormal early warning method of critical assembly process based on BP neural network are studied. Finally, the analysis is illustrated in the case of a realistic application from a local manufacturing enterprise to highlight the proposal’s feasibility and applicability by applying the proposed intelligent manufacturing model.

Rapid Prototyping (RP) technology has become the powerful tool for product development in almost every branch of engineering. Many new and upcoming processes offer means for the fast creation of models with steadily increasing accuracy, built speed, other model properties and economic advantages. Fused Deposition Modeling (FDM) is the most famous and commercially available RP system. This paper presents the application of Utility concept with Taguchi method for multiresponse optimization of the FDM process. Stratatys Fortus 400mc FDM setup is used to conduct experiments as per Taguchi’s L₉ orthogonal array. FDM parameters: Layer thickness, part orientation and raster angle were optimized based on multiple responses, i.e. tensile, flexural, impact and compressive strength. The optimum process parameters are calculated using utility concept. The Analysis of variance (ANOVA) is applied to find out the most significant factor. It has been found that layer thickness is the most significant factor, followed by part orientation and raster angle. The confirmation tests with optimal levels of process parameters are conducted to illustrate the efficacy of the proposed method. It is found that optimum combination of process parameters gives the highest utility value, which indicates that multiresponses of the FDM process can be improved through this approach.

Laser beam welding (LBW) is based on interaction between the laser source and base metals. Different methods have been developed recently to produce weld joints of light metals. This produces good weld bead to simplify the structure and reduce the weight and cost to meet the important concerns of aerospace industry. To achieve these, Ti (Ti6Al4V) and Al (AA2024) dissimilar alloy sheets are welded with butt joint configuration using Nd: YAG pulsed laser welding unit. The weldment is subjected to tests to evaluate mechanical and distortion characteristics. From the test results, it is found that LBW is very much suitable for joining Titanium/Aluminium (Ti/Al) alloy sheets.