Journal of Advanced Manufacturing Systems

The workshop scheduling has historically emphasized the production metrics without involving any environmental considerations. Low-carbon scheduling has attracted the attention of many researchers after the promotion of green manufacturing. In this paper, we investigate the low-carbon scheduling problem in a job shop environment. A mathematical model is first established with the objective to minimize the sum of energy-consumption cost and completion-time cost. A discrete genetic-grey wolf optimization algorithm (DGGWO) is developed to solve the problem in this study. According to the characteristics of the problem, a job-based encoding method is first employed. Then a heuristic approach and the random generation rule are combined to fulfill the population initialization. Based on the original GWO, a discrete individual updating method the crossover operation of the genetic algorithm is adopted to make the algorithm directly work in a discrete domain. Meanwhile, a mutation operator is adopted to enhance the population diversity and avoid the algorithm from getting trapped into the local optima. In addition, a variable neighborhood search is embedded to further improve the search ability. Finally, extensive simulations are conducted based on 43 benchmark instances. The experimental data demonstrate that the proposed algorithm can yield better results than the other published algorithms.

The market trends from last few decades have increased the pressure on development of the new products in minimum time. This can be supplemented with modification in the manufacturing processes for product development. Since last two decades, additive manufacturing (AM) also known as rapid prototyping has been used to produce components. The present work aims at optimization of thickness of deposition and surface finish of copper on Acrylonitrile Butadiene Styrene (ABS) components produced by Fused Deposition Modeling (FDM), which can be used for indirect tooling. The different process parameters such as Voltage, Concentration of Copper sulfate Solution (CSS) and deposition time are optimized. The experiment reveals that the best process parameters for maximum deposition rate gives a deposition rate of 0.19 mm/h. The surface roughness is found to be minimum at the lower values of the process parameters as 1.87 $\mu$m. It has been observed that voltage (43.2%) and concentration of CSS (48.8%) significantly affect the deposition rate and for surface finish the concentration of CSS (69.59%) is the major contributor. The microstructure study of the deposited copper reveals a variation in the grain size from coarse to fine as the thickness of the deposited layer increases.

In a dynamic production environment, not only the customer’s needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.

Backlash, friction, and servo lag factors often result in protrusion or segment difference phenomenon in the moving speed reversal of a machine tool’s moving table. This phenomenon can be improved by adjusting the backlash control parameters of the machine tool controller, but the control parameters must vary with the feed rate and payload of the moving table. Therefore, this study performed the circular test process for CNC machine tools, and used different feed rate, radius, and payload motion conditions to discuss the effect of backlash control parameters on quadrant protrusions. First, this study used parameter-range reduction combined with the Taguchi method and the binary search algorithm to search for the optimal backlash control parameters in the parameter setting range, so that the machine tool could have preferable quadrant protrusion performance when executing circular tests. Afterward, the correlation of the moving table feed rate, radius, and payload to the quadrant protrusion was analyzed according to the experimental results. The results indicated that the machine tool moving table feed rate had the most apparent effect on quadrant protrusions, and the relationship between the payload and quadrant protrusion was influenced by the moving table feed rate and circular radius simultaneously.

Micro-Electro Discharge Machining ($\mu$-EDM) is one of the nonconventional micro-machining processes in which electrical discharges are produced between the micro-sized electrodes — tool and workpiece immersed in dielectric fluid. The electrical discharges thus produced remove the workpiece material through melting and vaporization process and results in the creation of micro-craters. This paper focuses on machining of micro-channels on copper alloy — phosphor bronze plate using $\mu$-EDM. Various micro-channels have been machined on phosphor bronze with input parameters: voltage, capacitance and pulse on-time varied at four levels. The experimental layout plans were designed with robust design technique — Taguchi-based L${}_{16}$ orthogonal array and the process parameters were optimized using optimization technique — Grey Relational Analysis (GRA). The dimensional aspects — depth, width, profile and surface roughness of machined channels were selected as output responses. The experimental results show that the parameters have individual and combined effect on responses. Analysis of Variance (ANOVA) technique has been used to explore the effect of parameters on responses. Detailed surface morphology of the machined features has also been analyzed using scanning electron microscope (SEM) and noncontact optical profiler images. As an extension of machining channels, Y-shaped channel has been machined on copper alloy which can be used for micro-fluidic applications.

Additive manufacturing (AM), applied to metal industry, is a family of processes that allows complex shape components to be realized from raw materials in the form of powders. Electron beam melting (EBM) is a relatively new additive manufacturing (AM) technology. Similar to electron-beam welding, EBM utilizes a high-energy electron beam as a moving heat source to melt metal powder, and 3D parts are produced in a layer-building fashion by rapid self-cooling. By EBM, it is possible to realize metallic complex shape components, e.g. fine network structures, internal cavities and channels, which are difficult to make by conventional manufacturing means. This feature is of particular interest in titanium industry in which numerous efforts are done to develop near net shape processes. In the field of mechanical engineering and, in particular, in the aerospace industry, it is crucial for quality certification purpose that components are produced through qualified and robust manufacturing processes ensuring high product repeatability. The contribution of the present work is to experimentally identify the EBM job parameters (sample orientation, location of the sample in the layer and height in the build chamber) that influence the dimensional accuracy and the surface roughness of the manufactured parts in Ti6Al4V. The repeatability of EBM is investigated too.

An exothermic flux for submerged arc welding process has been developed which is capable of enhancing weld penetration of the joint. For this purpose, thermit mixture in different proportions (20% and 40%) has been added to the parent flux by agglomeration process. Beads on plate were deposited using parent and developed exothermic fluxes for a comparative study. EH14 filler wires in combination with parent and exothermic fluxes were used in this investigation. The effects of welding parameters and exothermic flux on weld penetration were investigated and the results have been presented in this paper. It has been found that the penetration increases from 2.95 to 3.51mm with 40% thermit mixture addition to the parent flux. It is further observed that penetration increases with increase in the amount of thermit mixture added. A mathematical model has been developed to predict weld penetration or select suitable welding parameters to obtain the desired penetration. The significance of coefficients was tested using Student’s $t$-test and the adequacy of developed model was tested using $F$-test. The effects of various parameters on penetration have been presented in graphical form for better understanding.

Glass fiber reinforced polymer (GFRP) composite gear is used in a number of applications where fine motion transmission and silent rotation is required. In order to increase its usage there is a need to increase the quality of gear. Shrinkage problem is associated with injection molded gear. In present case blank is prepared by injection molding and teeth are cut on gear shaper by which metrology can be controlled by optimizing the machining parameters. An analysis of variance was applied on 27 experiments to validate the process and found out that rotary feed is at rank 1 which is 0.15mm/stroke, cutting fluid ratio is at rank 2 which is 12%, cutting speed is at rank 3 which is 240 stroke/min, fluid flow rate is at rank 4 which is 30 ml/min. By using these parameters optimum performance obtained is 0.213mm root diameter deviation (RD), 0.165mm tooth thickness variation (TT) and 1$\mu$m roughness average (Ra) with grey relational grade of 0.8318. The optimum response provided the best value of RD, TT and Ra for the range included in experimental results which is 0.138 to 0.416mm, 0.012 to 0.187mm and 1.2 to 2.43$\mu$m respectively. Surface roughness improvement in this work is 49.8% higher as compared to result available in literature.

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.

Titanium alloys find increased applications in many engineering fields such as bio-medical, aerospace, electronics and automobile industries due to their superior physical properties such as high specific strength, weight to strength ratio, corrosive resistant and toughness. Low thermal conductivity of titanium alloys makes it difficult to machine since high cutting temperature is generated at tool work piece interface. As it is highly chemically reactive at high temperature, work piece is adhered to the tool surface. The present work aims at investigating the effect of drilling process parameters viz. spindle speed, drill bit diameter and feed rate on machining performance measures such as burr height at entry and exit, surface roughness and circularity at entry and exit. In order to optimize all the performance measures simultaneously, the present investigation utilizes a hybrid approach known as superiority and inferiority ranking method combined with technique for order preference by similarity to ideal solution (SIR-TOPSIS) to convert these characteristics into single equivalent characteristic ($r$-flow). A statistically valid empirical relationship is developed for r-flow in terms of machining parameters using nonlinear regression technique. The study aims at attaining optimal parametric setting that maximizes $r$-flow. To optimize $r$-flow, an improved version of meta-heuristic evolutionary algorithm known as harmony search (HS) algorithm is implemented. The confirmatory experiment suggests the robustness of the methodology in optimizing the drilling operation.