Journal of Advanced Manufacturing Systems

Nowadays, reverse logistics (RL) is one of the key strategies in many industries, especially in the electronics industry due to increasing environmental awareness and sustainable management. The main aim of this research is to investigate the correlation of RL performance to solutions for RL practice of the electronics industry in Thailand. In this research, questionnaires were distributed to 417 companies in the electronics industry of Thailand. A conceptual model was developed and the model examined by using structural equation modeling (SEM). Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used in this study. The hypotheses were tested in which RL performance was directly positively associated with the overall solutions for RL practices. RL performance was also indirectly positively associated with management & organization solutions, legal & technology solutions and collaboration and support solutions of the various solutions for RL practices. Empirical data was tested by using SEM and it was found that the proposed model could fit with the empirical data. The proposed results of this study will help to understand more about RL practices and could provide further direction for researchers and practitioners in the electronics industry and other related industries.

One of the most practical optimization problems in the reliability field is the redundancy allocation problem (RAP). This problem optimizes the reliability of a system by adding redundant components to subsystems under some constraints. In recent years, various meta-heuristic algorithms applied to find a local or global optimum solution for RAP in which redundancy strategies are chosen. Among these algorithms, simulated annealing algorithm (SA) is a capable one and makes use of a mathematical analogue to the physical annealing process to finding the global optimum. In this paper, we present a new simulated annealing algorithm named knowledge-based simulated annealing (KBSA) to solve RAP for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. In the KBSA algorithm, the SA part searches the solution space to find good solutions and knowledge model saves the knowledge of good solution and feed it back to the algorithm. In this paper, this approach achieves the optimal result for some instances in the literature. In order to evaluate the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. Finally, the results illustrate that the proposed algorithm has a good proficiency in obtaining desired results.

Decision of finest machining parameters is very essential factors in any processing of machined parts. This article presents a multi-objective optimization technique, based on WASPAS method toward optimize the machining parameters in abrasive jet machining (AJM) process: pressure, nozzle to tip distance (NTD), and average grain diameter on NICKEL 233 alloy. Three conflicting objectives, Material Removal Rate (MRR), surface roughness (Ra) and taper angle (Ta) are simultaneously considered. The proposed technique weighted aggregated product sum assessment technique is investigation of parametric optimization on AJM process. Its outcome using tool in any ranges of responses in AJM process is the optimal setting of parameters are determined through experiments illustrated. Broad usage of Aerospace industries for NICKEL 233, generating a hole of the machining data first time in this work using AJM will be useful.

This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.

Heat reveals during the bone drilling operations in orthopedic surgery because of friction between bone and surgical drill bit. The heating causes extremely important damages in bone and soft tissues. The heating has a critical threshold and it is known as 47^∘C. If bone temperature value exceeds 47^∘C, osteonecrosis occurs in bones and soft tissues. Many factors such as surgical drill bit geometry and material, drilling parameters, coolant has important roles for the temperature rise. Many methods are used to decrease the temperature rise. The most effective method among them is to use the coolant internally. Numeric simulations of a new driller system to avoid the overheating during the orthopedic operating processes were performed in this study. The numerical simulation with/without coolant was also performed using the finite element based software. Computer aided simulation studies were used to measure the bone temperatures occurred during the bone drilling processes. The outcomes from the simulations were compared with the experimental results. A good temperature level agreement between the experimental results and FEA simulations was found during the bone drilling process.

The paper discusses the effect of cutting parameters and cutting tool material on chip compression ratio, cutting forces and surface roughness in turning of high strength temperature resistant superalloys (HSTR). The experiments were performed in dry cutting environment on precision CNC lathe with fixed depth of cut of 0.5mm. Analytical model is developed to determine chip segmentation frequency, shear angle and shear strain and it is correlated with the machining parameters. The machinability of the selected superalloys is assessed in terms of cutting force, chip compression ratio and surface roughness. It is found from the experimental analysis cutting force magnitude is less at higher cutting speed for all the superalloys. Chip compression ratio is found maximum in case of Inconel 718 due to precipitation hardening of alloy and followed by Inconel 600 and Inconel 800. The chip segmentation frequency is high at lower cutting speed for Inconel 600 due significant strain hardening. Serrated chips are produced during machining of three selected superalloys and it is found that serrated tooth spacing decreases with cutting speed. Shear plane angle increases on cutting speed increases which effect tool workpiece contact length during machining resulted thin, short and snarled chips was produced. From analytical modeling it shows that shear strain decreases with cutting speed which indicate that at higher cutting speed material deformed elastically than plastically. The effect of cutting tool material is observed on the surface roughness. The better surface finish is obtained with coated carbide inserts as compared to ceramic inserts for all the selected superalloys. However, Inconel 800 shows higher surface roughness due to combination of (Ni–Cr–Fe) alloying element which is responsible for carburization of surface layer during machining.

The present study addresses the machinability investigation in finish dry hard turning of high strength low alloy steel with coated ceramic tool by considering cutting speed, feed and depth of cut as machining parameters. The technological parameters like surface roughness, flank wear, chip morphology and economical feasibility have been considered to investigate the machinability performances. Twenty seven set of trials according to full factorial design of experiments are performed and analysis of variance, multiple regression method, Taguchi method, desirability function approach and finally Gilbert’s approach are subsequently applied for parametric influence study, mathematical modeling, multi-response optimization, tool life estimation and economic analysis. Results indicated that feed and cutting speed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness and tool flank wear is considered. Abrasions, adhesion followed by plastic deformation have been observed to be the principal wear mechanism for tool life estimation and observed tool life for coated ceramic insert is 47min under optimum cutting conditions. The total machining cost per part is ensued to be lower ($0.29 only) as a consequence of higher tool life, reduction in downtime and enhancement in savings, which finds economical benefits in hard turning. The current work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process considering techno-economical and ecological aspects.

In a mass production environment of supply chains where volume of production runs into millions of parts per month where even the slight improvement in the manufacturing process through cycle time reduction or better material utilization has the potential to produce huge savings and productivity to match customer tact time, the paper serves a useful insight into appropriate tooling technology for moulding processes. Though there have been several studies on hot runner technology in injection molding processes vis a vis conventional cold runner technology, ultimate manufacturer still has to conduct trials and calculate ROIs, initial investment, quality and maintainability aspect to conclude if the manufacturer can go for hot runner or not. The paper lends a scientific expression to this decision making problem and offers a mathematical model with variables like Gross weight to Net Weight ratio, machine tonnage, volume involved. The user only has to put in the values to get an index value with a threshold number to decide if hot runner is viable for the component in hand or not.

This paper addresses a multi-echelon capacitated location–allocation–inventory problem under uncertainty by providing a robust mixed integer linear programming (MILP) model considering production plants at level one, central warehouses at level two, and the retailers at level three in order to design an optimal supply chain network. In this model, the retailer’s demand parameter is uncertain and just its upper and lower bounds within an interval are known. In order to deal with this uncertainty, a robust optimization approach is used. Then, a self-learning particle swarm optimization (SLPSO) algorithm is developed to solve the problem. The results show that the proposed algorithm outperforms the exact method by providing high quality solutions in the reasonable amount of computational runtime.