Journal of Advanced Manufacturing Systems

In the past few decades, sophisticated machining industries have rapidly improved in order to achieve the required shape of a part within a specific time while also not affecting material properties. Accuracy of machined components in all industries is important. In the case of subassemblies of components, geometrical accuracy of hole is vital. The researchers updated their machinery from time to time from this perspective. In the machining process, environment and time are the vital parameters that are mainly affected, so in this case, the design of experiments that are very useful in the machining region needs to be considered to overcome these challenges. This study aims to analyze the fabrication of hot abrasive jet machining (HAJMing) with different abrasive temperatures using fluidized bed system as well as accomplishment of cutting performance in hot-abrasive jet machining concerning target surface erosion. Additionally, this research study accomplishes the experimental study and computational fluid dynamics (CFD) technique for erosive footprint prediction extent in HAJMing constraints on target surface for intricately shaped tapered holes generation. The use of hot abrasives in the HAJM process has demonstrated an interest in improving cutting performance for material erosion. Moreover, this proposed experimental work contains the manufacturing, design and fabrication of hot abrasive jet machine (HAJM) using commercially available hardware and software. So, the components are manufactured indigenously as per the designed parameters for the purpose of improving the machining performance. Simulation for material erosion mechanism of HAJMing is used to obtain the nature of produced workpiece profile. Additionally, FB-HAJMing also deals with the sustainability assessment under environmental-friendly hot abrasive-assisted machining conditions.

Reverse logistics (RL) network can be adequately planned and implemented to gain additional benefits such as maximizing customer satisfaction and a positive image of the business organization, even though most distribution networks are not equipped with reverse channels to deal with the return of merchandise. Therefore, the main objective of this paper is to develop a new mixed-integer nonlinear programming (MINLP) mathematical model with a single-objective, single-product, multi-stage closed-loop supply chain network design (CLSC ND), considering the fixed transportation charge in the distribution network that has been neglected in the recently published papers in the field of CLSC ND. Since such network design challenges belong to the class of NP-hard problems, an algorithm based on ant colony optimization (ACO) is proposed to design a multi-stage RL network with fixed transportation cost and variable cost for the routes. Four network characteristics of different sizes were designed, and 30 instances were randomly generated for each network characteristic to evaluate the effectiveness of the proposed algorithm. The computational analysis of the results shows the high-quality effectiveness of the proposed ACO algorithm compared with the exact results.

The objective of this study is to examine the impact of ICT deployment on agility, responsiveness, asset management efficiency and reliability to increase the performance of supply chain. This study used questionnaire survey to collect the data from Chinese manufacturing firms and employed structural equation modeling. The findings revealed that ICT deployment positively and significantly improved the reliability, agility, responsiveness and asset management efficiency, which further translated into supply chain performance. This study will provide help to the managerial staff in Chinese firms and practitioners to understand the importance of ICT. Further, this study also motivates the governmental bodies to build smart and digital infrastructure, which will not only support the firms at the micro level but will spur the economic growth at the macro level.

The study of production systems taxonomy not only provides a good description of organization dominant groups but also provides the ground for more specialized studies such as a study of performance, the proper form of production decisions in each group, and the theorizing in it. In some taxonomic studies, due to high speed and ease of implementation, K-means cluster analysis was used to analyze data but the convergence took place in local optimum. For this reason, Hybrid Clustering Algorithms were used for the clustering of manufacturing companies. The clustering results of these methods were compared using validation indicators. According to the results of the comparisons, the best clustering algorithm was chosen, based on which cluster naming was done. Then, using the results of Discriminant Analysis, the distinctive dimensions of clusters were identified and the results showed that the manufacturing systems in Iran can be introduced in two dimensions of green production planning and resource capacity.

Single-period newsboy models and the corresponding EOQ models are, respectively, constructed under the condition that the manufacturer inspects raw materials of his supplier with total or sample inspection strategies and the manufacturer repairs or cheaply disposes the defective quality products when single supplier and manufacturer are faced with the customer stochastic demand and quality defects of raw materials. It is also found that marginal effect will be enlarged when unqualified products are disposed at a reduced price. There is a smaller impact to EOQ when manufacturer cheaply disposes the unqualified products when he repairs the products. Under demand-obeying normal distribution, EOQ will be linearly decreasing when the number of unqualified products increases. But EOQ will be linearly increasing when the order is greater than the mean demand and EOQ will also be linearly increasing with the demand standard deviation increase. Finally, the propositions and inferences are confirmed which can exert an impact on EOQ by adopting Monte Carlo simulation software. This research can provide some theoretical basis for manufacturers to reduce internal and external loss costs.

Machine learning approaches are disseminating very rapidly in manufacturing industry. They emerge out as latent modeling approaches showing a great potential for solving manufacturing-related problems. This study aims to analyze and predict the micro-irregularities on the surface of Inconel 690 superalloy processed by wire electro-spark machining using machine learning approaches. Experimental runs are completed by using the box-Behnken design of response surface methodology. Spark-on time, spark-off time, current and voltage with varying constraints are used as the input process variables, whereas surface integrity is used as the output variable. Support vector machines with two different functions and random forest regression are exercised to predict the machining outcomes with the help of existing models and validate the experimental outcomes. Support vector machine with radial basis function emerged out as the best approach in predicting the process outcomes. It is also perceived that spark-on time is the most dominating input process variable.

Electrical discharge machining (EDM) is a non-conventional process utilized for machining electrically conductive materials which cannot be machined by conventional processes. This paper is focused on modeling and optimization in electrical discharge machining (EDM) of UNS T30407 steel. The variables such as pulse on time (Ton), pulse off time (Toff), and current will influence the material removal rate (MRR) and surface roughness (Ra) which were taken as response variables. Experimental data were collected as per the central composite design (CCD) matrix of response surface methodology (RSM) approach and mathematical models have been developed for both the response variables. The analysis of variance (ANOVA) was employed to determine the significant machining parameters and optimize the input parameters for both the responses (MRR and Ra). The statistical adequacy of the models is tested with the help of ANOVA. The optimum parametric combination for maximum MRR and smaller Ra (highest surface finish) was found. The surface characteristics of the machined surface were investigated by a scanning electron microscope (SEM).

Inkjet printing (IJP) is an efficient, simple, scalable and low-cost additive manufacturing technique for the deposition of functional materials on substrates used in flexible electronic devices, sensors, and light-emitting diodes to name a few. Nanoparticle ink, metal oxide decomposition (particle-free ink), polymer ink, and semiconductor ink are classifications of the inks used in IJP. Effective printing of the material is possible when the ink parameters (viscosity, particle size, surface tension) and its derived dimensionless quantities (Weber number, Reynolds’ number, and Ohnesorge number) fall within a desirable range. The formation of the coffee-ring effect during the post-printing process is one of the major concerns, which affects the morphology and electrical conductivity of the printed pattern. In this review, a summary of recent developments of Ni-based inks in terms of formulation, sintering and properties is presented, along with the effect of combining Ni with other materials such as NiO, Ag, Cu, Zn, Fe, carbon, and rare earth metals on the film properties. The precursors and solvents used for the Ni ink preparation, along with the additives and surfactants, have been presented to understand their impact on the film’s properties and develop a design to choose the ideal precursor–solvent pair. Finally, the challenges in formulating inks and the necessity to develop a model to optimize the choice of solvent/ precursor are presented. The model would improve the selection of additives and precursors and reduce material wastage and enhance performance with fewer defects.

Metal castings with the presence of shrinkage porosity are often recycled or rejected and in turn reduce productivity of the process as well as increase energy cost involved in the process. This can be overcome by prevention of its occurrence using a suitable prediction technique. A detailed study of literature reveals that several Criterion Functions (an empirical model that connects solidification phenomena with formation of shrinkage porosity) have been employed to predict the location of shrinkage porosity in castings manufactured using particular process-alloy combination by values of process parameters (mostly thermal gradient, cooling rate, and velocity of molten metal). However, criterion function considering the effect of geometric variation in stainless steel castings on an extent of shrinkage porosity need to be established. In the present work, a benchmark casting, a combination of three T junctions, has been cast and used for the development of geometry-driven criterion function for stainless steel castings. Real experimental results with the presence of shrinkage porosity were used for superimposing on virtual experimental results (simulated results) for establishment of local simulation conditions. These conditions are further used in extrapolating results using casting simulation. The developed geometry-driven criterion function was further validated and found to be effective in prediction of shrinkage porosity.

This paper presents a hybrid model based on the integration of three interrelated topics including statistical process monitoring (SPM), production cycle length, and maintenance scheduling for imperfect production systems. The proposed model takes both process deterioration and product deterioration into account i.e., the process can go out of control and the products can deteriorate over time. In this regard, a nonuniform sampling strategy is presented for calculating the sampling intervals to avoid exceeding the hazard rate from a threshold value. Moreover, in order to make the model more adapted to real industrial systems, the shift magnitude in the mean parameter of quality characteristic is considered as a stochastic variable. To elaborate the model effectiveness, three comparative studies and a sensitivity analysis are presented. The first comparison shows that by using the variable sampling interval scheme instead of the fixed one, the ratio of in-control time interval to the whole production cycle increases dramatically. Moreover, the use of variable sampling interval scheme leads to a significant cost savings and decreases the out-of-control time interval. The second comparative study indicates that although using statistical considerations in establishment of the control chart imposes a higher cost on the manufacturer, both values of the in-control average run length (${\mathrm{\text{ARL}}}_{\mathrm{\text{in}}}$) and out-of-control average run length (${\mathrm{\text{ARL}}}_{\mathrm{\text{out}}}$) improve significantly. In other words, the use of economic-statistical model reduces the amount of non-conforming products as well as the risk of issuing a false alarm by the control chart. The third comparative study indicates that, in terms of total inventory holding cost, the proposed model performs better than the model without considering deterioration possibility due to the significant reduction in the deterioration cost.