Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manufacturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases, which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.

Sustainable development is now the focus of researchers and organizations worldwide. Several concepts, such as reverse logistics (RLs) and closed-loop supply chains, have been introduced to encourage sustainability in supply chains. RLs refers to the set of activities needed by consumers to collect the product used for reuse, repair, remanufacturing, recycling, or disposal of the used product. There are various processes involved in RL, and one of them is collection systems. Collection refers to a company obtaining custody of specific items. We review the literature on RLs collection systems. A bibliometric analysis was conducted to provide better insight into the field and establish any trends. Firstly, we present the classification methods used in the field, based on available review papers. Secondly, we evaluate literature from several fields that are related to either the problem setting or the technical features. Different perspectives are presented and classified. This method facilitates the identification of manuscripts related to the reader’s specific interests. Throughout the literature review, trends in measuring the performance of collection systems are identified, and directions for future research are identified and presented.

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

Supply chain agility and sustainability is an essential element for the long-term survival and success of a manufacturing organization. Agility is an organization’s ability to respond rapidly to customers’ dynamic demands and volatile market changes. In a dynamic business environment, manufacturing firms demand agility to be evaluated to support any alarming decision. Sustainability is an aspect to sustain collaboration, value creation, and survival of firms under a dynamic competitive business scenario. Agility is a capability that drives competitiveness to foster sustainability aspects. The purpose of this article is to consider and evaluate the supply chain behavior within the context of Saudi enterprises. The efficacy and relevance of this model were explored through a case study conducted in a Saudi dairy manufacturing corporation. Owing to the complexity and a large number of calculations that are required for evaluating the agility of the supply chain, a decision support system was proposed as a tool to assess the supply chain and identifying barriers to a strategic sustainable solution for a specific organizational target. The decision support system is extensive as it contains six separate agility enablers and ninety-three agility attributes for the supply chain. The assessment was carried out using a fuzzy multi-criteria method. It combines the performance rating and importance weight of each agile supply chain-enabler-attribute. To achieve and sustain local and global success, the case organization strove to become a major local and global manufacturer to satisfy its customers, reduce its time to market, lower its total ownership costs, and boost its overall competitiveness through improving its agility across supply chain activities to foster sustainability for a manufacturing organization located in Saudi Arabia.

Electron beam melting (EBM) technology is a novel additive manufacturing (AM) technique, which uses computer controlled electron beams to create fully dense three-dimensional objects from metal powder. It gives the ability to produce any complex parts directly from a computer aided design (CAD) model without tools and dies, and with variety of materials. However, it is reported that EBM has limitations in building overhang structures, due to the poor thermal conductivity for the sintered powder particles under overhang surfaces. In the current study, 2D thermo-mechanical finite element models (FEM) are developed to predict the stresses and deformation associated with fabrication of overhang structures by EBM for Ti-6Al-4V alloy. Different support structure geometries are modeled and evaluated. Finally, the numerical results are validated by experimental work.

The proton exchange membrane fuel cell (PEMFC) is the fastest growing fuel cell technology on the market. Due to their sustainable nature, PEMFCs are widely adopted as a renewable energy resource. Fabricating a PEMFC is a costly process; hence, mathematical modeling and simulations are necessary in order to fully optimize its performance. Alongside this, the feasibility of a waste heat recovery system based on the organic Rankine cycle is also studied and power generation for different operating conditions is presented. The fuel cell produces a power output of 1198 W at a current of 24A. It has 50% efficiency and hence produces an equal amount of waste heat. That waste heat is used to drive an organic Rankine cycle (ORC), which in turn produces an additional 428 W of power at 35% efficiency. The total extracted power hence stands at 1626 W. MATLAB/Simulink R2016a is used for modeling both the fuel cell and the organic Rankine cycle.

This research work primarily focused on investigating the effects of changing rotational speed on the forming temperature and microstructure during incremental sheet metal forming (ISF) of AA-2219-O and AA-2219-T6 sheets. Tool rotational speed was varied in the defined range (50–3000 rpm). The tool feed rate of 3000 mm/min and step size of 0.3 mm with spiral tool path were kept fixed in the tests. The sheets were formed into pyramid shapes of 45° draw angle, with the hemispherical end forming tool of 12 mm diameter. While the sheets were forming, the temperature variation due to friction at the sheet–tool contact zone was recorded, using a non-contact laser projected infrared temperature sensor. It was observed that the temperature rising rate for the T6 sheet during ISF is higher as compared to the annealed sheet, thereby showing that the T6 tempered sheet offers higher friction than the annealed sheet. Due to this reason, the T6 tempered sheet fails to achieve the defined forming depth of 25 mm when the rotational speed exceeds 2000 rpm. The effects of rotational speed and associated rise in the temperature were examined on the microstructure, using the scanning electron microscopic (SEM). The results reveal that the density of second phase particles reduces with increasing speed reasoning to corresponding temperature rise. However, the particle size in both tempers of AA2219 received a slight change and showed a trivial response to an increase in the rotational speed.

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