Basil M. Darras

The predicted rapid accumulation of end-of-life lithium-ion batteries (LIBs) from electric vehicles (EVs) has raised environmental concerns due to the toxic nature of LIB materials. Consequently, researchers have developed reusing and recycling plans for end-of-life LIBs to extend their life spans, mitigate residual capacity loss, and reduce their environmental impact. As a result, many studies have recommended establishing a lifecycle framework for LIBs to identify and manage the potential options for reusing, recycling, remanufacturing, or disposal of second life LIBs. In response, the state of health (SOH) and state of safety (SOS) methods were introduced as key performance indicators (KPIs) to determine the batteries’ health and usability based on their capacity levels. Thus, both SOH and SOS methods are crucial for battery cell selection frameworks employed to designate batteries’ second-life applications. Various papers have analyzed and compared SOH methods, yet none have clearly quantified their differences, to determine the most effective method. Therefore, this study aims to create a framework for selecting the most effective SOH method for use in LIB frameworks by identifying and quantifying their main KPIs. The proposed framework will utilize scientometric analysis to identify the KPIs necessary for a gray relation analysis (GRA)-based multi-criteria decision matrix (MCDM) to select the appropriate SOH method.

Friction stir welding (FSW) has gained increasing prominence in the realm of metal–plastic hybrid joints, yet its long-term sustainability remains a subject of uncertainty. This research investigates the sustainability aspect of FSW, positioning it against conventional techniques like adhesive bonding (AB) and self-piercing riveting (SPR). A comprehensive evaluation framework encompassing environmental, social, economic, and physical factors was employed, through which specified criteria were applied to select pertinent sustainability indicators across all dimensions to ensure a thorough assessment. In this study, two advanced multi-criteria decision-making methods (MCDM) were deployed for data normalization and aggregation. Sensitivity analysis was conducted to examine the robustness of the results. The outcomes yielded a sustainability rating system, facilitating a direct and insightful comparison with traditional methods. Based on the results of this study, SPR outperforms both FSW and AB in terms of overall sustainability with comparative average sustainability scores of 75.3%, 54.2%, and 35.3%, respectively. This study not only sheds light on the current state of FSW sustainability but also provides a valuable benchmark for decision-makers in selecting environmentally conscious methods for metal–plastic hybrid joints.

Characterized by its resilience, connectivity, and real-time data processing capabilities, the fourth industrial revolution, referred to as Industry 4.0, is the main driver of today’s digital transformation. It is crucially important for manufacturing facilities to correctly identify the most suitable Industry 4.0 technologies that meet their operational schemes and production targets. Different technology selection frameworks were proposed to tackle this problem, several of which are complex, or require historic data from manufacturing facilities that might not always be available. The aim of this paper is to develop a novel Industry 4.0 selection framework that utilizes Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) to rank different Industry 4.0 technologies based on their economic, social, and environmental impact. The framework is also implemented on a real-life case study of a manufacturing firm to rank the different Industry 4.0 technologies required for its digital transformation based on their significance to the facility’s key performance indicators. The framework is utilized to select the top three Industry 4.0 technologies from a pool of eight technologies that are deemed important to the manufacturing firm. Results of the case study showed that Cyber-Physical Systems, Big Data analytics, and autonomous/industrial robots are the top three ranked technologies, having closeness coefficient scores of 0.964, 0.928, and 0.601, respectively. Moreover, the framework showed sensitivity towards weight changes. This is an advantage in the developed framework, since its main aim is to provide policymakers with a customized list of technologies based on their importance to the firm.

Metal–polymer hybrid structures are becoming desirable due to their wide range of applications in the automotive, aerospace, biomedical and construction industries. Properties such as a light weight, high specific strength, and design flexibility along with the low manufacturing costs of metal–polymer hybrid structures make them widely attractive in several applications. One of the main challenges that hinders the widespread utilization of metal–polymer hybrid structures is the challenging dissimilar joining of metals to polymers. Friction stir welding (FSW) shows a promising potential in overcoming most of the issues and limitations faced in the conventional joining methods of such structures. Several works in the literature have explored the FSW of different metal-to-polymer combinations. In some of the works, the joints are examined based on processing parameter optimization, microstructural characteristics, and mechanical performances. It is, therefore, important to summarize the findings of these works as a means of providing a reference to researchers to facilitate further research on the utilization of FSW in joining metals to polymers. Thus, this work aims to present a comprehensive technical review on the FSW technique for joining metals to polymers by reviewing the reported literature findings on the impact of materials, tools, process parameters, and defects on the strength and microstructure of the produced joints. In addition, this work reviews and presents the latest practices aiming to enhance the metal–polymer joint quality that have been reported in the literature.

Transitioning to zero-emission vehicles (ZEVs) is thought to substantially curb emissions, promoting sustainable development. However, the extent of the problem extends beyond tailpipe emissions. To facilitate decision-making and planning of future infrastructural developments, the economic, social, and technological factors of ZEVs should also be addressed. Therefore, this work implements the circular economy paradigm to identify the most suitable vehicle type that can accelerate sustainable development by calculating circularity scores for Internal Combustion Engine Vehicles (ICEVs) and two ZEVs, the Battery Electric Vehicles (BEVs), and Fuel Cell Electric Vehicles (FCEVs). The circularity assessment presents a novel assessment procedure that interrelates the environmental, economic, social, and technological implications of each vehicle type on the three implementation levels of the circular economy (i.e., The macro, meso, and micro levels). The results of our analysis suggest that not all ZEVs are considered sustainable alternatives to ICEVs. BEVs scored the highest relative circularity score of 36.8% followed by ICEVs and FCEVs scoring 32.9% and 30.3% respectively. The results obtained in this study signify the importance of conducting circular economy performance assessments as planning tools as this assessment methodology interrelate environmental, social, economic, and technological factors which are integral for future infrastructural and urban planning.

Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.