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.

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.