Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems  
[PERSON]  
1 Department of Engineering Design and Robotics, Technical University of Cluj-Napoca,  
Bvd. Muncil 103-105, 400641 Cluj-Napoca, Romania; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Engineering Design and Robotics, Technical University of Cluj-Napoca,  
Bvd. Muncil 103-105, 400641 Cluj-Napoca, Romania; [EMAIL_ADDRESS]  
Beyond any necessary transformations of processes in terms of digitalization and data-driven business models, clean production, and mechanisms for recovery, recycling, and reuse of products, consideration of lifecycle and lifetime perspectives in close connection with servitization and mass customization requires, with no reservation, embedded capabilities of reconfigurability in the manufacturing systems and associated equipment to fulfill the goals of industry 5.0.  
Reconfigurability at the machine level is about modularity, convertibility, personalized flexibility, scalability, diagnosability, integrability [10, 11]. By comprising these capabilities, a reconfigurable machine tool from a manufacturing system is aligned to industry 4.0, circular economy, and smart workforce requirements. Thus it is aligned to the paradigm of industry 5.0, also coined as intelligent manufacturing ecosystem [7].  
Nevertheless, reconfigurability comes with an increased entropy in design. Therefore, it is crucial for tackling the design process of reconfigurable equipment in the context of a given manufacturing capacity to align it to the guiding principles of industry 5.0. The major challenge is the necessity to handle the mix of objective functions (also named target functions) that frames reconfigurable equipment.  
Modularity is achieved by the ability of the independent, intelligent equipment to cooperate and act as a compact unit [12]. Scalability is reached using the communication protocol where theoretically, an unlimited number of high and low priority equipment could be connected [12]. Convertibility and customization are accomplished at a certain level by the ability to configure on request and in real time the functionality of intelligent equipment [12]. Integrability is expressed by implementing solutions that ensure stability and compatibility between old and new hardware modules and software packages [12]. Diagnosability is the ability of the main control unit to gather information from the connected modules, identify incompatibilities, cancel commands to the concerned equipment, and alert the operator about the identified issues [12].  
The key characteristics of reconfigurability are interconnected. In principle, a robust design requires tackling them concurrently. It is thus the purpose of this paper to focus the research investigation on a generic design algorithm that incorporates the potential to tackle simultaneously engineering key performance indicators (E-KPIs) that describe a complex design optimization function (note: here, reconfigurability).  
In this respect, the next section of the paper provides a synthesis of design algorithms suitable for the purpose mentioned above. The investigation is not limited only to algorithms strictly dedicated to reconfigurable machine-tool design, even if this area is also included in our analysis. The reason is to explore contributions beyond the scope of reconfigurable design that could be useful also for this specific topic.