## 2 Research Focus and Relevance
This research is outside the scope of our focus because it does not treat reconfigurability from the perspective of its intrinsic characteristics and has nothing to do with the conceptual design of the reconfigurable machine tools. Nevertheless, it draws on the angle of applying the PSO algorithm to solve the multiobjective optimization problem, but in the register delimited by dimensional optimization [48]. In [51], the authors propose a framework for rapid design of the architectural layout of a reconfigurable machine tool. The focus is strict on modularity, which is only a secondary characteristic of reconfigurability, and consists of a step for layout configuration and a subsequent step of layout evaluation. This limitation places research from [51] outside the scope of the job investigated in this paper. Paper [52] also deals with design in the case of reconfigurable machine tools, but the focus is on a different design stage; that is, after the stage of configuration design, treating the problem of accuracy of the machine in the case of a new reconfiguration. Despite the value of this research, the design focus is not in the same area as the one indicated by our paper. As the case of [51], paper [53] also treats reconfigurable machine-tool design from the limited scope of modularity. It proposes an algorithm to define the minimum number of modules necessary for the machine to solve a given family of parts. It is more an algorithmic search for a minimum in a predefined space, which does not require an infusion of creativity, but rather a search in the space of possibilities. From this perspective, this work is also not related to the scope of research investigated in this paper. In [54], the research stays in the same register as the one in [53], dealing with minimization of modules changed when parts are changed. This is an important element in the reconfigurable machine-tool design but is mainly related to a single key performance indicator that optimizes modularity layout. Using tabu search algorithms, paper [55] operates with the same optimization problem of defining the optimal path for reconfiguring a machine tool. It is another way to solve the same problem investigated by [53; 54]. The layout design of a reconfigurable machine tool starting from the space defined by the family of parts using metaheuristics is proposed in [56]. Even if the design problem remains in the register signaled by [53; 54; 55], this work has the merit of putting the design in a more concrete context--the lifecycle perspective of the parts that will be manufactured on that machine. This idea is also considered in our research. The last paper identified in the databases dealing with reconfigurable machine-tool design is [57]. It is about applying typical multiobjective optimization problems to measure machine reconfigurability from a modularity perspective and machine capability. It uses genetic algorithms to identify the non-dominated combinations and afterward a multicriteria method to rank Pareto frontiers. The topic of optimization is at the stage of design conceptualization where quantitative parameters are already known; thus, being possible to map the space of possibilities with a classical optimization formalism. The idea to take this aspect in an earlier phase of conceptualization is imported into our research. Our literature survey led us to conclude that, even if many of the design algorithms have merits to be considered for the problem of reconfigurable machine-tool design, some limitations still exist, in the sense that they are too sectorial. In practice, the problem is a bit more complex than in the simplified spaces investigated by the reference papers. These aspects motivated us to investigate new frontiers for knowledge creation and to propose an augmented design algorithm that simultaneously combines computer-driven evolutionary intelligence and human intelligence to formulate a mature solution to a problem.