Prevailing trends such as increasing pressure in terms of cost, quality and time require manufacturing companies to continuously optimize both the production environment and engineering. Currently, the planning and engineering of new production systems for the final automotive assembly requires considerable effort. One effective method to increase engineering efficiency is the reuse of production system modules. However, due to the constantly growing number of possible mechatronic systems as well as the volatile framework conditions of the production environment, the question arises as to which modules are suitable for reuse. In this context, this paper presents an approach for analyzing and comparing the reusability of modules. Based on roughly planned reuse cases and the reuse of individual artifacts, the effectiveness and efficiency of reuse is analyzed. In addition, based on future scenarios of the assembly environment, the risk of potential reconfigurations of the modules is considered. The approach is validated using the example of a representative use case of the final automotive assembly.

In Production Systems Engineering (PSE), domain experts aim at reusing production processes implemented as Industry 4.0 assets and software.However, the knowledge on reusable assets is often scattered on multi-disciplinary engineering artifacts and domain experts, making it hard to find suitable reusable assets and map them to requirements.In this paper, we (i) identify challenges and requirements for reuse in PSE based on a domain analysis; (ii) introduce the Industry 4.0 Asset Network (I4AN) that integrates multi-disciplinary dependencies between the assets and exposes recurring patterns; and (iii) present four patterns for reuse in PSE that aim at improving reuse efficiency and risk.We evaluate the I4AN with reuse scenarios in a feasibility study.The study results indicate that the I4AN model satisfies the elicited requirements and enables PSE domain experts to identify patterns for reuse in their contexts.

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.

Due to the increasing individualization of products by the customer and shorter product lifecycles, there is an increasing number of variants in assembly lines in the final automotive assembly. As a result, the planning of flexible machinery poses a highly complex and time-consuming process while requiring supporting tools and methods. The planning can be facilitated by the automation of the engineering process of the plant planning. In this context this paper presents an approach for the attribution of product, process, resource and skill (PPRS) for the final automotive assembly. The objective is the generation of an optimal resource for both product and process. In order to increase the PPRS attribution in terms of efficiency, an aggregation of similar products and processes within a re-source occurs. This aggregation process additionally includes a holistic consideration of all impacts on the resource.

Optimal selection of resources for a Cyber-Physical Production System (CPPS) has a strong potential to reduce the cost of producing product families. Unfortunately, engineers rarely have specific methods and tools to represent their expert knowledge for applying mathematical optimization approaches. Representing this knowledge requires capabilities to model the dependencies between products, production processes, resources, skills, and their variability. In this paper, we provide these capabilities and focus on improving CPPS resource selection by defining aggregated requirements of a set of similar processes for resources. We illustrate the industrial use case Car Body with Screwed-on Parts on defining requirements for resource candidates suitable for a set of screwing process variants. We introduce the Product-Process-Resource-Skill & Variability (PPRS+V) method for consistently designing aggregated Product-Process-Resource (PPR) skills required for optimal CPPS resource selection as foundation for engineering support. We introduce the Skill Aggregation algorithm to efficiently aggregate the skills for a process step required to produce a product family. In an initial feasibility study, domain experts found the PPRS+V method and the Skill Aggregation algorithm usable and useful.