Optimization approach for the combined planning and control of an agile assembly system for electric vehicles

For some years now, the automotive industry has been challenged by growing market dynamics, shorter product lifecycles and customers' increasing demands for individualization. In order to cope with this development, the automotive assembly needs to adapt quickly to changing demands with a low level of investment in the future. Under the current circumstances, the traditional line assembly for high volume production is reaching its limits in terms of adaptability and scalability. A promising solution to address the current challenges is the concept of the agile assembly. The concept of agile assembly breaks up the rigid linkage of assembly stations and, thus, enables full flexibility in the sequence of assembly operations only limited by the precedence graph. Therefore, the routing of electric vehicles in the agile assembly is based on the availability of resources such as assembly stations and automated guided vehicles that handle the material supply. Further, by transferring the transport function to the vehicle itself, investments for convey or systems are eliminated. This research work presents an optimization approach for the machine scheduling and transportation planning, which derives instructions for electric vehicles, assembly stations as well as automated guided vehicles. For each electric vehicle, an optimized route is calculated, taking into account product-specific precedence graphs and minimizing the overall makespan. In addition, the machine scheduling and transportation planning is integrated into a combined planning and control concept which covers the allocation of resources and the assignment of capabilities of the entire assembly system. The approach is implemented and applied to a practical case of a compact electric vehicle. Thus, the work contributes to the evaluation of agile assembly systems in automotive production

Virtual Asset Representation for enabling Adaptive Assembly at the Example of Electric Vehicle Production

Manufacturing companies are confronted with the challenge of adapting to ever-changing requirements of markets in order to remain competitive. Besides the rising number of product variants, increasingly frequent product changes require a continuous adaptation of assembly processes including its work instructions. Adaptive and highly connected agile assembly systems are designed to meet these challenges by enabling the interaction of various assets in assembly. A successful implementation of such Industry 4.0 (I4.0) solutions requires the development of a semantic oriented adaptive framework, which connects the physical with the virtual world. It enables interactive and situation-aware solutions such as Augmented Reality applications to adapt to worker capabilities and to improve worker satisfaction by providing information, based on individual experience, skills and personal preferences. A central part of the adaptive framework is the semantic representation of tangible and intangible assets through a Virtual Asset Representation containing all relevant asset information for adaptive assembly. This paper shows a three levels structure for adaptive assembly implementation, consisting of the adaptive framework level, the Virtual Asset Representation (VAR) ontology level and the use case level. The implementation of an adaptive assembly system is shown in the use case of a rear light assembly process of an electric vehicle in the context of the EU funded project A4BLUE. Based on the gained experiences a critical reflection on target fulfilment and user-friendliness of the VAR is given