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The product development process within the automotive industry is subject to changing demands due to internal and external influences. These influences and adjustments especially affect the car body and its inherent joining technology, as critical stages of variant creation. However, current literature does not offer a suitable analytical method to identify and assess these critical influences. We propose an advanced analytics approach that combines data mining and machine learning techniques within the car body substructure. The evaluation within the Mercedes-Benz AG shows that our approach facilitates a quantitative assessment of unknown interdependencies between car body modules and corresponding joining techniques. [ABSTRACT FROM AUTHOR]

The product development process within the automotive industry is subject to changing and varying demands due to external influences and internal strategic decisions. Influences and decisions, such as a shift from hybrid vehicles to fully electric architectures, turn into explicit requirements. For a manufacturer to remain competitive, these requirements need to be fulfilled within the early design phases of the development process. The car body substructure and body shop in particular have the highest interdependence with the automotive product development process. To reduce the resulting complexity of managing various requirements for each car body type, car manufacturers use modular design to create partitioned subassemblies. Although there is a wide range of strategies for the modularization of individual requirements, none of them offers the possibility of considering multiple influences and identifying interdependencies between formed modules. To solve this problem, we provide a structured approach for the early planning and design phases as means of achieving this multi-criteria modularization. We transform results from the requirement elicitation into dependency matrices to weight, compare and synchronize critical and relevant requirements. Thus, it is possible to analyze interdependencies to gain knowledge for the design of new product and production modules. In cooperation with the Mercedes-Benz Cars division, we validate our approach and create a domain-specific blueprint for car body substructures. Therefore, we are able to deepen the understanding, give part-specific recommendations and provide a new decomposition of modules of future vehicle architectures. [ABSTRACT FROM AUTHOR]

Compressed air operated industrial equipment has been always an important part of car body production. Advantages of compressed air powered tools are their robust construction, low-cost manufacturing as well as high performances especially forces at a minimum of construction size. Disadvantages are high operating costs, due to pressure losses in case of leakage, high installation costs and intricate cabling especially at highly flexible manufacturing systems. As a result, many automobile manufacturers aiming for a reduction of air powered production tools or even air free automobile manufacturing systems. Against this background a new concept of a vacuum grippers will be presented, which does not need central compressed air supply. Results of the development of suction cups will be shown, which are based on mechanical air displacement inspired by artificial muscles. Furthermore, a control of clusters of new suction cups with artificial intelligence is given. [ABSTRACT FROM AUTHOR]

Due to various trends, such as e-mobility, a long-term change in the automotive value creation structure is foreseeable. Shorter development cycles demand early prototypes and cause more changes. Currently, there is no cost-effective production process for originally deep drawn sheet metal parts in series quality at small production volumes. In this paper, the application of additively manufactured functional elements in deep drawing tools is investigated. Besides the first concept of such tools, first experimental test series are described and analyzed. [ABSTRACT FROM AUTHOR]

Fixture and clamping technologies of automated production lines for structural components in vehicles turned out as the main cost driver with up to 29 % of total investment costs of the production equipment. Additionally, they limit the product flexibility for product variants and change requests during the product development process. Flexible production concepts, like jigless joining with component-integrated jig features (CJF) might be a promising approach to reduce component specific jig technology. Technical as well as economical aspects are investigated on a low-cost battery pack housing for electric vehicles and are compared to conventional concepts concerning annual production volumes. [ABSTRACT FROM AUTHOR]

There are five major shops in an automotive factory, namely, press shop, body shop, power train shop, paint shop, and trim shop (general assembly shop). When an automotive factory develops a new car model, new production lines should be constructed at the same time, and the body shop especially needs to be entirely rebuilt whenever a new car model is developed. The body shop is a typical example of the flow shop (or transfer line). Many parts are assembled together in the body shop by various welding operations. Therefore, the cycle time of each robot and the buffer size between two sub-lines should be determined in the design phase of the body shop. This paper explains a case study of a body shop design using 3D simulation in a Korean automotive factory. 3D simulation is useful in verifying if the factory design would meet the production quantity target. It also helps in understanding the structure of the factory. QUEST®, IGRIP®, and Factory-CAD® are used for the 3D simulation model. [ABSTRACT FROM AUTHOR]

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC.

The automotive body shop meets numerous new requirements in the context of electric mobility. Various production concepts currently exist to meet these requirements. Particularly, flexible solutions are gaining in importance. However, the actual effects on body shop are unclear. The aim of this paper is therefore to develop an evaluation framework for the flexibility of production concepts in body shop. First, general and specific requirements for an innovative body shop are derived and characteristics of production concepts in body shop identified. Subsequently, a correlation analysis between requirements and characteristics lays the basis for a conception and validation of possible production concepts. [ABSTRACT FROM AUTHOR]