In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product

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... ies, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Abstract Precision machine tools are usually installed on foundations via supporting elements. In this configuration, errors because of inappropriate support lengths (level error) have to be adjusted during the installation to align the level of the machine tool. Consequently, a quantitative indication of level errors is a necessity for levelling works. Therefore, a model-based approach for level condition monitoring, which can identify level errors, is proposed. In the model, machine supports are modeled as linear springs. The relationship between the level error and preload distribution is modeled in matrix form. This report verifies the fundamentals of the proposed approach through experiments performed on a machining center. Abstract Precision machine tools are usually installed on foundations via supporting elements. In this configuration, errors because of inappropriate support lengths (level error) have to be adjusted during the installation to align the level of the machine tool. Consequently, a quantitative indication of level errors is a necessity for levelling works. Therefore, a model-based approach for level condition monitoring, which can identify level errors, is proposed. In the model, machine supports are modeled as linear springs. The relationship between the level error and preload distribution is modeled in matrix form. This report verifies the fundamentals of the proposed approach through experiments performed on a machining center.