Data analysis and error detection in assembly lines using a three-dimensional visualization technique - Sand, Christian; Schuh, Patricia; Lechler, Tobias; Franke, Jörg (2017 7th International Electric Drives Production Conference (EDPC), 2017)
Usually, specific parameter ranges or tolerances are used to evaluate the quality of a process in serial production and especially in assembly processes [1]. Despite met tolerances along the whole assembly process, defective parts are produced due to malicious process parameter combinations. This paper presents a novel three- dimensional visualization technique to analyze and evaluate data from assembly processes and quality measurements in actuator manufacturing. It reveals correlations between process, energy and quality data in order to identify events which influence the quality of the final goo

Potentials for Error Detection and Process Visualization in Assembly Lines Using a Parallel Coordinates Plot - Sand, Christian; Lechler, Tobias; Schuh, Patricia; Franke, Jörg (Applied Mechanics and Materials - Energy Efficiency in Strategy of Sustainable Production IV, 2018)
Assembly lines consist of chained or unchained stations, yet usually only single stations are regarded individually for process and quality analytics. Since the quality of the final product depends on interactions of process parameters along the assembly flow, it is insufficient to analyze process parameters of each station separately. Therefore, data of every single assembly station along the assembly line has to be collected and stored. To explore such a big amount of multidimensional data and their correlations, different techniques are established. In this paper, assembly flows and their respective data are visualized using a parallel coordinates plot (PCP). Here, this technique visualizes process parameter combinations along the whole assembly chain. The contribution of this paper is to prove that the presented approach enables a fast detection of stations with malicious impacts on the product quality, when it comes to complex assembly lines. The goal is to help users to detect global problems in those lines, not only single station problems. Furthermore, the relevance of various processes to the quality (good or defective) of the final good shall be revealed.
Keywords: Clustering; Error Detection; Assembly Lines; Parallel Coordinates; Data Mining