Christian Sand
University of Erlangen-Nuremberg
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Featured researches published by Christian Sand.
Applied Mechanics and Materials | 2018
Christian Sand; Tobias Lechler; Patricia Schuh; Jörg Franke
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.
Applied Mechanics and Materials | 2017
Christian Sand; Dominik Manke; Jörg Franke
The advance of digitalization changes the requirements of processes in industrial production and assembly. For this reason, production and assembly must now be able to execute complex process steps. This is about quality and productivity expectations, as well as flexibility and reliability of production, lines and plants [1]. Today, data is generated by almost every system, machine and sensor, yet it is hardly used for process optimization. Manufacturing processes are usually organized as workshop production or chained production systems, in addition to standalone machines [2,3]. Most analytic projects focus on chained systems and serial production, unlike individual machines and specific workshop production. Depending on manufacturing IT, process data from serial production is stored in data bases, which are usually optimized for traceability. Standalone machines and machines within workshop production are scarcely connected to a common data base. The required process data is stored either on the module itself or inside a local data base [4]. The identification of dependencies between individual assembly processes, energy data and the quality of the finished product is necessary for an extended optimization. These optimizations can be process-specific, as well as environmental and resource related. Due to decentralized process data storages, an overall view of a dynamic order-oriented value chain is denied. Therefore, the potential of the machines is largely unused. Based on Data Mining, this advanced development can be counteracted by process monitoring and optimization. Therefore, this paper provides a solution for a virtual process data linkage of assembly stations. This enables the acquisition, processing, transformation and storage of unstructured raw data by special software and methods, which is also able to cope with chained production systems and standalone machines. For further analysis of interdependencies, a visualization is developed for advanced monitoring and optimization [5,6].
Applied Mechanics and Materials | 2017
Christian Sand; Florian Renz; Akin Cüneyt Aslanpinar; Jörg Franke
Modern large-scale assembly lines need to deliver a highly varied and flexible output, while achieving 0 ppm scrap. This is becoming more and more demanding due to an increasing complexity of the products. Thus, it will be a major step in manufacturing processes to develop process monitoring strategies which increase productivity as well as flexibility and reliability of the entire assembly process. Therefore, it is necessary to advance the entire chained assembly line instead of only isolated processes and stations. For this reason, technological processes have to be assessed as a chain of upstream and downstream partial processes instead of being considered in isolation. [3] Moreover, data mining projects depend on the available data bases, while additional data sources may increase the derived knowledge. [2] These ideas are extendable by energy data measurements, besides process and quality data. Existing monitoring approaches to reduce scrap usually use dashboards linked with process and quality data. [5] Therefore, this paper presents a new methodology using data mining analysis of energy data for assembly presses as well as complete assembly lines for electromagnetic actuators. This novel holistic approach realized by a Quick Reaction System allows to increase efficiency, while decreasing energy and resource consumption for actuator manufacturing on large scale assembly lines. In particular, the data base consists of process and quality data, enriched by energy data measurements. This approach enables a comprehensive process characterization as well as monitoring of whole assembly lines by using data mining tools. Furthermore, this paper describes a quantitative evaluation of its data mining based event detection of critical process parameters.
Applied Mechanics and Materials | 2017
Christian Sand; Stephanie Kawan; Tobias Lechler; Manuel Neher; Daniel Schweigert; Jörg Franke
Conventional serial and workshop productions use specific parameter ranges to evaluate the quality of a process. Our research showed that parameters within tolerances do not ensure good quality of the final product due to malicious parameter combinations along the assembly line. Therefore, data sets from assembly processes like force-way or force-time curves and quality measurements are evaluated in this novel approach. Using Fourier Transform, k-means, decision trees and a dynamic envelope curve, classification and process monitoring are processed in time and frequency domain. This enables new possibilities to characterize quality and process data, for advanced error detection as well as a more simplified tracing of faults. Here, holistic optimization and monitoring follows two strategies. First, a simplified tracing approach of malicious impacts regards quality results from test benches. Therefore, assembly processes are monitored and characterized by quality data. Second, defective influences, like tool break or calibration errors, are linked to variations of the usual process behavior. Here, the error detection approach focuses on process data from single assembly stations. This approach uses three different methods. First, Fourier Transform extracts additional information from process, energy and quality data. Second, k-means algorithm is used to cluster quality data and extend the data base. Third, a decision tree classifies the quality of the final good and characterizes assembly processes. Last, results of k-means clustering and selected classification methods are compared. This combination allows to increase process quality, improve product quality and reduce failure costs.
international electric drives production conference | 2016
Christian Sand; Sabrina Kunz; Henning Hubbert; Jörg Franke
Large-scale production lines aim to realize 0 ppm defects. This is getting more and more complicated, due to all the so far achieved process optimizations. However, our research showed that a huge amount of unpredictable disturbance variables influences production systems, which promote defects. Here, the modelling of every single influence like temperature, machine condition, tool wear and quality of supplied parts is almost impossible, regarding a fully automated assembly line for actuators. Yet conventional methods for process optimization like Six Sigma, Kaizen, etc. usually focus on single processes and are not suited for quick reactions when disturbances occur during manufacture. Therefore, we created and evaluated a novel method based on data mining. To speed up failure detection, process data and testing results as well as batch information and new methods are required. This paper introduces an inline anomaly detection system to automatically highlight critical conditions with very low delay. Here, three independent systems analyze the data in order to detect jumps and outliers of process values and to find an anomalous distribution of defective parts within processes. For further investigations of detected malicious conditions an efficient root cause analysis for a whole production line including assembly and quality processes is introduced, which uses clustering and decision trees. Based on the detected anomalies of the system, we propose cluster algorithms to discover complex combinations of malicious process influences on the quality of the final product.
international electric drives production conference | 2016
Christian Sand; Moritz Meiners; Jonas Daberkow; Jörg Franke
Industrial manufacturing and assembly aim to realize a wide range of product variance at high quality standards. [7] The fabrication processes are commonly organized as workshop production or chained production systems, besides standalone machines. [3][4] A lot of process data is generated by every single machine, yet it is hardly used for process optimization. Depending on the manufacturing IT, process data of series production is stored within databases optimized for traceability, whereas standalone machines and machines within workshop production are usually not connected to a common database. The required process data is either stored on the assembly machine itself or inside a local database. [9] The identification of interdependences of each single assembly process and the quality of the finished good is necessary for advanced optimization. Due to the decentralized process data storage, data mining analysis is taking a huge amount of time to find and prepare the process and quality data, especially in workshop production. To enable process monitoring and holistic optimization based on data mining methods in workshop production, a methodology is required to extract, transform and store process data like pressing curves and quality data. Therefore, this paper provides a concept for a virtual process data linkage of assembly stations to enable data mining inside workshop production, which is also able to cope with chained production systems and standalone machines. For further analysis of interdependencies of assembly presses, a dynamic envelope curve is developed for advanced monitoring and optimization as novel methodology.
international electric drives production conference | 2016
Christian Sand; Kim Bogus; Sabrina Kunz; Jörg Franke
Holistic production optimizations within large-scale productions are not yet used because classic methods like Six Sigma or DoE are less expedient when it comes to huge or more complex data sets. Thus no standardized analysis for integrated production optimization exists, to realize 0 ppm defects. This paper introduces a holistic analytics approach using data mining techniques to reduce the error and scrap rate, addressing experts and non-specialized workers.
ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb | 2016
Christian Sand; David Funk; Stephanie Baumann; Jörg Franke
Kurzfassung Die vorhandenen Tools des Beanstandungsmanagements erfüllen die existierenden Bedürfnisse nicht mehr ausreichend. Der Einsatz von Data Mining bewältigt diese Herausforderungen im Beanstandungsmanagement. Die vorgestellten Data Mining-Konzepte ermöglichen die Prognose der Fehlerkategorie einer Beanstandung. Weiterhin können multivariate Auffälligkeiten im Prozess identifiziert und Risikoabschätzungen hinsichtlich weiterer betroffener Teile innerhalb von Minuten vorgenommen werden.
Applied Mechanics and Materials | 2016
Christian Sand; Matthias Seidl; Christian Leinauer; Maximilian Neuner; Moritz Meiners; Stephanie Baumann; Jörg Franke
Modern assembly lines are usually optimized towards output and tact time as well as process capability and quality. Yet, approaches for energy saving are hardly used in assembly presses. Therefore, assembly lines are using more electric power and compressed air than necessary. Especially at high load, during handling phases and in different idle modes there is a huge potential for energy savings. Current research is focusing on high-power consuming turning and milling machines as well as laser welding. Energy saving projects usually focus on whole factory halls instead of manufacturing lines and single assembly machines. Therefore, this paper presents a new methodology using a top-down-approach and data mining analysis regarding a conventional assembly press as well as a whole assembly line. Here, relevant information types like process data, quality factors, expenditure of energy per produced part and power consumption are used to generate more insight into chained assembly processes. Various tools like energy analysis, process flow and correlation analysis are used to identify focus stations of a whole assembly line for energy saving projects and quality improvements. This novel holistic approach regards the electrical power and compressed air consumption of each relevant station and its machine components during different operating states as well as its correlations between process data, quality factors and energy consumption. Besides tact-time-analysis of the process, the scheduled and unplanned downtimes of the machine are also regarded. Furthermore, it enables predictions of tool wear and breakdown, quality impacts of supplied parts, as well as energy savings on process and machine level. Due to an increased quality, the material efficiency may rise as well.
Procedia CIRP | 2016
Alexander Meyer; Andreas Heyder; Alexander Kühl; Christian Sand; Hermann Gehb; Sandra Abersfelder; Jörg Franke; Rocco Holzhey; Ulrich Büttner; Sebastian Wangemann