Concept for Automated Fuse and Relay Assembly in the Field of Cable Harness Manufacturing - Heisler, Paul; Sand, Christian; Hefner, Florian; Süß-Wolf, Robert; Franke, Jörg (RWTH Aachen 2017 – 7. WGP-Jahreskongress, 2017)

Concept for a virtual process data linkage of assembly stations and a dynamic envelope curve for process monitoring - Sand, Christian; Meiners, Moritz; Daberkow, Jonas; Franke, Jörg (2016 6th International Electric Drives Production Conference, EDPC 2016 - Proceedings, 2016)
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.
CRIS-Team Scopus Importer:2023-03-07
Keywords: assembly machines; data mining; envelope curve; process optimization

Concept for a virtual process data linkage of assembly stations and a dynamic envelope curve for process monitoring - Sand, Christian; Meiners, Moritz; Daberkow, Jonas; Franke, Jörg (Electric Drives Production Conference (EDPC), 2016 6th International, 2016)

Concept for Magnet Intra Logistics and Assembly Supporting the Improvement of Running Characteristics of Permanent Magnet Synchronous Motors - Meyer, Alexander Wilhelm; Heyder, Andreas; Kühl, Alexander; Sand, Christian; Gehb, Hermann; Abersfelder, Sandra; Franke, Jörg; Holzhey, Rocco; Buettner, Ulrich; Wangemann, Sebastian (Procedia CIRP, 2016)
Current scientific studies prove the dependency of the running characteristics of permanent magnet synchronous motors on variations of the physical properties of permanent magnets and manufacturing errors. Currently, this fact is not considered in most process chains for magnet and rotor assembly. Thus, insufficient rotor quality can only be detected during the end-of-line test, causing rejects of whole motors. As a solution for these drawbacks, a computerized magnet intra-logistics storage and assembly concept is presented, using sophisticated magnet measurement technologies. This concept allows for the traceability of single magnets and enables to compensate variations of the magnetic properties by selective magnet assembly.
CRIS-Team Scopus Importer:2023-03-07
Keywords: magnet assembly; magnetic tolerancing; PMSM; tolerancing; traction drives

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

Evaluation of energy measurements for error detection in press-fitting processes - Sand, Christian; Meiners, Moritz; Eberle, Jonathan; Franke, Jörg (2018 2nd International Symposium on Small-Scale Intelligent Manufacturing Systems, SIMS 2018, 2018)
Modern assembly lines consist of many different stations like assembly presses, which are rigidly chained or stand-alone on the shop floor. Current state-of-the-art process monitoring regards force-way curves to classify good or defective process runs using tolerance windows or envelop curves. Yet, approaches for online monitoring based on energy data are hardly used in assembly presses. Therefore, avoidable failure and scrap costs, as well as resource and material waste occur due to commonly unobserved malicious process states. This paper evaluates which types of errors can be detected and if an energy-based process observation is a useful addition or alternative to conventional process monitoring. Here, relevant information is used to generate deeper insight into assembly processes. Impacts like altered material, different batches of supply parts, tool wear and tool breakage shall be revealed by utilizing electrical current curves. Therefore, assembly settings are varied by six impact factors to evaluate their significance on force-displacement-curves. Eventually, a methodology on how to detect and compare key points in characteristic curves is proposed when evaluating energy data acquisition with current-displacement-curves thus presenting possible prospective research topics on deeper data analysis with data mining methods.
CRIS-Team Scopus Importer:2023-03-07
Keywords: current-displacement-curve; DoE; Force-displacement-curve; press-fit connection

Evaluation of energy measurements for error detection in press-fitting processes - Sand, Christian; Meiners, Moritz; Eberle, Jonathan; Franke, Jörg (Small-scale Intelligent Manufacturing Systems (SIMS), 2018 2nd International Symposium on, 2018)

Holistic production analysis for actuator manufacturing using data mining - Sand, Christian; Bogus, Kim; Kunz, Sabrina; Franke, Jörg (2016 6th International Electric Drives Production Conference, EDPC 2016 - Proceedings, 2016)
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.
CRIS-Team Scopus Importer:2023-03-07
Keywords: Actuator; Data Mining; Optimization; Tool Wear

Potentials for Energy Saving and Quality Improvement of Assembly Presses Using Data Mining - Sand, Christian; Seidl, Matthias; Leinauer, Christian; Neuner, Maximilian; Meiners, Moritz; Baumann, Stephanie; Franke, Jörg (Applied Mechanics and Materials (Volume 856), 2016)

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

Potentials for Error Detection and Quality Improvement in Assembly Lines Using FFT, Clustering and Dynamic Envelope Curve - Sand, Christian; Kawan, Stephanie; Lechler, Tobias; Neher, Manuel; Schweigert, Daniel; Franke, Jörg (Applied Mechanics and Materials - Energy Efficiency in Strategy of Sustainable Production III, 2017)
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.<br
Keywords: Fourier Transform; Clustering; Data Mining; Dynamic Envelope Curve

Towards an inline quick reaction system for actuator manufacturing using data mining - Sand, Christian; Kunz, Sabrina; Hubbert, Henning; Franke, Jörg (2016 6th International Electric Drives Production Conference, EDPC 2016 - Proceedings, 2016)
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.
CRIS-Team Scopus Importer:2023-03-07
Keywords: anomaly detection; cluster analysis; data mining; decision trees; fast reaction system; jump detection; magnetic actuators; outliers; process optimization; root cause analysis