A Quick Reaction System Using Energy, Process and Quality Data for Process Characterization and Holistic Monitoring of Large Scale Assembly Lines

2017 ◽  
Vol 871 ◽  
pp. 44-51
Author(s):  
Christian Sand ◽  
Florian Renz ◽  
Akin Cüneyt Aslanpinar ◽  
Jörg Franke
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Reaction System ◽  
Quality Data ◽  
Assembly Lines ◽  
Major Step ◽  
Electromagnetic Actuators ◽  
Energy Data ◽  
Process Characterization ◽  
Using Data

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.

Data Mining zur Nacharbeitsdauerprognose*/Data Mining-based forecasting of rework duration - Predictive rework control and work process optimization in the automotive assembly

2017 ◽  
Vol 107 (10) ◽  
pp. 773-778
Author(s):  
S. Krzoska ◽  
M. Eickelmann ◽  
J. Schmitt ◽  
J. Prof. Deuse
Keyword(s):  
Data Mining ◽  
Process Optimization ◽  
Quality Data ◽  
Operating Process ◽  
Automotive Assembly ◽  
Related Quality ◽  
Mining Methods ◽  
Product And Process ◽  
Using Data ◽  
Manufacturing Execution

Der Fachbeitrag zeigt am Beispiel der Nacharbeitssteuerung und Arbeitsprozessoptimierung in der Automobilmontage, wie produkt- und prozessbezogene Qualitätsdaten durch den Einsatz von Data Mining-Methoden analysiert sowie effizient genutzt werden können. Dazu wurden Daten aus Manufacturing-Execution-Systemen (MES) mithilfe von Regressionsbäumen zur Entwicklung einer fahrzeugspezifischen Nacharbeitsdauerprognose ausgewertet. Das grundlegende Data Mining-Konzept sowie die Pilotierungsergebnisse werden nachfolgend dargestellt.   The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

Data Mining Prospects in Mobile Social Networks

2013 ◽  
pp. 145-172 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Zhongshi He ◽  
Huazheng Zhu
Keyword(s):  
Data Mining ◽  
Social Networking ◽  
Large Scale ◽  
New Technologies ◽  
Data Mining Techniques ◽  
Large Scale Data ◽  
Social Technologies ◽  
New Ideas ◽  
Using Data ◽  
Fundamental Insight

Unable to accommodating new technologies, including social technology, mobile devices and computing are other potential problems, which are significant challenges to social-networking service. The very broad range of such social-networking challenges and problems are demanding advanced and dynamic tools. Therefore, in this chapter, we introduced and discussed data mining prospects to overcome the traditional social-networking challenges and problems, which led to optimization of MSNs application and performances. The proposed method infers defining and investigating social-networking problems using data mining techniques and algorithms based on the large-scale data. The approach is also exploring the possible potential of users and systems contexts, which leads to mine the personal contexts such as the users’ locations and situations from the mobile logs. In these sections, we discussed and introduced new ideas on social technologies, data mining techniques and algorithm’s prospects, social technology’s key functional and performances, which include social analysis, security and fraud detections by presenting a brief analysis, and modeling based descriptions. The approach also empirically discussed using the real survey data, which the result showed how data mining vitally significant to explore MSNs performance and its crosscutting impacts. Finally, this chapter provides fundamental insight to researchers and practitioners who need to know data mining prospects and techniques to analyze large, complex and frequently changing data. This chapter is also providing a state-of-the-art of data mining techniques and algorithm’s dynamic prospects.

Visualization and Analysis of 3D Images Using Data Mining Approaches

2020 ◽  
pp. 289-295
Author(s):  
Parimala Boobalan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Realistic Model ◽  
Natural Sciences ◽  
The Internet ◽  
3D Images ◽  
Using Data ◽  
Numeric Data ◽  
Innovative Techniques ◽  
Insight Into

With the recent advancements in supercomputer technologies, large-scale, high-precision, and realistic model 3D simulations have been dominant in the field of solar-terrestrial physics, virtual reality, and health. Since 3D numeric data generated through simulation contain more valuable information than available in the past, innovative techniques for efficiently extracting such useful information are being required. One such technique is visualization—the process of turning phenomena, events, or relations not directly visible to the human eye into a visible form. Visualizing numeric data generated by observation equipment, simulations, and other means is an effective way of gaining intuitive insight into an overall picture of the data of interest. Meanwhile, data mining is known as the art of extracting valuable information from a large amount of data relative to finance, marketing, the internet, and natural sciences, and enhancing that information to knowledge.

Using data-mining to identify and study patterns in lexical innovation on the web

2018 ◽  
Vol 25 (1) ◽  
pp. 174-200
Author(s):  
Daphné Kerremans ◽  
Jelena Prokić ◽  
Quirin Würschinger ◽  
Hans-Jörg Schmid
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Web Mining ◽  
The Internet ◽  
Mining Tool ◽  
Lexical Innovation ◽  
Innovation And Diffusion ◽  
Using Data ◽  
And Diffusion ◽  
The Web

Abstract This paper presents the NeoCrawler – a tailor-made webcrawler, which identifies and retrieves neologisms from the Internet and systematically monitors the use of detected neologisms on the web by means of weekly searches. It enables researchers to use the web as a corpus in order to investigate the dynamics of lexical innovation on a large-scale and systematic basis. The NeoCrawler represents an innovative web-mining tool which opens up new opportunities for linguists to tackle a number of unresolved and under-researched issues in the field of lexical innovation. This paper presents the design as well as the most important characteristics of two modules, the Discoverer and the Observer, with regard to the usage-based study of lexical innovation and diffusion.

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