scholarly journals Data Driven Root Cause Analyses in Multistage Manufacturing Utilising Life Cycle Wide Product Information

2019 ◽  
Vol 26 (4) ◽  
2020 ◽  
Vol 110 (07-08) ◽  
pp. 532-535
Author(s):  
Eckhart Uhlmann ◽  
Roman Dumitrescu ◽  
Julian Polte ◽  
Maurice Meyer ◽  
Deniz Simsek

Die Zuverlässigkeit von Werkzeugmaschinen ist ein kritischer Faktor für den Erfolg produzierender Unternehmen. Durch die Analyse von Daten in der Produktplanung können Maschinenhersteller Ausfallursachen eliminieren und Maschinen systematisch verbessern. Jedoch stellt eine umfassende Datenanalyse viele Unternehmen vor große Herausforderungen. Die in diesem Beitrag vorgestellte Methodik adressiert diese Problematik und unterstützt Unternehmen bei der zielgerichteten Datenanalyse.   The reliability of machine tools is a critical factor for the success of manufacturing companies. By analyzing data in product planning, machine manufacturers can eliminate causes of failure and systematically improve machines. However, comprehensive data analysis poses great challenges for many companies. The methodology presented in this paper addresses this problem and supports companies in the goal-driven data analysis.


Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.


Fuel ◽  
2019 ◽  
Vol 246 ◽  
pp. 187-195
Author(s):  
Fanxu Meng ◽  
Carolyn LaFleur ◽  
Asanga Wijesinghe ◽  
John Colvin

Author(s):  
Anantha Narayanan ◽  
Paul Witherell ◽  
Jae Hyun Lee ◽  
K. C. Morris ◽  
Sudarsan Rachuri

Materials play a central role in product manufacturing, contributing to each phase of product development in the form of either a component or process material. As the product revolves around materials, so does much of the product information. Material information plays a significant role in the decision making process at any stage of the product life cycle, especially with respect to the sustainability of a product. Material information in the manufacturing stages of a product’s life cycle will relate to the processes used in manufacturing and assembling individual components. The material properties may determine what processes can be used and how these processes should be controlled. To support sustainable manufacturing, the impacts of material choice should be considered during design, when resources are being committed. When comparing material alternatives at design time, it is not as simple as saying one material is “more sustainable” than another. Many different factors determine the sustainability of a product, and each of these factors may be influenced by multiple material properties represented through various information requirements. In order to develop a material information model that can satisfy these information requirements, we need to carefully study the requirements from an information modeling perspective. In this paper, we use activity models to describe design and manufacturing scenarios that rely on the availability of proper material information for sustainability decision making. We will use these models to first define specific scenarios and then to identify the types of material information that is typically required in these scenarios, and collect and categorize key concepts. Based on this study, we will make recommendations that will aid the development of a useful material information model for sustainable decision making.


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