Evaluation of effectiveness and impact of decentralized automation

Wagner, Thomas; Schertl, Andreas; Elger, Jürgen; Vollmar, Jan

doi:10.1109/etfa.2008.4638539

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…With respect to engineering processes different views and business models can be considered [4]. One of these businesses is the solution business which covers the development of production systems for a special purpose, e.g.…”

Section: Motivationmentioning

confidence: 99%

Aggregation of engineering processes regarding the mechatronic approach

et al. 2011

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Within the last few years different process structures for the execution of engineering processes in the field of production systems have been developed and partially standardized. These processes have strong similarities and dependencies enabling the development of a generalized engineering process for production systems. In addition the different engineering processes can highlight the use of mechatronical units. This paper will survey existing engineering processes and will make a first attempt to define such a mechatronic oriented generalized engineering process for production systems. Thereby, it intends to provide a starting point for further discussion on this subject.

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Section: Motivationmentioning

confidence: 99%

Aggregation of engineering processes regarding the mechatronic approach

et al. 2011

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“…The concept of automation began in this revolution which immensely helped mass production (David, 2017;Dunk, 1992;Kuric et al, 2018;Wu et al, 2015a). With the innovation of computers, several manufacturing concepts were progressed, cumulating the production effectiveness (Azzone & Bertele, 2007;Clegg et al, 2010;Gorecky et al, 2014;Hudson, 1982;Singh et al, 2013;Wagner et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

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For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

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