Einsatz von Data-Mining in modernen Produktentstehungsprozessen

Breitsprecher, Thilo; Kestel, Philipp; Küster, Christof; Sprügel, Tobias; Wartzack, Sandro

doi:10.3139/104.111423

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Numerous literature reviews have also been conducted in specific areas of system design. Shabestari et al [21] explore the use of machine learning in the early stages of product development, while Breitsprecher et al [22] and Burggräf et al [23] investigate knowledge-based problem solving and Data Mining in modern product development processes. Trauer et al [24] evaluate industrial climate system developers and identify several opportunities for data-driven design throughout the product development process, with a particular focus on system design.…”

Section: System Designmentioning

confidence: 99%

Digital Engineering Methods in Practical Use during Mechatronic Design Processes

Gerschütz

Sauer

Kormann

et al. 2023

Designs

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This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

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Section: System Designmentioning

confidence: 99%

Digital Engineering Methods in Practical Use during Mechatronic Design Processes

Gerschütz

Sauer

Kormann

et al. 2023

Designs

Self Cite

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“…KBS use this acquired knowledge and present it to the developer in a single architecture. Existing approaches and applications of using data mining for automated knowledge acquisition in modern product development process are presented by Breitsprecher et al (2015). The KBS architecture usually consists of the elements user interface (UI), inference mechanism, knowledge base, knowledge acquisition, explanation and context facility (Styczynski et al, 2017).…”

Section: Knowledge-based Product Developmentmentioning

confidence: 99%

Data Driven Product Portfolio Analysis of Electric Motors Based on Product Platforms Using Knowledge-Based Systems

chsen

Pop

Koch

et al. 2019

Proc. Int. Conf. Eng. Des.

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For a company it is necessary to know, which products can be configured using carry-over-parts or the same technology. This can become quite relevant in the context of automobile electrification, where complex mechatronic systems are used. Consisting of various mechanical components, these systems perform the required function while being actuated by electronically controlled motors. To solve this, a novel mechanism for data driven portfolio analysis based on product platforms using knowledge-based systems is proposed in this paper. Further, the mechanism is tested by applying it to an electrical motors' case study. Using this method, all possible combinations of a product platform are calculated and finally displayed in different product portfolios. Additionally, all the non-feasible motor designs are removed from the solutions portfolio using the acquired knowledge base and performing design checks. The latter are employed for penalising and eliminating from the pareto-front of the designs, which violate the thermal, mechanical and acoustic constraints. The generated product portfolio can be used further to expand the systems engineering collaboration and support decision-making.

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