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A Methodology for Product Development in Mobile Machinery: Case Example of an Excavator

Department of Machine Design, LUT University, 53850 Lappeenranta, Finland
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Machines 2019, 7(4), 70; https://doi.org/10.3390/machines7040070
Received: 13 October 2019 / Revised: 4 November 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
Digital tools have become indispensable for the testing and modification of prototypes in mobile and industrial machine manufacturing. Data that are extracted from virtual experimentation and analysis are both affordable and valuable, due to their repeatability and because they are close to real-world observations. Expert knowledge is a prerequisite for full deployment of computer aided engineering tools in the design phase and concomitant stages of product development. Currently, such knowledge, for the most part, is provided by the product development team and the manufacturer. Yet, it is important that manufacturers and designers receive end-user feedback throughout the product development process. However, end-users often lack sufficient know-how about the technical and engineering background of the product development, and this lack of understanding can become a barrier to user-designer communication. The aim of this article is to present an alternative to traditional design approaches that is based on customized real-time multibody simulation. This simulation-based approach can be seen as a platform that has the potential to improve knowledge management systems for product development. End-user feedback to the designer is given in a systematic manner throughout the design process using a multipurpose XML-based multibody environment. View Full-Text
Keywords: multibody dynamics; product development; real-time simulation; product-service system; modular design multibody dynamics; product development; real-time simulation; product-service system; modular design
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Alaei, N.; Kurvinen, E.; Mikkola, A. A Methodology for Product Development in Mobile Machinery: Case Example of an Excavator. Machines 2019, 7, 70.

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