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Product development and product manufacturing are entering a new era, namely an era where engineering tasks are executed under collaboration of all involved parties. Engineers and potential customers work together mainly in a virtual world for the design and realization of the product. We address this so called “crowdsourcing” trend in the automotive industry that lowers cost and accelerates production of new car. Current practice and prior studies fail to handle data management and collaboration aspects in sufficient detail. We propose a PLM based crowdsourcing platform that applies best practices to the established approach and adapt it with new methods for handling specific requirements. Our work provides a basis for establishing an improved collaboration platform to support a Gig Economy in the automotive industry.
It is widely recognized that sustainability is a new challenge for many manufacturing companies. In this paper, we tackle this issue by presenting an approach that deals with material and substance compliance within Product Lifecycle Management in a complex value chain. Our analysis explains why, how and when sustainable manufacturing arises, and it identifies, quantifies and evaluates the environmental impact of a new product. We propose (I) a Life Cycle Assessment tool (LCA) and (II) a model to validate this approach and evaluate the risk of noncompliance in supply chain. Our LCA approach provides comprehensive information on environmental impacts of a product.
Product and materials cycles are parallel and intersecting, making it challenging to integrate Material Selection Process across Product Lifecycle Management, Integration of LCA with PLM. We provide only a foundation. Further research in systems engineering is necessary. LCA is sensitive to data quality. Outsourcing production and having problems in supplier cooperation can result in material mismatch (such as property, composition mismatching) in the production process due to that may cause misleading of LCA results.
This paper also describes research challenges using riskbased due diligence.
The paper investigates an innovative actuator combination based on the magnetic shape memory technology. The actuator is composed of an electromagnet, which is activated to produce motion, and a magnetic shape memory element, which is used passively to yield multistability, i.e. the possibility of holding a position without input power. Based on the experimental open-loop frequency characterization of the actuator, a position controller is developed and tested in several experiments.
Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resolution images with neural networks is still an open problem. Existing methods separate inpainting of global structure and the transfer of details, which leads to blurry results and loss of global coherence in the detail transfer step. Based on advances in texture synthesis using CNNs we propose patch-based image inpainting by a single network topology that is able to optimize for global as well as detail texture statistics. Our method is capable of filling large inpainting regions, oftentimes exceeding quality of comparable methods for images of high-resolution (2048x2048px). For reference patch look-up we propose to use the same summary statistics that are used in the inpainting process.
In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to include B-spline curve approximation directly into the neural network architecture. The resulting parametrizations yield tight approximations and are able to outperform state-of-the-art methods.
Der DEX Deutscher Ethik Index® gibt Auskunft darüber, ob der Erfolg eines Unternehmens auf anständige Weise erreicht wurde. Zielgruppe des veröffentlichten DEX sind alle Anspruchsgruppen (Stakeholder) und die Gesellschaft. Der DEX informiert darüber, ob das Unternehmen einen Mehrwert für alle seine Stakeholder schafft (= Stakeholder Value). Der DEX unterstützt Entscheidungen der Öffentlichkeit, ob einer Organisation aufgrund ihres Beitrags für die Gesellschaft die „License to Operate“ erteilt wird (= Shared Value).
SDG Voyager - A practical guide to align business excellence with Sustainable Development Goals
(2018)
By now, an inflationary high number of international publications on the topic “Agenda 2030” exist. But unanswered to this day seems to be the question of how the CSR-management of a company can make a concrete contribution to the SDGs. Instead of unilaterally demanding the reporting of companies’ sustainability activities, the SDG Voyager starts earlier in the process with the intention of encouraging companies of all sizes to become familiar with the fields of action for corporate responsibility and to attend to these issues without feeling overwhelmed. Many companies will find that they are already making a big contribution to sustainable development in a number of fields. In other areas, however, there will still be an urgent need for action. The SDG Voyager aims to acquaint companies with these topics and support them to fulfill their responsibilities towards their stakeholders and society.