Laboratoire Conception de Produits et Innovation

This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young’s modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value.

This paper presents a computational design assistant that supports novice designers willing to evaluate the additive manufacturability of their ideas. The design rules extractor for additive manufacturability (DREAM) uses natural language processing techniques to analyse a textual description contained in an ideation card so as to recommend applicable design rules. The design rules for additive manufacturing are structured in an ontology, and the selection of applicable ones depends on their semantic proximity to sentences of the idea card. The empirical evaluation of DREAM led to the identification of six applicable design rules among which two were false positives, which are due to short sentences containing non-discriminant terms. Future works will concentrate on the evaluation with a larger industrial dataset for continuous improvement. Finally, we will analyse the sketches in the idea card and suggest design improvements based on the opportunities offered by additive manufacturing.

Abstract In the age of the fourth industrial revolution, the competition between enterprises is fierce to operate efficiently and hold on to their customers. Due to lack of time and methodology, many leaders struggle to establish optimized strategies for their businesses. To do so, the key processes need to be measured using dashboards that proactively help decisions making, facilitate the strategy execution and keep the employees focused. The purpose of this article is to identify the design features of a risk analysis dashboard and their effects on the perceived ease of use and perceived usefulness.

Design in the healthcare environment is challenging. More than in the other activity sectors, it requires to take the users (patients and medical staff) as experts in the loop. This article will describe the bases of a new design method integrating TRIZ concepts for the healthcare environment. A first case study will be presented on the design of an exoskeleton specialized in the assistance of hemiplegic patients during their re-education.

Additive manufacturing is an innovative manufacturing process that enables rapid manufacturing of functional products and parts. On the other side, considering environmental aspects in design is beneficial as it leads to lower costs, improved product quality, new business opportunities. Thus, in order to foster the potential of AM in product innovation and product manufacturing in the light of environmental concerns, a new design method is necessary. This paper proposes a method in the context of Design with Additive Manufacturing, to take into account the specificities of this manufacturing process in a Design to Environment approach. The method is focused on the Early Design Stages (EDS) of the product development process, which are crucial not only for choices regarding the product characteristics but also for the environmental parameters that need to be taken into consideration. The implementation of the proposed method in creativity session of the EDS underlined the need for dedicated supports in terms of environmental decisions. More and specifically the need for providing tools to capitalize the decisions made focusing on each Life Cycle Stage of the product was identified as a requirement for this support.

The deployment of additive manufacturing (AM) processes had a rapid and broad increase in the last years, and the same trend is expected to hold in the near future. A way to better exploit the advantages of such technology is the use of appropriate information tools. However, today there is a lack of software applications devoted to this innovative manufacturing process. To overcome this issue, in the present work the application of manufacturing execution systems (MES), a tool commonly used in traditional manufacturing processes, is extended to AM. Furthermore, a framework for the deployment of shop-floor data, acquired through a monitoring system, in the design phase is presented: hence, MES should cooperate with design for additive manufacturing (DFAM), a set of methods and tools helpful to design a product and its manufacturing process taking into account AM specificities from the early design stages. In order to better understand the advantages of such cooperation, a case study for a proof of concept has been developed: the obtained results are promising, thus an online implementation would be recommended.