Faurecia (France)

For manual wheelchair users, overuse of the upper limbs can cause upper limb musculoskeletal disorders, which can lead to a loss of autonomy. The main objective of this study was to quantify the risk level of musculoskeletal disorders of different slope propulsions in manual wheelchair users using fuzzy logic. In total, 17 spinal cord injury participants were recruited. Each participant completed six passages on a motorized treadmill, the inclination of which varied between (0° to 4.8°). A motion capture system associated with instrumented wheels of a wheelchair was used. Using a biomechanical model of the upper limb and the fuzzy logic method, an Articular Discomfort Index (ADI) was developed. We observed an increase in articular discomfort during propulsion on a slope with increasing discomfort at the shoulder, elbow and wrist, due to an increase in kinetics. There was a kinetically significant change in the kinetic global ADI (22 to 25%) and no change in the kinematic. The ADI increased from 14 to 36% during slope propulsion for each joint. The quantification of the level of discomfort helps us to highlight the situations with the most high-risk exposures and to identify the parameters responsible for this discomfort.

A ceramic monolith risks breakage during production of the exhaust systems in the automotive industry. This is due to its position at a specific angle throughout the canning phase (stuffing technique). To overcome this problem, quality control needs to be automated on each brick. This control aims to adjust, if needed, the positioning of the brick before starting the production. This paper applies image processing techniques following the Canny-Hough method and reaches more than 99% of good detection of straight lines within a tolerance of ±5 degrees, as requested by the plant. Some vision parameters (gain, exposure time and aperture range), have been tested in order to have a better visibility of the reference part. Furthermore, a repeatability test is validated in this paper, allowing the algorithm to be deployed in the plant. The dataset is accessible on the following link: https://doi.org/10.5281/zenodo.5948822

Industrial companies are more and more interested in the use of artificial intelligence (AI) in the control and monitoring of their processes. They try to take advantage of the power of this technology in order to increase the level of automation and to build smarter machines with new capabilities of self-adaptation and self-control. Especially, the automotive industry, with their high requirements in productivity and diversity management, are eager to adapt AI concepts to their processes. However, the training of Deep Learning (DL) models requires an important effort of data preparation, providing a dataset of all possible configurations. Indeed, this dataset must be collected and then annotated. Considering the fact that automotive industry deals with a huge number of references and that it often and quickly needs to modify their products, it is very difficult, if not impossible, to gather sufficient datasets for each produced reference and to have the time to train DL models in the plants with the traditional methods. This paper presents an innovative methodology to prepare the dataset by creating virtual images instead of collecting real ones and then automatically annotating them. It will demonstrate that this method will reduce the efforts and the time of the preparation of the dataset significantly. The paper will also present how this method was deployed for the quality control of welding operations in the automotive industry.

Future shared robot taxis should reduce traffic congestion in cities, and in order to design services adapted to the needs of users, the sources of comfort and discomfort must be specified. In order to project people into the use of this future mobility, the technique of Guided Imaginary Projection was used with 40 men and women between 22 and 66 years of age. It made it possible to specify the effect produced by the absence of a driver, a driver who usually takes on the role of mediator who reassures, organizes and manages the unexpected events. Recommendations for the design of such services were drafted.

Managing the new product development (NPD) is a challenging mission, and most researches would argue that design is fundamentally linked to intentional action and it cannot emerge out of complexity. In fact, its complexity is generated by a large number of entities and actors which cooperate simultaneously with an unpredictable way to understand what customers want and then design product with diverse objectives in mind. A slight change in one activity may cause tremors everywhere. Within a dynamic environment and in order to meet concurrently these challenges, several researchers have implemented design for X (DFX) techniques. Regarding the availability of numerous DFX, the decision as to which one to apply remains absent. Hence, the purpose of this paper is to present a comprehensive overview of the most prominent DFX techniques with respect to sustainability dimension as well as the cost ownership and product differentiation strategies. In addition to that, complex product necessitates the consideration of integrated DFX to optimize product life cycle from a more holistic perspective. In this respect, the paper addresses a systematic review from 1980 to 2018 by investigating and discussing the past and current research of each DFX techniques as well as for integrated ones. The key problems and issues that future DFX research should address have been identified and discussed in this paper.

With the fast-ongoing evolution towards C.A.R.E. vehicles (Connected, Automated, Ride-Sharing, Electrified) interior arrangements are deeply transformed. Both seats and surfaces as part of the interior are becoming populated with actuators and sensors in order to make them truly adaptive and predictive to the various situations encountered while driving. This evolution leads to the perception of the full interior as an extensive “Human Machine Interaction (HMI)” system providing customized comfort, safety, smart and enjoyable life-on-board in all situations. The main benefit for the car users is utilizing the driving time for productive activities in continuity with their occupations outside of the car. This paper explains the end-to-end architecture (“Cockpit Intelligence Platform”—CIP) views of Faurecia for such holistic interior HMI solutions. Faurecia is a worldwide leader in car interiors which has created an extensive partnership network in the last years in order to meet these new challenges. Faurecia demonstrated its concrete solutions and innovations through prototypes and demonstrators at the Frankfurt Motor Show 2017, Las Vegas CES 2018 and recently at the Paris Motor Show, where the company attracted a considerable attention from the automotive community.

The amine cure of cyclic carbonate blends, derived from renewable resources and carbon dioxide, in the presence of a liquid fluorohydrocarbon as physical blowing agent with no ozone depletion potential, enables the facile tailoring of flexible bio‐based nonisocyanate polyurethane (NIPU) foams. Unlike conventional PU foams, neither isocyanates nor phosgene or aromatic amines are required as intermediates in NIPU foaming. Typically, rigid cyclic carbonates such as carbonated trimethylolpropane glycidylether (TMPGC) are blended together with the corresponding flexible cyclic carbonate such as ethoxylated TMPGC (EO‐TMPGC) which lowers monomer viscosity and reactivity. This is reflected by higher pot life and gelation times for the cure with hexamethylene diamine (HMDA), improving NIPU foam processing. With increasing EO‐TMPGC content, rigid TMPGC/HMDA‐NIPU foams are rendered flexible and soft, as verified by simultaneously declining storage modulus and glass transition temperature. In this NIPU foam family, the TMPGC/EO‐TMPGC (60 wt%/40 wt%) blend cured with HMDA in the presence of Solkane 365/227 affords flexible NIPU foams exhibiting low density, very good mechanical hysteresis, and tailored hardness, meeting the demands of various applications like automotive seating. Emission tests confirm the absence of critical compounds mentioned in the global automotive declarable substance list. image