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Title: Towards sustainable luxury materials selection : measuring the perceived quality of automotive interior materials : innovation report
Author: Newton, Claudia
ISNI:       0000 0004 7431 6833
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2017
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Automotive companies are searching for new, innovative materials that attempt to redefine what is traditionally associated as a ‘luxury material’. Market research shows that future customers will demand tangible sustainability in vehicle interiors through the use of eco-friendly materials. However, research has also identified customer scepticism towards the quality of green products sold by luxury brands. The perception of quality is typically determined by peripheral and sensorial product properties such as styling, shape and touch. The uncertainty of new materials compounded by the need to balance sustainability, sensory and emotional appeal mean it is no longer possible to rely on the designers’ intuition and experience to evaluate materials. Rigorous, robust methods which include both objective material assessments and the quantification of subjective, sensory and experiential attributes will maximize the chance of successful adoption by customers. They can also offer further insight, such as demonstrating that the Perceived Quality (PQ) of a cheaper material can be improved just by making the material softer using a foam backing, as was found in this research. To address this, a new process has been developed to measure the perceived haptic quality of soft automotive interior materials. Studies were conducted in the UK and Hong Kong to generate user-defined metrics. Of these metrics, roughness and hardness had the largest impact on PQ, so mechanical testing was conducted to obtain objective measurements of both. The subjective and objective measurements were found to correlate strongly, implying that objective measurements alone could indicate a customer’s opinion of these materials. The final stage of the process introduces a statistical model which uses the objective data to predict PQ scores. This is based around an Artificial Neural Network validated as accurate to within 4.5%. A graphical user interface was designed so practitioners can use the model to predict how customers may respond to a new material or a change in the surface characteristics of an existing material, without needing to conduct the initial customer research. The process has been integrated in part within the sponsor company and has influenced future research and business strategy in this area.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council ; Jaguar Land Rover
Qualification Name: Thesis (D.Eng.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TL Motor vehicles. Aeronautics. Astronautics