The image displays variation simulation in the program RD&T
Perceived Quality (PQ) signifies how a customer interprets quality with her/his senses. This theme focuses on how to develop products with an accurate level of PQ for the intended customer and product segment. This involves understanding the customers in order to find the adequate level, verifying PQ virtually and also understanding the impact on PQ from other attributes.
Theme leader is Dr. Casper Wickman, Volvo Car Group/Chalmers
Product specifications to
reach PQ can be very expensive, which means that sub-attributes within
PQ need to be optimally balanced during product development. New methods
and tools, to track PQ progress in developing programs in combination
with qualitative and quantitative customer data, will be developed to
support this process in the theme. Automated data gathering methods,
connected products, and data sharing increase the availability of data.
This enhances the possibilities to understand customer behaviour and
interaction with other attributes. The theme also intends to investigate
how adjacent attributes and customer interaction indirectly affect
interpretation of PQ.
Important research areas
- Customer demands and requirement for improved PQ
- Simulation and visualization of visual PQ
- Squeak and rattle (S&R) simulation
Casper Wickman is also responsible for the Squeak & Rattle Competence Arena, a forum that focuses on simulation and verification of S&R.
Ongoing research questions
Research Challenges (RCs) and preliminary Research Questions (RQs) addressed in the theme are:
Research challenge 1: To secure correct virtual verification of PQ while meeting shorter development time and reduction of physical series
RQ1: How can part behaviour based on specifications and knowledge be gathered and stored as behavioural data in PLM system and be used for automated verification of PQ? (SE, GA)
RQ2: How can CAD and meta data be used in combination with variation simulation for early risk detection of PQ? (SE, GA)
RQ3: How can virtual and augmented reality be used to verify PQ with high confidence? (GA)
Research challenge 2: To understand customer needs from different markets and for different product segments in order to rank importance of PQ attributes.
RQ4: How can ground attributes of PQ be ranked using different data collection methods? (SE, GA)
RQ5: How does PQ ranging differ between markets and car segments? (GA)