Combining information flow and physics-of-failure in mechatronic products

A continuous acceleration in the rate of technological development, shorter product life cycles, more intense competition due to maturing of markets and globalization, have forced firms to increasingly rely on new products for sales and profitability. However, reliability and quality management becomes extremely difficult and challenging in such circumstances, as products have to be on the market before the manufacturer knows and is able to control their long-term behavior. Despite the many efforts to predict reliability in the course of the product development process, it is not unlikely to see deviation between predicted and real product performance in the field. Therefore, companies need to react proactively to such deviations. Doing so implies the development of field feedback control loops, which measure field reliability and provide enough information for both corrective actions in existing products and preventive actions in future products. Such a development of reliability field feedback control loop requires, however, considerable efforts for improving existing field feedback systems, since those systems are traditionally focused on logistics of product repair. The difficulty is that failures of complex products are strongly influenced by the product-user interaction. In such a context, the field information should not anymore solely focus on parts/components failures but also on the user-product interaction. First, a literature review on field feedback systems, reliability and quality of information was conducted. This literature review enabled to establish a set of criteria which field feedback information should fulfill, namely: time, deployment, format and content. As a second step the design and definition of an analysis system for reliability-oriented field feedback information has been carried out. Once the criteria mentioned above had been established, a case study was performed to assess the quality of the existing field feedback process in an innovative company, in relation to certain products available on the market. Using the developed system, this case study identified the different classes of failures per product, using the classification defined in the roller-coaster model (i.e. class one: infant mortality; class two: early wearout; class three: random; class four: wear-out). It was found that some products experienced a dominant number of class one and class two failures, while other products experienced none of these failures without the producer realizing. These failures were, despite their importance for the company, not taken into account for quality improvement. Class one failures are traditionally tackled through the implementation of adequate quality control on manufacturing processes and have therefore not been subject to a specific analysis in the course of this thesis. Class two failures concern a distinct subpopulation of products showing an accelerated degradation in performance, caused either by product variability (usually internal flaw caused by manufacturing process) or by customer use variability (customer using the product in extreme/unexpected conditions). To prevent reoccurrence of these failures, the design needs to be revised. Pursuant to this case study, it was noticed that the field feedback information was relevant but not, per se, suitable for root-cause analysis and could not, in itself, allow design improvement. A method for bridging the gap between the available field feedback information and the information actually needed for design improvement was therefore necessary. From a theoretical perspective, the problem should be tackled using a synthesis of two existing fields of reliability engineering: system engineering and physics-of-failure. The system engineering approach aims at understanding the behavior of and interaction among systems components. The physics-of-failure is a discipline that focuses on the understanding of the physical processes of failure at a detailed level (i.e. component level). This method is suited for analyzing a failure mechanism and improving the design but is far more complex once applied to a complete product because of the too many potential failure mechanisms to be studied. A new method was therefore suggested consisting in the combination of field feedback information (Top–down approach) and physics-of-failure (Bottom-up approach). The physics-of-failure provides analytical models, which explain individual failure mechanisms. The field feedback information, in particular analysis of failed product, also provides significant clues to guide the identification of the relevant system components and the selection of the most likely failure mechanisms to be studied. Application of the first step of the method resulted, as was expected, not to unambiguous identification of dominant failure mechanisms, but gave, as was the intention, a clear first priority. Subsequent experiments were then performed to confirm, validate or reject the occurrence of this failure mechanism. Parameters were selected, based on product knowledge, comparative study with other products, and correlation between design and potential failure mechanism. As a next step the experiments executed under controlled conditions were compared, on effect level, to dominant field failures. Such iterative process was carried out until a correlation with field failed products could be established. Finally, once the failure mechanism was properly identified and understood, the design optimization phase was undertaken. The method was applied successfully, and demonstrated that design improvement should be prioritized according to the class of failure occurring on products available on the market. In particular, class two failures can be analyzed and reproduced at design level, so that it is possible to predict failure and adequate design modifications can be suggested. In this study, the method has been implemented on "low medium capital industry and consumers" products that present certain characteristics. However, it is expected that the method could be applied to different products and industries under certain conditions.