Modular Product Verifications Based on Design for Assembly

The desire to conquer markets through advanced product design and trendy business strategies are still predominant approaches in industry today. In fact, product development has acquired an ever more central role in the strategic planning of companies, and it has extended its influence to R&D funding levels as well. It is not surprising that many national R&D project frameworks within the EU today are dominated by product development topics, leaving production engineering, robotics, and systems on the sidelines. The reasons may be many but, unfortunately, the link between product development and the production processes they cater for are seldom treated in depth. The issue dealt with in this article relates to how product development is applied in order to attain the required production quality levels a company may desire, as well as how one may counter assembly defects and deviations through quantifiable design approaches. It is recognized that product verifications (tests, inspections, etc.) are necessary, but the application of these tactics often result in lead-time extensions and increased costs. Modular architectures improve this by simplifying the verification of the assembled product at module level. Furthermore, since Design for Assembly (DFA) has shown the possibility to identify defective assemblies, it may be possible to detect potential assembly defects already in the product and module design phase. The intention of this paper is to discuss and describe the link between verifications of modular architectures, defects and design for assembly. The paper is based on literature and case studies; tables and diagrams are included with the intention of increasing understanding of the relation between poor designs, defects and product verifications.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.