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This paper presents the latest development of using an integrated modeling approach to estimate the statistical ranges of key vehicle dynamics performance in the early design phase. The virtual analytical tools predict the statistical confidence interval for specified ride and handling (R&H) metrics to enable a robust design by concurrently simulating the dimensional tolerance of the structural parts as well as the compliance variation. The compliance variation can be defined as load deflection properties of bushings as well as vehicle weight effects on preload. The model can then be used to better represent real world customer experience, allowing prediction of performance ranges relative to targets. In order to better predict these targets, measurements of physical vehicles were made and compared to the model to reveal the actual interactions relative to the theoretical.

During component development of multiple fastener bolted joints, it was observed that one or two fasteners had a higher potential to slip when compared to other fasteners in the same joint. This condition indicated that uneven distribution of the service loads was occurring in the bolted joints. The need for an optimization tool was identified that would take into account various objectives and constraints based on real world design conditions. The objective of this paper is to present a method developed to determine optimized multiple fastener locations within a bolted joint for achieving evenly distributed loads across the fasteners during multiple load events. The method integrates finite element analysis (FEA) with optimization software using multi-objective optimization algorithms. Multiple constraints were also considered for the optimization analysis. In use, each bolted joint is subjected to multiple service load conditions (load cases).

Dimensional variation simulation is a computer aided engineering (CAE) method that analyzes the statistical efforts of the component variation to the quality of the final assembly. The traditional tolerance analysis method and commercial CAE software are often based on the assumptions of the rigid part assembly. However, the vehicle functional attributes, such as, ride and handling, NVH, durability and reliability, require understanding the assembly quality under various dynamic conditions while achieving vehicle dimensional clearance targets. This paper presents the methods in evaluating and analyzing the impacts of the assembly variations for the vehicle dynamic performance. Basic linear tolerance stack method and advanced study that applies various CAE tools for the virtual quality analysis in the product and process design will be discussed.

Dimensional variation analysis is a critical step of the dimensional management process for understanding the predicted dimensional variation from manufacturing process. Through analysis, product and process capabilities were evaluated, and the variation can be predicted and documented at each step of the build. The objective to develop the concurrent virtual dimensional analytical (CVDA) methods was to concurrently design a product and manufacturing process insensitive to part and process variations by integrating various analytical tools in literatures and in industry practice. The allowable variation targets of the product that meet the functional requirements are defined with the geometric dimensioning and tolerancing (GD&T) drawings. If at all possible, each part’s datum features defined on the drawing are required to be coordinated with the locating features of production tools.

Design for Six Sigma (DFSS) is a product improvement process based on statistical problem solving capabilities which is typically followed IDDOV approach - Identify, Define, Develop, Optimize, and Verify the design. Flexible inspection system (FIS) is defined as one where the inspection routines are not fixed but are determined just prior to performing the inspection [1]. In FIS the inspection stations have the capacity of performing different inspection routines according to a global inspection strategy. In this paper, the IDDOV steps, as well as some DFSS variation analysis techniques, are applied with the FIS to provide an analytical framework for an optimized strategy of real time inspection allocations.

The concept of design for six sigma (DFSS) offers a framework to design a product and process right the first time. In general, Taguchi's robust design method has been widely adapted in design optimization, which is a critical phase in any DFSS projects. The objective of the paper is to develop an advanced strategy in selecting an optimized product design and manufacturing process that should be insensitive to various multivariate variation patterns of the multi-stage manufacturing system. A Monte Carlo variation simulation based method is presented that integrates Mohalanobis Distance (MD) method, a discriminant analysis technique, to analyze the manufacturing variation patterns detected by using the multivariate statistical tool, such as principal component analysis (PCA). The proposed method will be explained with an example of an automotive assembly.