MSEC2006-21087 VARIATION PROPAGATION ANALYSIS ON COMPLIANT ASSEMBLIES CONSIDERING CONTACT INTERACTION

ABSTRACT Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products is important to understand how it propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly non-linear. Traditional assembly models have represented assembly as a linear process. However, assemblies that include the contact between their components and tools show a highly non-linear response. This paper presents a new assembly methodology considering the contact effect. In addition, an efficient to predict output response is presented. The enhance dimension reduction method (eDR) is used to accurately and efficiently predict the statistical response of the assembly to variation on the input parameters

Variation propagation analysis on compliant assemblies considering contact interaction

Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products it is important to understand how the variation propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly nonlinear. Traditionally, assembly process modeling has been approached as a linear process. However, many assemblies undergo highly complex nonlinear physical processes, such as compliant deformation, contact interaction, and welding thermal deformation. This paper presents a new variation propagation methodology considering the compliant contact effect, which will be analyzed through nonlinear frictional contact analysis. Its variation prediction will be accurately and efficiently conducted using an enhanced dimension reduction method. A case study is presented to show the applicability of the proposed methodology

Quality monitoring and fault detection on stamped parts using DCA and LDA image recognition techniques

New vision technologies provide an opportunity for fast detection and diagnosis of quality problems compared with traditional dimensional measurement techniques. This paper proposes a new use of image processing to detect quality faults using images traditionally obtained to guide manufacturing processes. The proposed method utilizes face recognition tools to eliminate the need of specific feature detection on determining out-of-specification parts. The algorithm is trained with previously classified images. New images are then classified into two groups, healthy and unhealthy. This paper proposes a method that combines Discrete Cosine Transform (DCT) with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) to detect faults, such as cracks, directly from sheet metal parts. Copyright © 2008 by ASME

Part-by-part dimensional error compensation in compliant sheet metal assembly processes

Dimensional variation in assembly processes is one of the most important issues that affect quality. Although robust design and statistical process quality control help to reduce this problem, they cannot be used for instant variation reduction during assembly operations, especially during process ramp-up. This paper introduces a complete methodology for dimensional-related error compensation in compliant sheet metal assembly processes. The proposed methodology is divided into two steps: (1) an off-line error control-learning module using virtual assembly models to determine necessary adjustments; and (2) an in-line control implementation using a feed-forward control strategy based on the learned adjustments. The off-line learning step focuses on determining control actions or corrections to compensate for the negative effects incoming part errors have on Key Product Characteristics. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine optimal control actions. The in-line feed-forward control identifies appropriate part-by-part adjustments using these learned control actions and incoming assembly component measurements. In this paper, two case studies are presented. First, a mathematical case study presents an empirical proof for the feasibility of the Iterative Sampling and Fitting Algorithm. Second, a simulation-based case study illustrates the effectiveness of the proposed methodology to improve dimensional quality in assembly operations for compliant sheet metal parts

Dimensional error compensation in compliant assembly processes using virtual assembly training

Dimensional variation propagation and accumulation in multistage manufacturing processes are among the most important issues that affect quality. Although robust design and statistical process quality control help to reduce the effects of these problems, neither of these two methods can be used for instant variation reduction during assembly operations. This paper introduces a complete methodology for error compensation in compliant sheet metal assembly processes. The proposed methodology can be divided in two steps: (1) an off-line error control-learning module using virtual assembly models, and (2), an in-line control implementation using a feedforward control strategy. The off-line learning method focuses on determining the optimal control actions or corrections to a set of predefined deviations. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine an optimal control action. The in-line feedforward control uses measurements of incoming assembly components to select an appropriate pre- learned control action. Two case studies are presented; first, a mathematical case study is used as the empirical proof for the feasibility of the iterative sampling and fitting algorithm. Second, a simulation-based case study is used to illustrate the effectiveness of the proposed methodology to improve dimensional quality in assembly operations of compliant sheet metal parts. Copyright © 2008 by ASME

Impact of fixture design on sheet metal assembly variation

This paper presents a new fixture design methodology for sheet metal assembly processes. It focuses on the impact of fixture position on the dimensional quality of sheet metal parts after assembly by considering the effect of part variation, tooling variation and assembly springback. An optimization algorithm combines finite element analysis and nonlinear programming methods to determine the optimal fixture position such that assembly variation is minimized. The optimized fixture layout enables significant reduction in assembly variation due to part and tooling variation. A case study is presented to illustrate the optimization procedure