Jaime A. Camelio

Modeling Variation Propagation of Multi-Station Assembly Systems With Compliant Parts

Products made of compliant sheet metals are widely used in automotive, aerospace, appliance and electronics industries. One of the most important challenges for the assembly process with compliant parts is dimensional quality, which affects product functionality and performance. This paper develops a methodology to evaluate the dimensional variation propagation in a multi-station compliant assembly system based on linear mechanics and a state space representation. Three sources of variation: part variation, fixture variation and welding gun variation are analyzed. The proposed method is illustrated through a case study on an automotive body assembly process.

A Review of Engineering Research in Sustainable Manufacturing

Karl R. Haapala 1 School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331 e-mail: Karl.Haapala@oregonstate.edu Fu Zhao School of Mechanical Engineering, Division of Environmental and Ecological Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907 e-mail: fzhao@purdue.edu Jaime Camelio Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 235 Durham Hall, Blacksburg, VA 24061 e-mail: jcamelio@vt.edu John W. Sutherland Division of Environmental and Ecological Engineering, Purdue University, 322 Potter Engineering Center, West Lafayette, IN 47907 e-mail: jwsuther@purdue.edu Steven J. Skerlos Department of Mechanical Engineering, University of Michigan, 2250 GG Brown Building, Ann Arbor, MI 48105 e-mail: skerlos@umich.edu David A. Dornfeld Department of Mechanical Engineering, University of California, 6143 Etcheverry Hall, Berkeley, CA 94720 e-mail: dornfeld@berkeley.edu I. S. Jawahir Department of Mechanical Engineering, University of Kentucky, 414C UK Center for Manufacturing, Lexington, KY 40506 e-mail: jawahir@engr.uky.edu A Review of Engineering Research in Sustainable Manufacturing Sustainable manufacturing requires simultaneous consideration of economic, environmen- tal, and social implications associated with the production and delivery of goods. Funda- mentally, sustainable manufacturing relies on descriptive metrics, advanced decision- making, and public policy for implementation, evaluation, and feedback. In this paper, recent research into concepts, methods, and tools for sustainable manufacturing is explored. At the manufacturing process level, engineering research has addressed issues related to planning, development, analysis, and improvement of processes. At a manufac- turing systems level, engineering research has addressed challenges relating to facility operation, production planning and scheduling, and supply chain design. Though economi- cally vital, manufacturing processes and systems have retained the negative image of being inefficient, polluting, and dangerous. Industrial and academic researchers are re- imagining manufacturing as a source of innovation to meet society’s future needs by under- taking strategic activities focused on sustainable processes and systems. Despite recent developments in decision making and process- and systems-level research, many chal- lenges and opportunities remain. Several of these challenges relevant to manufacturing process and system research, development, implementation, and education are highlighted. [DOI: 10.1115/1.4024040] Andres F. Clarens Department of Civil and Environmental Engineering, University of Virginia, D220 Thornton Hall, Charlottesville, VA 22904 e-mail: aclarens@virginia.edu Jeremy L. Rickli Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 217 Durham Hall, Blacksburg, VA 24061 e-mail: jlrickli@vt.edu Corresponding author. Contributed by the Manufacturing Engineering Division of ASME for publication in the J OURNAL OF M ANUFACTURING S CIENCE AND E NGINEERING . Manuscript received July 11, 2012; final manuscript received March 4, 2013; published online July 17, 2013. Editor: Y. Lawrence Yao. Manufacturing and Sustainability The concept of sustainability emerged from a series of meetings and reports in the 1970s and 1980s, and was largely motivated by environmental incidents and disasters as well as fears about Journal of Manufacturing Science and Engineering C 2013 by ASME Copyright V AUGUST 2013, Vol. 135 / 041013-1 Downloaded From: http://manufacturingscience.asmedigitalcollection.asme.org/ on 07/09/2014 Terms of Use: http://asme.org/terms

Compliant Assembly Variation Analysis Using Component Geometric Covariance

Dimensional variation is one of the most critical issues in the design of assembled products. This is especially true for the assembly of compliant parts since clamping and joining during assembly may introduce additional variation due to part deformation and springback. This paper discusses the effect of geometric covariance in the calculation of assembly variation of compliant parts. A new method is proposed for predicting compliant assembly variation using the component geometric covariance. It combines the use of principal component analysis (PCA) and finite element analysis in estimating the effect of part/component variation on assembly variation. PCA is used to extract deformation patterns from production data, decomposing the component covariance into the individual contributions of these deformation patterns. Finite element analysis is used to determine the effect of each deformation pattern over the assembly variation. The proposed methodology can significantly reduce the computational effort required in variation analysis of compliant assemblies. A case study is presented to illustrate the methodology.

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.

A Review and Perspective on Control Charting with Image Data

Machine-vision systems are increasingly being used in industrial applications due to their ability to provide not only dimensional information but also information on product geometry, surface defects, surface finish, and other product and process characteristics. There are a number of applications of control charts for these high-dimensional image data to detect changes in process performance and to increase process efficiency. We review the control charts that have been proposed for use with image data in industry and in some medical-device applications and discuss their advantages and disadvantages in some cases. In addition, we highlight some application opportunities available in the use of control charts with image data and provide some advice to practitioners.

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.

Modeling and Control of Compliant Assembly Systems

The assembly of compliant, non-rigid parts is widely used in automotive, aerospace, electronics, and appliance manufacturing. Dimensional variation is one important measure of quality in such assembly. This paper presents models for analyzing the propagation of dimensional variation in multi-stage compliant assembly systems and the use of such models for robust design and adaptive control of assembly quality. The models combine engineering structure analysis with advanced statistical methods in considering the effect part variation, tooling variation, as well as part deformation due to clamping, joining and springback. The new adaptive control algorithm makes use of the fine adjustment capabilities in new programmable tooling in achieving reduction of assembly variation.

Value flow characterization during product lifecycle to assist in recovery decisions

The value of a product at the end of its useful life determines whether it is disposed, recycled, remanufactured or is handled some other way within the recovery infrastructure. This value changes over time, and depends on factors such as the quality of the product and its perception by business entities within the recovery infrastructure. Under the assumption that the last user wants to maximize economic gain, these factors can be considered as uncertainties that affect the decision of the last user with regard to product disposition. Since the choice made by the last user has a significant effect on the environmental impact of a product, it is important to understand the value of the product over its lifecycle and thus its end-of-life value. A model is proposed in this paper to characterize the value flow during the product lifecycle. The model considers three product lifecycle stages: (i) manufacturing (the value creation stage); (ii) usage (value consumption stage); (iii) recovery/post-use (value reclamation stage). The role of product attributes and product usage history on the value flow across the product lifecycle is investigated. In addition, the perception-dependent nature of product value is explored and related to utility theory. The model has application in decisions related to product use and recovery; an example is presented to demonstrate the use of the value model in selecting the best product recovery option.

Multiple Fault Diagnosis for Sheet Metal Fixtures Using Designated Component Analysis

This paper presents a new approach to multiple fault diagnosis for sheet metal fixtures using designated component analysis (DCA). DCAfirst defines a set of patterns based on product/process information, then finds the significance of these patterns from the measurement data and maps them to a particular set of faults. Existing diagnostics methods has been mainly developed for rigid-body-based 3-2-1 locating scheme. Here an N-2-1 locating scheme is considered since sheet metal parts are compliant. The proposed methodology integrates on-line measurement data, part geometry, fixture layout and sensor layout in detecting simultaneous multiplefixture faults. A diagnosability discussion for the different type of faults is presented. Finally, an application of the proposed method is presented through a computer simulation. @DOI: 10.1115/1.1643076 # Fixtures are used to locate and hold workpiece in manufacturing. In general, fixture elements can be classified by their functionality into locators and clamps. Locators establish the datum reference frame and provide kinematic restraint. Clamps provide additional restraint by holding the part in position under the application of external forces during the manufacturing process. A 3-2-1 locating scheme is used to uniquely locate a rigid body, constraining the six degrees of freedom of the part. According to this principle, three locators are placed in the primary plane, two in the secondary plane and one in the tertiary plane. However, for compliant sheet metal parts, Cai et al. @1# showed that an N-2-1 principle is more adequate and is widely used in industry. The N-2-1 fixture principle establishes that to locate and support compliant sheet metal parts, it is necessary to provide more than 3 locators in the primary plane due to part flexibility. In general, fixture failure directly affects part location and assembly dimensional quality. Ceglarek and Shi @2# found that during the launch of a new vehicle, fixture faults represent around 70% of all dimensional faults. Consequently, adequate fixture failure diagnosis can positively impact dimensional quality. Due to existence of new measurement systems, such as optical coordinate measurement machine ~OCMM !, a large amount of dimensional data can be obtained from manufacturing processes. Therefore, new opportunities for process diagnosis are available. Several authors have studied fixture diagnosis in the last few years. In general, past research in fixture diagnosis is based on three major approaches: principal component analysis, correlation clustering and least square regression. In 1992, Hu and Wu @3# introduced the principal component analysis ~PCA ! to identify sources of dimensional variation in automotive body assembly. They used PCA to extract variation patterns from dimensional data. Later, Ceglarek and Shi @4# proposed a fixture fault diagnosis method combining PCA with pattern recognition. They developed variation patterns for each hypothetical fault based on the fixture and measurement sensor layout. Then, they used principal component analysis to extract variation modes from production data and map the modes with the hypothetical variation patterns. This method focused on single assembly fixture failure. Ding et al. @5# developed a diagnosis method based on a state space dimensional variation model for multistage manufacturing processes using PCA. In addition, Ceglarek and Shi @6# include considerations of measurement noise in fixture failure diagnosis. Correlation clustering is able to detect multiple dimensional faults by matching a model behavior with the measured behavior. Shiu et al. @7# developed a multi-station assembly modeling for diagnostics in automotive body assembly process. The model is based on critical characteristics such as the locating mechanism ~fixture to part interactions ! and the joining conditions ~part to part interactions !. The variation patterns are expressed by a correlation matrix. Then, the correlation matrix from measurement data is compared with the different simulated correlated matrices associated with the faults. Multivariate process diagnosis has also been studied using a least squares approach. The approach consists of relating the measurement variation patterns to potential causes. The least squares method is used to identify the significance of each of these potential causes @8–12 #. Apley and Shi @9# used least squares algorithm to identify the significance or severity of multiple fixture faults. Fault severity was measured as the variance of the decomposition of the data for each variation pattern. The variation patterns where determined as the effect a fixture element fault over the measurement points.

Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks?

Recent cyberattacks have highlighted the risk of physical equipment operating outside designed tolerances to produce catastrophic failures. A related threat is cyberattacks that change the design and manufacturing of a machines part, such as an automobile brake component, so it no longer functions properly. These risks stem from the lack of cyber-physical models to identify ongoing attacks as well as the lack of rigorous application of known cybersecurity best practices. To protect manufacturing processes in the future, research will be needed on a number of critical cyber-physical manufacturing security topics.