Evaluation Method and Application of Assembly Yield in Two-Dimension Multi-Station Assembly Processes Based on Number-Theoretical Net

2012 ◽  
Vol 522 ◽  
pp. 921-926
Author(s):  
Ze Jun Wen ◽  
Zheng Qiang Zhu ◽  
Yan Ming Zhao ◽  
Fan Zhang
Keyword(s):  
Evaluation Method ◽  
State Space Model ◽  
Total Sample ◽  
Two Dimension ◽  
Automotive Body ◽  
Variation Propagation ◽  
Net Method ◽  
Good Lattice ◽  
Good Lattice Point ◽  
Component Assembly

Method for calculating assembly yield in two-dimension multi-station assembly processes is developed based on Number-Theoretical Net (NT-net). The discrepancy of NT-net is analyzed, and the principle of generating good lattice point (glp) based on NT-net method is introducted. Afterwards, taking fixture locating variations which are sampled using NT-net method for input vectors, the samples are substituted into state space model of dimension variation propagation in multi-station assembly processes to get output vectors. The statistics for qualified sample is accomplished, after comparing output vectors with the variations of measuring points on component. Assembly yield in two-dimension multi-station assembly processes is gained when qualified sample divided by total sample. Finally, a real case in automotive body floor assembly is given as an example to calculate the assembly yield in two-dimension three-station assembly processes. The result is validated by using Monte carlo simulation. It provides a new way to predict assembly yield in two-dimension multi-station assembly processes.

Evaluation Method of Assembly Yield in Three-Dimension Single-Station Assembly Process Based on Number-Theoretical Net and 3DCS

2013 ◽  
Vol 579-580 ◽  
pp. 270-275
Author(s):  
Ze Jun Wen ◽  
Fan Zhang ◽  
Jia Hu
Keyword(s):  
Evaluation Method ◽  
Three Dimensional ◽  
Total Sample ◽  
Three Dimension ◽  
Assembly Process ◽  
Variation Propagation ◽  
Single Station ◽  
Net Method ◽  
Component Assembly ◽  
Assembly Deviation

Evaluation method of assembly yield in three-dimension single-station assembly process is presented based on Number-Theoretical Net (NT-net) and 3DCS. Firstly, based on the analysis of fixture deviation affect assembly deviation under arbitrary layout, the three dimensional single-station assembly deviation model under arbitrary layout is developed. Then, the principle of generating good lattice point (GLP) based on NT-net method is introduced. Afterwards, taking fixture locating variations which are sampled using NT-net method for input vectors, the samples are substituted into assembly deviation model of dimension variation propagation in single-station assembly processes to get output vectors. The statistics for qualified sample is accomplished, after comparing output vectors with the variations of measuring points on component. Assembly yield in three-dimension single-station assembly processes is gained when qualified sample divided by total sample. Finally, a real case in sheet metal assembly is given as an example to calculate the assembly yield in three-dimension single-station assembly processes. The result is validated by 3DCS.

Method for Estimating Failure Probability of Mechanical Structures Using Number Theoretical Net (NT-net) Simulation

2009 ◽  
Vol 628-629 ◽  
pp. 239-244
Author(s):  
Z.J. Wen ◽  
De Shun Liu ◽  
Shu Yi Yang
Keyword(s):  
Failure Probability ◽  
Simulation Method ◽  
Computational Effort ◽  
Side Impact ◽  
Computational Accuracy ◽  
Simulation Techniques ◽  
Net Method ◽  
Good Lattice ◽  
Good Lattice Point ◽  
Point Set

According to poor computational accuracy at small to median sample sizes of Monte Carlo ( MC ) simulation techniques in estimating the probability failure of mechanical structures, the number theoretical net ( NT-net ) simulation method is proposed to reduce computing effort. Several key concepts, such as good point set, good-lattice point ( glp ), discrepancy and NT-net method, are defined. The sampling stategy is improved by introducing NT-net that can provide better convergent rate over MC. The new method is used to estimate failure probability of the side impact bar on the car door. Results indicate the computational effort needed by NT-net for the same accuracy is about 1/12 of that needed by the MC-based method, and the obtained results are more stable.

Variation propagation modeling and analysis of automotive body outer cover panels assembly systems

2019 ◽  
Vol 39 (2) ◽  
pp. 272-286 ◽  
Author(s):  
Wenwu Han ◽  
Qianwang Deng ◽  
Wenhui Lin ◽  
Xuran Gong ◽  
Sun Ding
Keyword(s):  
Welding Process ◽  
Tolerance Allocation ◽  
Basic Rule ◽  
Assembly Systems ◽  
Practical Case ◽  
Content Type ◽  
Automotive Body ◽  
Threaded Connection ◽  
Variation Propagation ◽  
Outer Cover

PurposeThis study aims to present a model and analysis of automotive body outer cover panels (OCPs) assembly systems to predict assembly variation. In the automotive industry, the OCPs assembly process directly influences the quality of the automobile body appearance. However, suitable models to describe variation propagation of OCPs assembly systems remain unknown.Design/methodology/approachAn adaptive state space model for OCPs assembly systems is introduced to accurately express variation propagation, including variation accumulation and transition, where two compliant deviations make impacts on key product characteristics (KPCs) of OCP, and the impacts are accumulated from welding process to threaded connection process. Another new source of variation from threaded connection is included in this model. To quantify the influence of variation from threaded connection on variation propagation, the threaded connection sensitivity matrix is introduced to build up a linear relationship between deviation from threaded connection and output deviation in KPCs. This matrix is solved by homogeneous coordinate transformation. The final deviation of KPCs will be transferred to ensure gaps and flushes between two OCPs, and the transition matrix is considered as a unit matrix to build up the transition relationship between different states.FindingsA practical case on the left side body structure is described, where simulation result of variation propagation reveals the basic rule of variation propagation and the significant effect of variation from threaded connection on variation propagation of OCPs assembly system.Originality/valueThe model can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase. The calculated results of assembly variation guide designers or technicians on tolerance allocation, fixture layout design and process planning.

Diagnosability Analysis of Multi-Station Manufacturing Processes

2001 ◽  
Vol 124 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yu Ding ◽  
Jianjun Shi ◽  
Dariusz Ceglarek
Keyword(s):  
Manufacturing Process ◽  
State Space Model ◽  
Manufacturing Processes ◽  
Assembly Process ◽  
Space Model ◽  
Distribution Strategy ◽  
Variation Propagation ◽  
Distribution Scheme ◽  
Stream Of Variation ◽  
Comprehensive Study

Variation propagation in a multi-station manufacturing process (MMP) is described by the theory of “Stream of Variation.” Given that the measurements are obtained via certain sensor distribution scheme, the problem of whether the stream of variation of an MMP is diagnosable is of great interest to both academia and industry. We present a comprehensive study of the diagnosability of MMPs in this paper. It is based on the state space model and is parallel to the concept of observability in control theory. Analogous to the observability matrix and index, the diagnosability matrix and index are first defined and then derived for MMP systems. The result of diagnosability study is applied to the evaluation of sensor distribution strategy. It can also be used as the basis to develop an optimal sensor distribution algorithm. An example of a three-station assembly process with multi-fixture layouts is presented to illustrate the methodology.

Digital Panel Assembly Methodologies and Applications for Compliant Sheet Components

2005 ◽  
Vol 128 (1) ◽  
pp. 270-279 ◽  
Author(s):  
Wayne W. Cai ◽  
Ching-Chieh Hsieh ◽  
Yufeng Long ◽  
Samuel P. Marin ◽  
Kong P. Oh
Keyword(s):  
Linear Models ◽  
Nonlinear Models ◽  
Taylor Series Expansion ◽  
Software Tool ◽  
Variation Analysis ◽  
Element Analysis ◽  
Automotive Body ◽  
Pros And Cons ◽  
Component Assembly ◽  
Variation Simulation

This paper presents digital panel assembly (DPA) methodologies and applications for sheet component assembly in automotive body manufacturing processes. Core to DPA is the customized finite element analysis formulas we have developed, which simulates assembly processes and predicts assembly dimensions by taking into consideration the panel compliances. Two key analysis types of the DPA are presented, the deterministic analysis and variation analysis. We present a methodology to utilize the quadratic form of Taylor series expansion to approximate the assembly dimensions efficiently in variation simulation, and discuss its pros and cons versus the traditional Monte Carlo method under different modeling conditions. For either the deterministic or variation analysis, linear models (without contact, efficient but less accurate), and nonlinear models (with contact, less efficient but accurate) can be established. It is shown that the linear models are only valid when panels do not penetrate, and that the nonlinear models should generally be used for accurate assembly dimension prediction. Based on the DPA methodologies, a software tool called Elastic Assembly Variation Simulation (EAVS) is presented, followed by application case studies. The confidence intervals for variation analysis are also discussed.

Diagnosability Analysis of Multi-Station Manufacturing Processes

Manufacturing ◽  
2002 ◽  
Author(s):  
Yu Ding ◽  
Jianjun Shi ◽  
Dariusz Ceglarek
Keyword(s):  
Manufacturing Process ◽  
State Space Model ◽  
Manufacturing Processes ◽  
Assembly Process ◽  
Space Model ◽  
Distribution Strategy ◽  
Variation Propagation ◽  
Distribution Scheme ◽  
Stream Of Variation ◽  
Comprehensive Study

Variation propagation in a multi-station manufacturing process (MMP) is described by the theory of “Stream of Variation.” Given that the measurements are obtained via certain sensor distribution scheme, the problem of whether the stream of variation of an MMP is diagnosable is of great interest to both academia and industry. We present a comprehensive study of the diagnosability of MMPs in this paper. It is based on the state space model and is parallel to the concept of observability in control theory. Analogous to the observability matrix and index, the diagnosability matrix and index are first defined and then derived for MMP systems. The result of diagnosability study is applied to the evaluation of sensor distribution strategy. It can also be used as the basis to develop an optimal sensor distribution algorithm. An example of a three-station assembly process with multi-fixture layouts is presented to illustrate the methodology.

Generalized good lattice point sets

2017 ◽  
Vol 33 (2) ◽  
pp. 887-901 ◽  
Author(s):  
Zong-Feng Qi ◽  
Xue-Ru Zhang ◽  
Yong-Dao Zhou
Keyword(s):  
Lattice Point ◽  
Point Sets ◽  
Good Lattice ◽  
Good Lattice Point

scholarly journals Design Evaluation of Multi-station Assembly Processes by Using State Space Approach

2002 ◽  
Vol 124 (3) ◽  
pp. 408-418 ◽  
Author(s):  
Yu Ding ◽  
Dariusz Ceglarek ◽  
Jianjun Shi
Keyword(s):  
State Space ◽  
Process Design ◽  
State Space Model ◽  
System Level ◽  
Space Representation ◽  
System Response ◽  
Design Evaluation ◽  
Assembly Process ◽  
Sensitivity Indices ◽  
Variation Propagation

This paper considers the problem of evaluating and benchmarking process design configuration in a multi-station assembly process. We focus on the unique challenges brought by the multi-station system, namely, (1) a system level model to characterize the variation propagation in the entire process, and (2) the necessity to describe the system response to variation inputs at both global (system level) and local (station level and single fixture level) scales. State space representation is employed to recursively describe the propagation of variation in such a multi-station process, incorporating process design information such as fixture locating layout at individual stations and station-to-station locating layout change. Following the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given process design configuration. Implication of these indices with respect to variation control is discussed and a three-step procedure of applying the sensitivity indices for selecting a better design and prioritizing the critical station/fixture is presented. We illustrate the proposed method using the group of sensitivity indices in design evaluation of the assembly process of an SUV (Sport Utility Vehicle) side panel.

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