The Modeling and Prediction of Gravity Deformation in Precision Machine Tool Assembly

2013 ◽  
Author(s):  
Junkang Guo ◽  
Jun Hong ◽  
Xiaopan Wu ◽  
Mengxi Wang ◽  
Yan Feng
Keyword(s):  
Machine Tool ◽  
State Space ◽  
State Space Model ◽  
Variation Theory ◽  
Assembly Process ◽  
Motion Error ◽  
Space Model ◽  
Precision Machine ◽  
Variation Propagation ◽  
Tool Assembly

The variation propagation in mechanical assembly is an important topic in several research fields, such as computer aided tolerancing (CAT) and product quality control. Mathematical models and analysis methods have been developed to solve this practical problem. Tolerance analysis which is based on the rigid hypothesis can be used to simulate the mass manufacturing and assembly. The state space model and stream of variation theory are mainly applied in flexible part assembly. However, in precision machine tool assembly, both tolerance design and process planning critically impact the accuracy performance, mainly because of the fact that the gravity deformation, including the part deformation and the variation in the joint of two connecting parts, cannot be ignored in variation propagation analysis. In this paper, based on the new generation GPS (Geometrical Product Specification and Verification) standards, the verification and modeling of key characteristics variation due to gravity deformation of single part and adjacent parts are discussed. The accurate evaluation of position and orientation variation taking into account form errors and gravity deformation can be solved from this model by FEM. A mathematical model considering rail error, stiffness of bearings is introduced to simulate the motion error in gravity effect. Based on this work to more accurately calculate the variation propagation considering gravity impact, a state space model describing the assembly process of machine tools is proposed. Then, in any assembly process, the final accuracy can be predicted to find out whether the accuracy is out of design requirement. The validity of this method is verified by a simulation of the assembly of a precision horizontal machining center.

An assembly sensitivity analysis method based on state space model

2017 ◽  
Vol 37 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Xin Li ◽  
Jianzhong Shang ◽  
Hong Zhu
Keyword(s):  
Sensitivity Analysis ◽  
State Space ◽  
Process Parameters ◽  
State Space Model ◽  
System Level ◽  
System Response ◽  
Assembly Process ◽  
Content Type ◽  
Space Model ◽  
Variation Propagation

Purpose This paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research. Design/methodology/approach To date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change. Findings 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 assembly process parameters. Originality/value A case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.

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.

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.

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.

Multiple Fault Diagnosis Method in Multi-Station Assembly Processes Using State Space Model and Orthogonal Diagonalization Analysis

2005 ◽  
Author(s):  
Zhenyu Kong ◽  
Ramesh Kumar ◽  
Suren Gogineni ◽  
Yingqing Zhou ◽  
Jijun Lin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
State Space ◽  
State Space Model ◽  
Measurement Data ◽  
Assembly Process ◽  
Dimensional Control ◽  
Multiple Fault ◽  
Space Model ◽  
Multiple Fault Diagnosis ◽  
Affine System

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.

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

2002 ◽  
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 a 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: (1) a system level model to characterize the variation propagation in the entire process, (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 to 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 a SUV (Sport Utility Vehicle) side panel.

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