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.

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.

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.

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.

Dynamic prediction and compensation of aerocraft assembly variation based on state space model

2015 ◽  
Vol 35 (2) ◽  
pp. 183-189 ◽  
Author(s):  
Yujun Cao ◽  
Xin Li ◽  
Zhixiong Zhang ◽  
Jianzhong Shang
Keyword(s):  
State Space ◽  
State Space Model ◽  
Propagation Model ◽  
Small Displacement ◽  
Assembly Process ◽  
Dynamic Prediction ◽  
Content Type ◽  
Assembly Accuracy ◽  
Variation Propagation ◽  
State Space Theory

Purpose – This paper aims to clarify the predicting and compensating method of aeroplane assembly. It proposes modeling the process of assembly. The paper aims to solve the precision assembly of aeroplane, which includes predicting the assembly variation and compensating the assembly errors. Design/methodology/approach – The paper opted for an exploratory study using the state space theory and small displacement torsor theory. The assembly variation propagation model is established. The experiment data are obtained by a real small aeroplane assembly process. Findings – The paper provides the predicting and compensating method for aeroplane assembly accuracy. Originality/value – This paper fulfils an identified need to study how the assembly variation propagates in the assembly process.

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.

Optimal control method on assembly precision for a remanufactured car engine based on state space model

2016 ◽  
Vol 36 (4) ◽  
pp. 460-472 ◽  
Author(s):  
Jing Hu ◽  
Yuan Zhang ◽  
Maogen GE ◽  
Mingzhou Liu ◽  
Liu Conghu ◽  
...  
Keyword(s):  
Optimal Control ◽  
State Space ◽  
Control Method ◽  
State Space Model ◽  
The State ◽  
Content Type ◽  
Space Model ◽  
Optimal Control Method ◽  
Assembly Error ◽  
Assembly Precision

Purpose The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem. Design/methodology/approach Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed. Findings The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study. Originality/value The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

Identification of Stochastic System and Controller via Projection Filters

1996 ◽  
Vol 118 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Hyun Chang Lee ◽  
Min-Hung Hsiao ◽  
Jen-Kuang Huang ◽  
Chung-Wen Chen
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Stochastic System ◽  
State Space Model ◽  
Open Loop ◽  
The State ◽  
Loop System ◽  
Test Facility ◽  
System Response ◽  
Space Model

A method based on projection filters is presented for identifying an open-loop stochastic system with an existing feedback controller. The projection filters are derived from the relationship between the state-space model and the AutoRegressive with eXogeneous input (ARX) model including the system, Kalman filter and controller. Two ARX models are identified from the control input, closed-loop system response and feedback signal using least-squares method. Markov parameters of the open-loop system, Kalman filter and controller are then calculated from the coefficients of the identified ARX models. Finally, the state-space model of the open-loop stochastic system and the gain matrices for the Kalman filter and controller are realized. The method is validated by simulations and test data from an unstable large-angle magnetic suspension test facility.

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.

Bayesian monthly index for building activity based on mixed frequencies: the case of Chile

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Byron J. Idrovo-Aguirre ◽  
Javier E. Contreras-Reyes
Keyword(s):  
Kalman Filter ◽  
Bayesian Inference ◽  
State Space ◽  
State Space Model ◽  
Natural Extension ◽  
The State ◽  
Likelihood Method ◽  
Content Type ◽  
Space Model ◽  
Construction Activity

PurposeThis paper combines the objective information of six mixed-frequency partial-activity indicators with assumptions or beliefs (called priors) regarding the distribution of the parameters that approximate the state of the construction activity cycle. Thus, this paper uses Bayesian inference with Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon.Design/methodology/approachUnlike other economic sectors of similar importance in aggregate gross domestic product, such as mining and industry, the construction sector lacked a short-term measure that helps to identify its most recent performance.FindingsIndeed, because these priors are susceptible to changes, they provide flexibility to the original Imacon model, allowing for the assessment of risk scenarios and adaption to the greater relative volatility that characterizes the sector's activity.Originality/valueThe classic maximum likelihood method of estimating the monthly construction activity index (Imacon) is rigid to the incorporation of new measures of uncertainty, expectations or different volatility (risks) levels in the state of construction activity. In this context, this paper uses Bayesian inference with 10,000 Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon, inspired by the original works of Mariano and Murasawa (2003) and Kim and Nelson (1998). Thus, this paper consists of a natural extension of the classic method used by Tejada (2006) in the estimation of the old Imacon.

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