Modelling of Variation Propagation for Multi-Operational Machining Processes Based on Perturbation Vectors

2010 ◽  
Vol 36 ◽  
pp. 120-128 ◽  
Author(s):  
Z.J. Wen ◽  
Ping Yu Zhu ◽  
X.P. Zhang ◽  
H.C. Liu
Keyword(s):  
State Space ◽  
State Space Model ◽  
Machining Process ◽  
Machining Processes ◽  
Machining Error ◽  
Space Model ◽  
Proposed Model ◽  
Variation Propagation ◽  
Dimensional Variation

A new state space model of multi-operational machining processes is presented for dimensional variation propagation, transformation and accumulation based on perturbation vectors (PV). Taking perturbation vectors (PV) for state vectors of part geometric variaton and the fixture variations for input vectors, the perturbation homogeneous transformation (PHT) is applied to analyze and derivate datum-induced deviation, re-location deviation, fixture error and machining error, and a state space model of variation propagation in multi-operational complicated machining processes is developed. Furthermore, a three-operation machining process of cylinder is given to illustrate the method presented. With the results of calculation and simulation, it is verified that the proposed model is effective and useful.

A New Dynamic State Space Model for the Cylindrical Grinding Process

2005 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Surface Roughness ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Grinding Process ◽  
Cylindrical Grinding ◽  
Space Model ◽  
Proposed Model ◽  
Grinding Power ◽  
Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

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.

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.

Part Dimensional Error and Its Propagation Modeling in Multi-Operational Machining Processes

2003 ◽  
Vol 125 (2) ◽  
pp. 255-262 ◽  
Author(s):  
Qiang Huang ◽  
Jianjun Shi ◽  
Jingxia Yuan
Keyword(s):  
State Space Model ◽  
Machining Process ◽  
Kinematic Modeling ◽  
Machining Processes ◽  
Dimensional Error ◽  
Physical Relationship ◽  
Modeling And Control ◽  
Propagation Modeling ◽  
Variation Propagation ◽  
Machining Operations

In a multi-operational machining process (MMP), the final product variation is an accumulation or stack-up of variation from all machining operations. Modeling and control of the variation propagation is essential to improve product dimensional quality. This paper presents a state space model and its modeling strategies to describe the variation stack-up in MMPs. The physical relationship is explored between part variation and operational errors. By using the homogeneous transformation approach, kinematic modeling of setup and machining operations are developed. A case study with real machined parts is presented in the model validation.

Simultaneous quadrotor autopilot system and collective morphing system design

2020 ◽  
Vol 92 (7) ◽  
pp. 1093-1100
Author(s):  
Oguz Kose ◽  
Tugrul Oktay
Keyword(s):  
State Space ◽  
State Space Model ◽  
Optimization Method ◽  
The State ◽  
Control Technique ◽  
Matlab Simulink ◽  
Content Type ◽  
Space Model ◽  
Proposed Model ◽  
Long Time

Purpose The purpose of this paper is to design a quadrotor with collective morphing using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach Quadrotor design is made by using Solidworks drawing program and some mathematical performance relations. Modelling and simulation are performed in Matlab/Simulink program by using the state space model approaches with the parameters mostly taken from Solidworks. Proportional integral derivative (PID) approach is used as control technique. Morphing amount and the best PID coefficients are determined by using SPSA algorithm. Findings By using SPSA algorithm, the amount of morphing and the best PID coefficients are determined, and the quadrotor longitudinal and lateral flights are made most stable via morphing. Research limitations/implications It takes quite a long time to model the quadrotor in Solidworks and Matlab/Simulink with the state space model and using the SPSA algorithm. However, this situation is overcome with the proposed model. Practical implications Optimization with SPSA is very useful in determining the amount of morphing and PID coefficients for quadrotors. Social implications SPSA optimization method is useful in terms of cost, time and practicality. Originality/value It is released to improve performance with morphing, to determine morphing rate with SPSA algorithm and to determine PID coefficients accordingly.

MODELING AND CONTROL OF DIMENSIONAL QUALITY OF A SERIAL MULTI-STATION MACHINING SYSTEM

2006 ◽  
Vol 13 (05) ◽  
pp. 399-419 ◽  
Author(s):  
SHICHANG DU ◽  
LIFENG XI ◽  
ERSHUN PAN ◽  
JIANJUN SHI ◽  
C. RICHARD LIU
Keyword(s):  
State Space ◽  
State Space Model ◽  
Machining Processes ◽  
Modeling And Control ◽  
Engineering Theory ◽  
Machining Errors ◽  
Proposed Model ◽  
Machining System ◽  
Linear State ◽  
And Control

Modeling and control of dimensional quality is one of deciding factors in current manufacturing competitions, and has always presented a great challenge to both scientists and engineers since for a multi-station machining system, the final product variation is an accumulation from all stations, and the complex non-linear relationship exits between dimensional quality and machining errors. This paper develops a linear state space model using homogeneous transformation to capture the influence of machined errors on dimensional quality, and the explicit expressions for system matrices of the model are explored. The proposed model employs a linear state space form, facilitating the use of the achievements of control theory, information technology and system engineering theory to support engineers supervisory control of physical machining processes, and it also can be used as an analytical engineering tool for efficient and effective faults diagnosis, system plan and design, and optimal sensors allocation. A real machining case illustrates the proposed model.

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