An approach to evaluating product assembly precision considering the effect of joint surface deformation

2014 ◽  
Vol 228 (14) ◽  
pp. 2604-2617 ◽  
Author(s):  
Cong Lu ◽  
Song-Ling Wang
Keyword(s):  
Surface Deformation ◽  
Hybrid Genetic Algorithm ◽  
Back Propagation ◽  
Propagation Model ◽  
Joint Surface ◽  
Element Analysis ◽  
Assembly Sequences ◽  
Variation Propagation ◽  
Product Assembly ◽  
Assembly Precision

This paper proposes an approach to evaluating the product assembly precision in different assembly sequences, considering the effect of joint surface deformation. Three assembly variation sources, including manufacturing variation, assembly clearance and joint surface deformation, which have effect on the final assembly precision are analyzed. Based on finite element analysis, a hybrid genetic algorithm and back-propagation neural network model is built to predict the assembly variations, which are caused by the joint surface deformation under different assembly conditions and different parameters of the joint surface. An assembly variation propagation model is built, and a product assembly precision evaluation approach is proposed to identify the feasible assembly sequences, and the optimal assembly sequence considering the effect of the joint surface deformation. Finally, a case study is given to verify the proposed approach.

The Research of the Assembly Precision of CNC Machine Tools Considering the Joint Surface Characteristics

2013 ◽  
Vol 397-400 ◽  
pp. 198-201
Author(s):  
Wan Yu Li ◽  
Cong Lu ◽  
Zhuo Yang
Keyword(s):  
Machine Tools ◽  
Surface Deformation ◽  
Surface Characteristics ◽  
Cnc Machine Tools ◽  
Joint Surface ◽  
Surface Model ◽  
Assembly Sequence ◽  
Cnc Machine ◽  
Element Analysis ◽  
Assembly Precision

In order to evaluate the assembly precision of the CNC machine tools influenced by the joint surface characteristics effectively, a method is proposed in this paper. Finite element analysis is used to obtain joint surface deformation data, and then a second-order response surface model is established as a surrogate model to express the function of joint surface static characteristics and deformation. The error propagation in assembly sequence is obtained, so that the assembly precision of CNC machine tools can be evaluated effectively.

Calculation of Angular Variation of Spacecraft Assembly Considering Measurement Uncertainty

2013 ◽  
Vol 457-458 ◽  
pp. 1058-1063
Author(s):  
Jing Zhao Yang ◽  
Guo Xi Li ◽  
Bao Zhong Wu
Keyword(s):  
Measurement Uncertainty ◽  
Propagation Model ◽  
Design Stage ◽  
Assembly Model ◽  
Angular Variation ◽  
Early Design Stage ◽  
Assembly Accuracy ◽  
Key Characteristics ◽  
Variation Propagation ◽  
Product Assembly

The present studies of assembly accuracy predicting for complex products are mainly oriented to the early design stage. The final product assembly accuracy is computed using a variation propagation model, which is constructed based on design tolerance model and assembly model. Generally, the solving results are quite different with the actual values. In this paper, the axis angular variation calculation of the spacecraft cabin at the assembly phase was studied. And a novel analysis method for assembly variation was proposed with the consideration of measurement uncertainty of the key characteristics. An example was studied to illustrate and demonstrate the feasibility of the proposed method.

The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050

2020 ◽  
Vol 235 (1-2) ◽  
pp. 265-277
Author(s):  
Wei Ma ◽  
Rongqi Wang ◽  
Xiaoqin Zhou ◽  
Xuefan Xie
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Propagation Model ◽  
Element Analysis ◽  
Swarm Optimization ◽  
Milling Forces

The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.

Inverse Solution for Bolt Preload Using Surface Deformation

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
A. Zaki ◽  
S. A. Nassar ◽  
S. Kruk ◽  
M. Shillor
Keyword(s):  
Inverse Problem ◽  
Surface Deformation ◽  
Control Method ◽  
Plane Surface ◽  
Joint Surface ◽  
Problem Solution ◽  
Bolted Joint ◽  
Element Analysis ◽  
Out Of Plane ◽  
Axisymmetric Elasticity

In this paper, an inverse biharmonic axisymmetric elasticity problem is solved by invoking measured out-of-plane surface deformation values at discrete locations around a preloaded bolt head, in order to calculate the underhead contact stress and joint clamp load that would have caused that out-of-plane surface deformation. Solution of this type of inverse problem promises to improve the automation process of bolted joint system assembly, especially in critical and safety-related applications. For example, a real-time optically measured joint surface deformation can be utilized for automating process control of bolted joint assembly in a reliable fashion. This would be a significant reliability improvement as compared to the commonly used method in mass production using torque-only control method in which there is wide scatter in the torque–tension correlation due to the normal scatter in frictional variables. Finite element analysis (FEA) method is used to validate the inverse problem solution provided in this paper.

Inverse Solution for Bolt Preload Using Joint Surface Deformation

2013 ◽  
Author(s):  
Amro M. Zaki ◽  
Sayed A. Nassar ◽  
Meyer Shillor ◽  
Serge Kruk
Keyword(s):  
Inverse Problem ◽  
Surface Deformation ◽  
Control Method ◽  
Plane Surface ◽  
Joint Surface ◽  
Problem Solution ◽  
Bolted Joint ◽  
Element Analysis ◽  
Out Of Plane ◽  
Axisymmetric Elasticity

In this paper, an inverse bi-harmonic axisymmetric elasticity problem is solved by invoking measured out-of-plane surface deformation values at discrete locations around a preloaded bolt head, in order to calculate the under head contact stress and joint clamp load that would have caused that out-of-plane surface deformation. Solution of this type of inverse problem promises to improve the automation process of bolted joint system assembly, especially in critical and safety related applications. For example, a real-time optically measured joint surface deformation can be utilized for automating process control of bolted joint assembly in a reliable fashion. This would be a significant reliability improvement as compared to the commonly used method in mass production using torque-only control method in which there is wide scatter in the torque-tension correlation due to the normal scatter in frictional variables. Finite Element Analysis (FEA) method is used to validate the inverse problem solution provided in this paper.

The Finite Element Analysis and Optimization for the Grinder Based on the Joint Surface

2013 ◽  
Vol 281 ◽  
pp. 165-169 ◽  
Author(s):  
Xiang Lei Zhang ◽  
Bin Yao ◽  
Wen Chang Zhao ◽  
Ou Yang Kun ◽  
Bo Shi Yao
Keyword(s):  
Finite Element ◽  
Natural Frequency ◽  
Element Model ◽  
Joint Surface ◽  
Weak Links ◽  
Response Analysis ◽  
Element Analysis ◽  
Static And Dynamic Analysis ◽  
Harmonic Response Analysis ◽  
Grinding Machine

Establish the finite element model for high precision grinding machine which takes joint surface into consideration and then carrys out the static and dynamic analysis of the grinder. After the static analysis, modal analysis and harmonic response analysis, the displacement deformation, stress, natural frequency and vibration mode could be found, which also helps find the weak links out. The improvement scheme which aims to increase the stiffness and precision of the whole machine has proposed to efficiently optimize the grinder. And the first natural frequency of the optimized grinder has increased by 68.19%.

scholarly journals Analysis and Structure Optimization of Radial Halbach Permanent Magnet Couplings for Deep Sea Robots

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bo Cheng ◽  
Guang Pan
Keyword(s):  
Permanent Magnet ◽  
Analytical Solutions ◽  
Deep Sea ◽  
Back Propagation ◽  
Optimization Procedure ◽  
Latin Hypercube Sampling ◽  
Transmission Efficiency ◽  
Element Analysis ◽  
Maxwell Stress Tensor ◽  
Pull Out

Permanent magnet couplings (PMCs) can convert the dynamic seal of transmission shaft into a static seal, which will significantly improve the transmission efficiency and reliability. Therefore, the radial Halbach PMC in this paper is suitable as the transmission mechanism of deep sea robots. A two-segment Halbach array is adopted in the radial PMC, and the segment arc coefficient can be adjustable. This paper presents the general analytical solutions of the distinctive Halbach PMCs based on scalar magnetic potential and Maxwell stress tensor. The analytical solutions of magnetic field are in good agreement with 2-D finite element analysis (FEA) results. In addition, an initial prototype of the radial Halbach PMC has been fabricated, and the analytical solutions of magnetic torque are compared with 3-D FEA and experiment results. This paper also establishes an optimization procedure for PMCs based on the combination of 3-D FEA, Back Propagation Neural Network (BPNN), and Genetic Algorithm (GA). 3-D FEA is performed to calculate the pull-out torque of the samples from Latin hypercube sampling, then BPNN is used to describe the relationship between the optimization variables and pull-out torque. Finally, GA is applied to solve the optimization problem, and the optimized scheme is proved to be more reasonable with the FEA method.

Locating a New Drainage Well by Optimization of a Back Propagation Model

2019 ◽  
Vol 38 (2) ◽  
pp. 342-352
Author(s):  
Saeid Maknouni Gilani ◽  
Mohammad Zare ◽  
Ezzatollah Raeisi
Keyword(s):  
Back Propagation ◽  
Propagation Model ◽  
Back Propagation Model
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