Entropy-Based Method for Evaluating Contact Strain-Energy Distribution for Assembly Accuracy Prediction

Assembly accuracy significantly affects the performance of precision mechanical systems. In this study, an entropy-based evaluation method for contact strain-energy distribution is proposed to predict the assembly accuracy. Strain energy is utilized to characterize the effects of the combination of form errors and contact deformations on the formation of assembly errors. To obtain the strain energy, the contact state is analyzed by applying the finite element method (FEM) on 3D, solid models of real parts containing form errors. Entropy is employed for evaluating the uniformity of the contact strain-energy distribution. An evaluation model, in which the uniformity of the contact strain-energy distribution is evaluated in three levels based on entropy, is developed to predict the assembly accuracy, and a comprehensive index is proposed. The assembly experiments for five sets of two rotating parts are conducted. Moreover, the coaxiality between the surfaces of two parts with assembly accuracy requirements is selected as the verification index to verify the effectiveness of the evaluation method. The results are in good agreement with the verification index, indicating that the method presented in this study is reliable and effective in predicting the assembly accuracy.

[1]  Serge Samper,et al.  Taking into account elastic displacements in 3D tolerancing , 1998 .

[2]  Zhifeng Zhang,et al.  Manufacturing complexity and its measurement based on entropy models , 2012 .

[3]  Robert Scott Pierce,et al.  A Method for Integrating Form Errors Into Geometric Tolerance Analysis , 2005, DAC 2005.

[4]  Liu Ting,et al.  Assembly Error Calculation with Consideration of Part Deformation , 2016 .

[5]  Hamed Fazlollahtabar,et al.  A cross-entropy heuristic statistical modeling for determining total stochastic material handling time , 2013 .

[6]  Pierre-Antoine Adragna,et al.  Modeling of 2D and 3D Assemblies Taking Into Account Form Errors of Plane Surfaces , 2009, J. Comput. Inf. Sci. Eng..

[7]  Hua Zhang,et al.  Contact-Free Detection of Obstructive Sleep Apnea Based on Wavelet Information Entropy Spectrum Using Bio-Radar , 2016, Entropy.

[8]  Zoubeida Messali,et al.  Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images , 2015, Entropy.

[9]  Max Giordano,et al.  A Generic Method for the Worst Case and Statistical Tridimensional Tolerancing Analysis , 2013 .

[10]  James R. Payne,et al.  Variables Affecting the Assembly Bolt Stress Developed During Manual Tightening , 2011 .

[11]  Kenneth W. Chase,et al.  A survey of research in the application of tolerance analysis to the design of mechanical assemblies , 1991 .

[12]  Yu. Pogoreltsev,et al.  The Application , 2020, How to Succeed in the Academic Clinical Interview.

[13]  Pere Caminal,et al.  Measuring Instantaneous and Spectral Information Entropies by Shannon Entropy of Choi-Williams Distribution in the Context of Electroencephalography , 2014, Entropy.

[14]  Kürsad Özkan,et al.  Application of Information Theory for an Entropic Gradient of Ecological Sites , 2016, Entropy.

[15]  Z. J. Zhang,et al.  Study on modeling method of the precision machined surface geometry form error based on Bi-cubic B-spline , 2015 .

[16]  Jia Xiao,et al.  A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy , 2016, Entropy.

[17]  Serge Samper,et al.  Form Errors Impact in a Rotating Plane Surface Assembly , 2013 .

[18]  S. Jack Hu,et al.  Variation simulation for deformable sheet metal assemblies using finite element methods , 1997 .

[19]  Steven A. Frank,et al.  Common Probability Patterns Arise from Simple Invariances , 2016, Entropy.

[20]  Karmele López de Ipiña,et al.  Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture , 2014, Entropy.

[21]  Derek W. Robinson,et al.  Entropy and Uncertainty , 2008, Entropy.

[22]  Guolong Chen,et al.  Angular Spectral Density and Information Entropy for Eddy Current Distribution , 2016, Entropy.

[23]  Spencer P. Magleby,et al.  Including Geometric Feature Variations in Tolerance Analysis of Mechanical Assemblies , 1996 .

[24]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[25]  G H Ball,et al.  A clustering technique for summarizing multivariate data. , 1967, Behavioral science.

[26]  Zhijing Zhang,et al.  Modeling method for assembly variation propagation taking account of form error , 2013 .

[27]  Antonio Armillotta Tolerance Analysis Considering form Errors in Planar Datum Features , 2016 .

[28]  Lihong Qiao,et al.  A cross-entropy-based approach for the optimization of flexible process planning , 2013 .

[29]  Subenoy Chakraborty,et al.  Hawking-Like Radiation from the Trapping Horizon of Both Homogeneous and Inhomogeneous Spherically Symmetric Spacetime Model of the Universe , 2016, Entropy.

[30]  Ingo Sieber,et al.  Robust Design of an Optical Micromachine for an Ophthalmic Application † , 2016, Micromachines.

[31]  Conghu Liu,et al.  Tolerance Redistributing of the Reassembly Dimensional Chain on Measure of Uncertainty , 2016, Entropy.

[32]  Lin Li,et al.  A method for assessing geometrical errors in layered manufacturing. Part 1: Error interaction and transfer mechanisms , 1998 .

[33]  Blake S. Pollard Markov Processes : A Compositional Perspective on Non-Equilibrium Steady States in Biology , 2016 .