Research On Mechanism And Process Parameters Optimization Of Remanufacturing Part Paint Cleaning By Wet Blast Cleaning

Remanufacturing engineering has received a lot of attention since it highly complies with the development requirements of the ’Dual Carbon’ approach and offers the benefits of being environmentally friendly and utilizing resources efficiently.The remanufacturing process consists of disassembling,cleaning,inspecting,and repairing.Cleaning is the process to remove dirt from remanufacturing parts through various cleaning techniques,which is the basis of remanufacturing engineering.The cleaning effect directly affects the quality of the subsequent inspecting and repairing.Paint is one of the main targets for remanufacturing cleaning.Traditional cleaning technology has been contradictory to the requirement of sustainable development due to serious pollution.Wet blast cleaning technology as a new cleaning technology,in line with the concept of remanufacturing,is extremely suitable for cleaning the paint on remanufacturing parts and has been widely used.In this thesis,wet blast cleaning technology was used to remove the paint.The microscopic bonding mechanism of the paint was analyzed,and the mechanism of wet blast cleaning with different abrasives in shape and size was revealed,and the effect of the wet blast cleaning process parameters on the cleaning effect was investigated and the multi-objective process parameters optimization was conducted.Therefore,this work provides data support and theoretical guidance for the practical application of removing paint from remanufacturing parts by wet blast cleaning.Firstly,the microscopic bonding mechanism of various paint/substrate interface systems was investigated using molecular dynamics simulations.Taking the widely used acrylic and polyurethane paint also the ADC 12 and HT250 substrate as representatives,the molecular dynamics models of four paint/substrate interface systems(PAA/Al2O3,PAA/Fe2O3,PU/Al2O3,PU/Fe2O3)were established.The interaction energy of the interfacial systems was calculated,and the results showed that the PAA/Fe2O3 interfacial system was the most stable interface system due to the highest interaction energy.By further analyzing the energy composition,atomic concentration distribution,surface microstructure and hydrogen bonding generation,the microscopic bonding mechanism was discussed in detail:The binding mechanisms of PAA/Al2O3,PU/Al2O3,and PU/Fe2O3 interfacial systems was mainly van der Waals interactions;while that of PAA/Fe2O3 interface system was mainly electrostatic interactions and hydrogen bond interactions.In addition,mechanical bonding between paint and substrate was also the bonding mechanism.The accuracy of the molecular dynamics simulation results was verified by actually testing the bonding strength of paint in various interfacial systems.Secondly,the mechanism of wet blast cleaning with different abrasives was studied considering the influence of abrasive characteristics.The shape and size of abrasives were analyzed,and wet blast cleaning experiments were conducted using different abrasives in shape and size.The cleaning process with different abrasives was investigated by analyzing the variation of specimen mass with the cleaning times,it was found that the cleaning process of angular abrasives was stable,while that of spherical abrasives was divided into three stages:gestation,development,and stabilization stage.In addition,the mechanism of wet blast cleaning with different abrasives was explained by characterizing and analyzing the microscopic morphology of the specimen:the mechanism of wet blast cleaning with angular abrasives was not affected by the size,brittle fracture due to cracking from large impact stresses and microcutting and ploughing were the main mechanisms;the mechanism of wet blast cleaning with spherical abrasives was related the size,for large size,the brittle fracture caused by the combination of longitudinal penetration fatigue cracks and transverse interface cracks was the main reason;for small size,the brittle fracture caused by fatigue cracks and impact cracks was the main reason.Finally,the effect of process parameters in wet blast cleaning on both cleaning efficiency and variation in the performance of substrate before and after cleaning was investigated,and a multi-objective optimization model based on neural network and genetic algorithm was established to optimize the process parameters.The effect of cleaning pressure,standoff distance and feed rate on cleaning efficiency,variation in surface roughness and Vickers hardness of the substrate before and after cleaning was explored by orthogonal experiment.The results showed that cleaning pressure was the most influential parameter for all the cleaning efficiency and the variation in surface roughness and Vickers hardness of the substrate before and after cleaning,followed by the standoff distance and feed rate.Based on the data from the orthogonal experiment,a neural network model for predicting the cleaning effects was developed,then it was combined with a genetic algorithm to establish a multi-objective process parameter optimization model for wet blast cleaning,and its Pareto solution was calculated.Then,the errors of the optimization results are experimentally proven to be within 10%,indicating that the results were credible.