微小研抛机器人加工大型曲面工艺规划技术
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摘要
本文紧密围绕国家高技术研究发展计划(“863”计划)“大型曲面自主研抛作业微小机器人技术”和国家自然科学基金项目“自定位微小研抛机器人精整加工大型曲面研究”开展研究工作,以实现微小研抛机器人能够在大型曲面上自主加工作业为研究目标,对微小研抛机器人工艺规划技术进行了深入研究。
在简要介绍微小研抛机器人各部分组成和工作过程的基础上,对微小研抛机器人工艺系统进行了研究,提出了研抛加工步骤,总结了影响表面质量和效率的因素。运用概率论、弹塑性力学和切削理论,在对磨具表面磨粒分布规律和材料去除机理进行详细分析的基础上,建立了材料去除率模型和材料去除轮廓模型,采用仿真分析和实验研究方法,对模型进行了讨论分析。通过大量研抛实验,研究了研抛压力、工具转速、进给速度、磨料粒度等工艺参数对表面质量和效率的影响;对实验数据进行总结分析,揭示了材料去除深度、表面粗糙度和加工效率随研抛次数增加的变化规律;分别采用理论分析、实验数据和人工神经网络方法建立了表面粗糙度模型。提出了三种适用于模具曲面研抛加工的工艺参数优化方法,即采用灰关联分析方法在给定实验条件下进行工艺参数优化组合、基于人工神经网络和遗传算法的工艺参数优化和基于模糊综合评判与范例推理的研抛工艺参数优化。对微小研抛机器人加工工艺进行了规划,提出了确定最佳研抛次数的方法,并进行了微小研抛机器人加工优化实验研究。
There are usually rough machining and finishing for mould free-form surface in order to obtain ideal surface quality. The developed countries have been paid attention to the automation polishing technology of mould free-form surface since end of the eighties in last century. At present, it has been reported that 30-50% of the total time required to manufacture a mould or die is spent on finishing operations, which are mostly performed by the skilled workers employing traditional techniques. So the efficiency and quality of finishing operations have great effects on the whole production cycle and surface quality of mould.
Nowadays all kinds of polishing equipments are seldom used for finishing large mould surface, such as large automobile panel mould. Because large polishing equipment is expensive, and there lie in the conflict of workspace, precision, fast motion ability and stability, it is very necessary to develop novel finishing process technology and equipment. Small polishing robot is proposed for finishing large free-form surface in this paper.
The small polishing robot can realize autonomous finishing operation only if intelligent process decision system is establishing. It is very important for small polishing robot to finish process planning according to the known information, and good surface quality and high machining efficiency could be obtained. Process system structure, finishing style, material removal mechanism and process characteristics on small polishing robot should be researched. Process planning method applicable for this robot should be presented considering interaction of each stage. So, research situation and development on mould finishing technology is summarized in this paper, then process planning technology of small polishing robot finishing large mould free-form surface is thorough studied. Main research contents, methods and conclusions are as follows:
(1) Process system of small polishing robot is studied. First, small polishing robot system is introduced, which consist of mechanical and control system. Finishing operation process, the active and passive compliance control are analyzed. The compliance property of abrasive tool is related to grain size and numbers of polishing sheet. The compliance property decreases with increasing of grain size and numbers of polishing sheet. According to the structure and machining style of this robot, process planning step is constructed, namely curved surface subdivision, polishing path planning, selecting type and parameters of abrasive tool, process parameters optimization (polishing pressure, tool speed, feed rate and number of polishing times). Evaluation parameters for polishing effect is put forward, and the effect of factors on machining effect are comprehensive summary.
(2) The material removal rate model and material removal profile model are established. It is assumed that the distribution of the abrasive grain protrusion heights of the abrasive tool surface closes to Gaussian distribution. The model is based on the probability statistics, the contact elastic mechanics, the contact plastic mechanics and the abrasive cutting theory. After analyzing interaction of the abrasive grains and the workpiece, the theoretical model of material removal rate is presented by calculating the removal volumes of all abrasive grains participating in cutting, and the relationship between this mode and other removal rate mode is given out. The material removal rate depends essentially on the mechanical properties of the workpiece and tool, the polishing tool specifications and polishing conditions. It increases with the increase of pressure and tool rotation speed, and decreases with the increase of feed rate and workpiece material hardness. The material removal profile could be obtained by integrating the linear removal intensity along the polishing contact path formed by the polishing tool passing this position. The methods of calculating material removal profile about two kinds of polishing types are given in detail. The effects of process parameters on material removal profile are analyzed. The predicted results based on the current model are shown to be approximately consistent with the experimental results. A simple equation of material removal depth is presented.
(3) Process characteristics of small polishing robot are researched. Many polishing experiments were carried out, and the effects of main process parameters on surface quality and efficiency are researched. The influence laws of polishing pressure, tool speed, feed rate and abrasive size on surface quality and efficiency are revealed. Applicable range and select basis of some process parameters are given. The variation laws of surface roughness, material removal depth and polishing efficiency with increasing number of polishing times are proposed, which provides theoretical foundation for selecting number of polishing times. Machining experiments in small polishing robot are carried out, which show that this robot is stable and reliable.
(4) The surface roughness model of polishing process is presented. The surface roughness model is established in term of the distribution of the abrasive grain protrusion heights and cutting theory. By experiment and analysis, it is concluded that this model is more suitable for calculating surface roughness finally reached using a certain abrasive tool in some condition. The model about surface roughness and number of polishing times is proposed for small polishing robot by summarizing experiment data in this paper. The prediction model of surface roughness based on artificial neural network (ANN) is presented. The network consists of one input layer (seven neurons), one hidden layer and one output layer (one neuron), among which input layer include main factors. The average relative error of prediction results is less than 5%, which show that it can basically map the relationship between input parameters and output parameter and is applicable for machining manufacture.
(5) Optimization method of process parameter for small polishing robot is studied and three methods of process parameter optimization for polishing are proposed. The grey relation analysis method is adopted. The grey correlation degree values of surface roughness and machining efficiency are calculated by using the experimental data, and then parameters combination of smaller surface roughness and higher machining efficiency are obtained. The results of comparative analysis prove the validity of this method. Polishing parameters optimization based on the ANN and the genetic algorithm (GA) is studied. The minimum surface roughness and the biggest machining efficiency are regarded as the objective functions. The multi-objective optimal model is built and the optimization results are given. The fuzzy comprehensive evaluation and case-based reasoning method used for selecting process parameters of small polishing robot is put forward. Firstly, primary selection of case in case base is done through the fuzzy comprehensive evaluation for material cutting. Then, the case retrieving is done again through calculating the similarity between the two cases using the nearest neighbor retrieval method. The mutual correlation parameter selection is used to calculate the correlation coefficient between every parameter and polishing quality to get the main influence parameters. Finally, linear extrapolation size adjustment and parameter fulfillment adjustment are adopted to modify the retrieved case. The experimental results show that this method is feasible to solve the problem of process parameters selection of small polishing robot.
(6) Research and experiments on process planning of small polishing robot are carried out. The process planning objective of the small polishing robot is put forward. According to the planning procedure given in chapter 2 and optimization results in chapter 5, the selection principle of grain size is presented, and the polishing pressure, tool speed and feed rate are determined. The best number of polishing times is determined by using critical times method. The experimental results show that the processing planning method of small polishing robot proposed in this paper is feasible.
在简要介绍微小研抛机器人各部分组成和工作过程的基础上,对微小研抛机器人工艺系统进行了研究,提出了研抛加工步骤,总结了影响表面质量和效率的因素。运用概率论、弹塑性力学和切削理论,在对磨具表面磨粒分布规律和材料去除机理进行详细分析的基础上,建立了材料去除率模型和材料去除轮廓模型,采用仿真分析和实验研究方法,对模型进行了讨论分析。通过大量研抛实验,研究了研抛压力、工具转速、进给速度、磨料粒度等工艺参数对表面质量和效率的影响;对实验数据进行总结分析,揭示了材料去除深度、表面粗糙度和加工效率随研抛次数增加的变化规律;分别采用理论分析、实验数据和人工神经网络方法建立了表面粗糙度模型。提出了三种适用于模具曲面研抛加工的工艺参数优化方法,即采用灰关联分析方法在给定实验条件下进行工艺参数优化组合、基于人工神经网络和遗传算法的工艺参数优化和基于模糊综合评判与范例推理的研抛工艺参数优化。对微小研抛机器人加工工艺进行了规划,提出了确定最佳研抛次数的方法,并进行了微小研抛机器人加工优化实验研究。
There are usually rough machining and finishing for mould free-form surface in order to obtain ideal surface quality. The developed countries have been paid attention to the automation polishing technology of mould free-form surface since end of the eighties in last century. At present, it has been reported that 30-50% of the total time required to manufacture a mould or die is spent on finishing operations, which are mostly performed by the skilled workers employing traditional techniques. So the efficiency and quality of finishing operations have great effects on the whole production cycle and surface quality of mould.
Nowadays all kinds of polishing equipments are seldom used for finishing large mould surface, such as large automobile panel mould. Because large polishing equipment is expensive, and there lie in the conflict of workspace, precision, fast motion ability and stability, it is very necessary to develop novel finishing process technology and equipment. Small polishing robot is proposed for finishing large free-form surface in this paper.
The small polishing robot can realize autonomous finishing operation only if intelligent process decision system is establishing. It is very important for small polishing robot to finish process planning according to the known information, and good surface quality and high machining efficiency could be obtained. Process system structure, finishing style, material removal mechanism and process characteristics on small polishing robot should be researched. Process planning method applicable for this robot should be presented considering interaction of each stage. So, research situation and development on mould finishing technology is summarized in this paper, then process planning technology of small polishing robot finishing large mould free-form surface is thorough studied. Main research contents, methods and conclusions are as follows:
(1) Process system of small polishing robot is studied. First, small polishing robot system is introduced, which consist of mechanical and control system. Finishing operation process, the active and passive compliance control are analyzed. The compliance property of abrasive tool is related to grain size and numbers of polishing sheet. The compliance property decreases with increasing of grain size and numbers of polishing sheet. According to the structure and machining style of this robot, process planning step is constructed, namely curved surface subdivision, polishing path planning, selecting type and parameters of abrasive tool, process parameters optimization (polishing pressure, tool speed, feed rate and number of polishing times). Evaluation parameters for polishing effect is put forward, and the effect of factors on machining effect are comprehensive summary.
(2) The material removal rate model and material removal profile model are established. It is assumed that the distribution of the abrasive grain protrusion heights of the abrasive tool surface closes to Gaussian distribution. The model is based on the probability statistics, the contact elastic mechanics, the contact plastic mechanics and the abrasive cutting theory. After analyzing interaction of the abrasive grains and the workpiece, the theoretical model of material removal rate is presented by calculating the removal volumes of all abrasive grains participating in cutting, and the relationship between this mode and other removal rate mode is given out. The material removal rate depends essentially on the mechanical properties of the workpiece and tool, the polishing tool specifications and polishing conditions. It increases with the increase of pressure and tool rotation speed, and decreases with the increase of feed rate and workpiece material hardness. The material removal profile could be obtained by integrating the linear removal intensity along the polishing contact path formed by the polishing tool passing this position. The methods of calculating material removal profile about two kinds of polishing types are given in detail. The effects of process parameters on material removal profile are analyzed. The predicted results based on the current model are shown to be approximately consistent with the experimental results. A simple equation of material removal depth is presented.
(3) Process characteristics of small polishing robot are researched. Many polishing experiments were carried out, and the effects of main process parameters on surface quality and efficiency are researched. The influence laws of polishing pressure, tool speed, feed rate and abrasive size on surface quality and efficiency are revealed. Applicable range and select basis of some process parameters are given. The variation laws of surface roughness, material removal depth and polishing efficiency with increasing number of polishing times are proposed, which provides theoretical foundation for selecting number of polishing times. Machining experiments in small polishing robot are carried out, which show that this robot is stable and reliable.
(4) The surface roughness model of polishing process is presented. The surface roughness model is established in term of the distribution of the abrasive grain protrusion heights and cutting theory. By experiment and analysis, it is concluded that this model is more suitable for calculating surface roughness finally reached using a certain abrasive tool in some condition. The model about surface roughness and number of polishing times is proposed for small polishing robot by summarizing experiment data in this paper. The prediction model of surface roughness based on artificial neural network (ANN) is presented. The network consists of one input layer (seven neurons), one hidden layer and one output layer (one neuron), among which input layer include main factors. The average relative error of prediction results is less than 5%, which show that it can basically map the relationship between input parameters and output parameter and is applicable for machining manufacture.
(5) Optimization method of process parameter for small polishing robot is studied and three methods of process parameter optimization for polishing are proposed. The grey relation analysis method is adopted. The grey correlation degree values of surface roughness and machining efficiency are calculated by using the experimental data, and then parameters combination of smaller surface roughness and higher machining efficiency are obtained. The results of comparative analysis prove the validity of this method. Polishing parameters optimization based on the ANN and the genetic algorithm (GA) is studied. The minimum surface roughness and the biggest machining efficiency are regarded as the objective functions. The multi-objective optimal model is built and the optimization results are given. The fuzzy comprehensive evaluation and case-based reasoning method used for selecting process parameters of small polishing robot is put forward. Firstly, primary selection of case in case base is done through the fuzzy comprehensive evaluation for material cutting. Then, the case retrieving is done again through calculating the similarity between the two cases using the nearest neighbor retrieval method. The mutual correlation parameter selection is used to calculate the correlation coefficient between every parameter and polishing quality to get the main influence parameters. Finally, linear extrapolation size adjustment and parameter fulfillment adjustment are adopted to modify the retrieved case. The experimental results show that this method is feasible to solve the problem of process parameters selection of small polishing robot.
(6) Research and experiments on process planning of small polishing robot are carried out. The process planning objective of the small polishing robot is put forward. According to the planning procedure given in chapter 2 and optimization results in chapter 5, the selection principle of grain size is presented, and the polishing pressure, tool speed and feed rate are determined. The best number of polishing times is determined by using critical times method. The experimental results show that the processing planning method of small polishing robot proposed in this paper is feasible.
引文
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