Research On The Intelligent Machining Techniques And Strategies For The Large-scale Crankshaft CNC Tangential Point Tracing Grinding Machine

As the key part of the internal-combustion engine used in ship, locomotive and electric power equipment, the large-scale crankshaft has close relationship with its wearing resistance, fatigue resistance, vibration and noise characteristic and has the direct influence on its reliability and useful life. With the rapid development of ship and locomotive building manufacturing, the older generations of internal-combustion engines have to be replaced by the new ones, which ask the high-speed, high-precision and complex processing technique of the large-scale crankshaft.Supported by National Science and Technology Major Projects to research and develop the large-scale tangential point tracing grinding machine for crankshaft(No. 2009ZX04001-111), this dissertation does systematical research on some key problems of tangential point tracing grinding to enhance the control level of grinding process for the large-scale crankshaft. Keeping in mind the special characteristics of this grinding technique and the huge crankshaft, the deep studies are conducted on the elastic deformation of crankshaft, the automatic positioning of crankshaft, the optimization of grinding allowance distribution, the intelligent decision of grinding parameters, the influence of grinding wheel wearing on machining quality and the intelligent compensation of machining error. The sensor detecting technology and the artificial intelligence technology are introduced in this dissertation to resolve the technical difficulties as mentioned above. The main researches and results of this dissertation are summarized as follows:The influence of the elastic deformation on the ground crank pin's dimension and roundness due to gravity, clamping force and grinding force is analyzed. Based on this analysis the methods including the auxiliary support from steady rest, the optimization of grinding parameters and the sound order of grinding process, for reducing crankshaft's elastic deformation is discussed. According to the different characteristics of vertical and horizontal deformations, the emphasis is putted on the study of corresponding control of steady rests'supporting force in each direction. The process for position control of steady rests'vertical supporting pads is based on the change of crank span. And based on the error of grinding depth for crank journal, a dynamic adjustment method is proposed to control the horizontal supporting force. Thanks to these control methods, crankshaft's elastic deformation can be decreased effectively.To position the crankshaft in machine coordinate system with the uniform distribution of grinding allowance automatically and exactly, the strategy for controlling touch trigger probe to realize the dynamic tracking measurement for cylindrical surface of crank pin is designed on the basis of measurement device grounded on coordinate measuring principle. Using formulae of all the crank pins'cylindrical surfaces obtained by measurement data, the method for crankshaft's automatic position is presented based on building optimization model of grinding allowance distribution. And the constraints are also established to avoid the negative grinding allowance of semi-manufactured crank pins. The hybrid particle swarm algorithm is adopted to solve the optimization model and the competition strategy based on unfeasible degree of solution is utilized to handle the constraints. The case study demonstrates that crankshaft can be located in machine coordinate system with uniform distribution of grinding allowance quickly and exactly through this optimization model and its solution algorithm.The intelligent decision system is designed to select the parameters of crankshaft tangential point tracing grinding. According to the classification of all the parameters, the parameter decision-making task is analyzed. CBR Agent,RBR Agent and MBR Agent are respectively designed as the basic, key and supplementary modules for inferring the main grinding parameters and grinding wheel dressing parameters. Using blackboard to mediate the communications and interactions among all the agents, the intelligent decision system consisting of HMI layer, decision layer and resource layer based on multi-agent framework is founded to realize the selection and optimization of the initial parameters of crankshaft tangential point tracing grinding.The state of grinding wheel has decisive influence on grinding quality of workpiece to a certain extent. Thus this dissertation analyzes the influence of change in grinding wheel dimesnsion on the ground contour of crank pin as well as the influence of grinding wheel wearing on surface waviness and roughness of the ground crank pin in details. According to the demand of controlling crankshaft tangential point tracing grinding, the measurement process based on contact senor is studied to survey grinding wheel radius. The ring-down count and root mean square of acoustic emission signal produced by the fragmentation and exfoliation of abrasive grains are applied to detect the contacting state of the grinding wheel and the workpiece or the dressing state of the grinding wheel. The grinding wheel wearing identification model is build using radical basis function neural networks (RBF NN)which takes the equivalent grinding thickness, the root mean square of AE signal and the average value of grinding wheel spindle power signal after polynomial regression analysis as inputs and the dressing signal of grinding wheel as output. The validity of this identification model is then verified by the experiment results.The compensation strategy, method and system for machining error of crank pin in tangential point tracing grinding are researched deeply in this dissertation. The additional impulses as the displacement correction of grinding carriage are given to numerical control system for reducing the errors, which is an effective compensation strategy apt for tangential point tracing grinding. Based on this strategy, an intelligent machining error on–line precompensation system is studied and its corresponding compensation algorithms and reasoning rules are also introduced. The RBF NN is used to decide the compensation regulation factor by which the intensity of error compensation can be controlled. Considering both the error compensation experience and its developmental trend, a new compensation method based on fuzzy reasoning and self-learning for crank pin's machining error is proposed. According to the radius error of crank pin and its change, the fuzzy reasoning module infers the compensation value for crank pin's machining error. The grinding experimental results show that the roundness error can be reduced effectively by both two methods, but the former method is more suitable for on-line compensation system and the latter one has higher compensation precision and efficiency.