The Research On The Stability For The Large-scale Crankshaft CNC Tangential Point Tracing Grinding

The Large-scale Crankshaft is one of the key components of marine diesel engine, heavy equipment and power generation equipment. Its finishing process is conducted by grinding process which guarantees the final surface quality,size precision and position accuracy. However,the chatter in the grinding process has a serious effect on the quality and the machining efficiency of the crankshaft, and can even lead to the damage of the grinding wheel and the machine tool. Study on the stability of the dynamic machining system is very important for the realization of the chatter free grinding.This dissertation focuses on the research of the stability of the Crankshaft Grinding System. The dynamic model of crankshaft grinding was constructed based on the analysis of tangential point tracing implementations on the crankshaft. According to the dynamic model, the calculation formula of the critical grinding depth was derived and the stability lobe diagrams were drawn. The stability of the grinding system was monitored by the intelligent recognition technology. In the light of the stability theory,the dynamic optimization of the whole life cycle was also studied. The scientific method will help design a new large-scale crankshaft grinding machine tool with high natural frequency and low total mass. The main research works and innovations are as follows:On the ground of the analysis of the basic structure of the grinding machine tool and the implementation method of tangential point tracing grinding,the dynamic model of the crankshaft grinding system is constructed according to Newton’s law of motion.Based on the principle of the regenerative chatter and the dynamic model of the system,the calculation formula of the critical grinding depth of the crankshaft is derived. By drawing large-scale crankshaft grinding stability Lobe diagram,the method for evaluating the stability of the crankshaft grinding regenerative chatter is proposed. According to the Lobe diagram,the influence of the stiffness of the grinding system and the change of the speed of the grinding wheel on the stability were studied.The detection system, composed of acoustic emission sensor, acceleration sensor and current sensor,is used to monitor the status of the large-scale crankshaft grinding machine tool. The system provided the experimental and the verification data for the grinding stability analysis. Study on optimizing the arrangement of acceleration sensor by using adaptive acceleration discrete multi-object particle swarm optimization algorithm.The sensor information was processed to present the true state of the grinding process for the correct judgement. We analyzed the acoustic emission signal(AE) by using Daubechies wavelet. The five high frequency details of the analysis,used as the independent time series and the Root Mean Square(RMS) of the high frequency detail coefficient,was chosen as the characteristic value. Support vector machine optimized by PSO was used to recognise the chatter.In order to meet the requirements of the stability of the crankshaft grinding process and the need for lightweight of the machine tool,the study on the multi-objective optimization design of large-scale crankshaft grinding machine tool was done with Response Surface Method(RSM) and the stability theory. The wall thickness of the main stress components of the grinding machine was used as a variable. The test points were selected by the method of Central Composite Design(CCD). The crankshaft grinding machine tool was analyzed with ANSYS to obtain the natural frequency and total mass. The quadratic polynomial response surface model was construced. With the first-order natural frequency and the total mass of the grinding machine as the optimization objective,the second-order and the third-order natural frequency as the constraint,the multi-objective optimization model of the large-scale crankshaft grinding machine tool is constructed. In order to obtain a better initial population,sample extraction by shifted Hammersley sampling method(SHS) and weighing sorting,the Multi-objective Genetic Algorithm(MOGA) was used to optimize the population which was obtained from the Pareto solution set. An optimal solution from the Pareto solution was selected by weigh balance between the first-order natural frequency and total quality. The first-order natural frequency of the system is increased and the total mass is reduced after optimization. It is proved that the optimization method based on the principle of stability has a better effect in practice.