In the internal thread extrusion forming, if the process parameters are not selected properly, the extrusion torque will increase, the extrusion temperature will be too high, or even the tap will break. In order to obtain effective process parameters under certain working conditions, this paper uses a combination of numerical simulation and process experiment to analyze the influence of the bottom hole diameter, extrusion speed, and friction factor on the extrusion torque and extrusion temperature. Through an orthogonal experiment, the significant influence law of different process parameters on the extrusion torque and extrusion temperature was studied, and the order of their influence was determined. Based on the optimal process parameters, numerical simulations and process tests were carried out, and the extrusion effect and related parameters were compared and analyzed. The results show that the extruded thread has clear contour, uniform tooth pitch, complete tooth shape, and good flatness. Compared with before optimization, the maximum extrusion torque has been reduced by 37.15%, the maximum temperature has been reduced by 29.72%, and the extrusion quality has been improved. It shows that the optimized method and optimized process parameters have good engineering practicability.
The extrusion process parameters have a great impact on the quality of the extrusion thread. In order to ensure the extrusion quality and the smooth progress of the extrusion process, a prediction model based on BP-GA neural network is established. The model takes the diameter of the bottom hole, extrusion speed, and friction coefficient as the input layer, and the extrusion torque, extrusion temperature, and tooth height as the output layer. After training the model with sample data, the extrusion internal thread quality prediction is carried out. The results show that the BP-GA neural network prediction model has a high accuracy in predicting the extrusion torque, extrusion temperature and tooth height rate during the cold extrusion of internal threads. The error between the experimental value and the predicted value of the extrusion torque is between 10% and 15%, the experimental value of the extrusion temperature is consistent with the predicted value, and the error between the experimental value and the predicted value of the tooth height rate is less than 5%. The BP-GA neural network prediction model can accurately predict the extrusion temperature, extrusion torque, and tooth height rate, providing a new way for the re-search of internal thread cold extrusion technology.
The diameter of the threaded bottom hole determines whether the extrusion can proceed smoothly and the fullness of the tooth shape after extrusion. The tooth height rate is an important indicator of the strength of the threaded connection. In order to establish the relationship between the diameter of the bottom hole and the tooth height of the extruded internal thread, this paper takes the aluminum alloy M8×1.25 mm internal thread as an example, and uses a method of combining numerical simulation and process test. Obtained the changing law of internal thread profile and tooth height rate of extruded workpieces with different bottom hole diameters. Using MATLAB to fit the numerical simulation results, the relationship between the tooth height of the internal thread and the diameter of the prefabricated bottom hole of the workpiece was obtained. The reliability of the numerical simulation results and the feasibility of the fitting formula are verified through experiments. The results show that the diameter of the prefabricated bottom hole of the workpiece is controlled within 7.33~7.39 mm when the M8×1.25 mm internal thread is processed by the cold extrusion process. The high rate of the internal thread after extrusion meets the requirements of thread connection strength, and the internal thread has high forming quality and good surface quality.
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