Residual stress plays a vital role in the structural strength and stability. The determination of residual stress at single-point has become mature at present. However, the method to determine residual stress distribu...Residual stress plays a vital role in the structural strength and stability. The determination of residual stress at single-point has become mature at present. However, the method to determine residual stress distribution is still in shortage. For this problem, a finite element approach combined with slot milling method was developed in this study. In the method, firstly a slot is milled on the specimen surface to release the residual stress and then the released displacement field is measured by optical method, such as digital image correlation （DIC）, finally the finite element approach is used to determine the residual stress distribution along the slot. In order to verify the feasibility of the method, it was applied to study the residual stress introduced by shot peening, mainly about the stress distribution along the direction vertical to the shot peened surface. Since the influence depth of shot peening was too small, we utilized focused ion beam （FIB） to determine the microscale residual stress distribution. The result measured by X-ray diffraction （XRD） demonstrated that the method was feasible to determine the residual stress distribution.展开更多
In this paper the CNC machining of St52 was modeled using an artificial neural network(ANN)in the form of a four-layer multi-layer perceptron(MLP).The cutting parameters used in the model were cutting fluid flow,feed ...In this paper the CNC machining of St52 was modeled using an artificial neural network(ANN)in the form of a four-layer multi-layer perceptron(MLP).The cutting parameters used in the model were cutting fluid flow,feed rate,spindle speed and the depth of cut and the model output was the tool life.For obtaining more accuracy and spending less time Taguchi design of experiment(DOE)has been used and correlation between the output of the ANN and the experimental results was 96%.Further optimization process has been done by use of a genetic algorithm(GA).After optimization process tool life was increased about 8%equal to 33 min and was corroborated by experimental tests.This demonstrates that the coupling of an ANN with the GA optimization technique is a valid and useful approach to use.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.11672153,11232008 & 11227801)
文摘Residual stress plays a vital role in the structural strength and stability. The determination of residual stress at single-point has become mature at present. However, the method to determine residual stress distribution is still in shortage. For this problem, a finite element approach combined with slot milling method was developed in this study. In the method, firstly a slot is milled on the specimen surface to release the residual stress and then the released displacement field is measured by optical method, such as digital image correlation （DIC）, finally the finite element approach is used to determine the residual stress distribution along the slot. In order to verify the feasibility of the method, it was applied to study the residual stress introduced by shot peening, mainly about the stress distribution along the direction vertical to the shot peened surface. Since the influence depth of shot peening was too small, we utilized focused ion beam （FIB） to determine the microscale residual stress distribution. The result measured by X-ray diffraction （XRD） demonstrated that the method was feasible to determine the residual stress distribution.
文摘In this paper the CNC machining of St52 was modeled using an artificial neural network(ANN)in the form of a four-layer multi-layer perceptron(MLP).The cutting parameters used in the model were cutting fluid flow,feed rate,spindle speed and the depth of cut and the model output was the tool life.For obtaining more accuracy and spending less time Taguchi design of experiment(DOE)has been used and correlation between the output of the ANN and the experimental results was 96%.Further optimization process has been done by use of a genetic algorithm(GA).After optimization process tool life was increased about 8%equal to 33 min and was corroborated by experimental tests.This demonstrates that the coupling of an ANN with the GA optimization technique is a valid and useful approach to use.
