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Double-sided lapping is an ultra-precision machining method capable of obtaining high-precision surface. However, during the lapping process of thin pure copper substrate, the workpiece will be warped due to the influence of residual stress-related, including the processing stress and initial residual stress, which will deteriorate the flatness of the workpiece and ultimately affect the performance of components. In this study, finite element method (FEM) was adopted to study the effect of residual stress-related on the deformation of pure copper substrate during double-sided lapping. Considering the initial residual stress of the workpiece, the stress caused by the lapping and their distribution characteristics, a prediction model was proposed for simulating workpiece machining deformation in lapping process by measuring the material removal rate of the upper and lower surfaces of the workpiece under the corresponding parameters. The results showed that the primary cause of the warping deformation of the workpiece in the double-sided lapping is the redistribution of initial residual stress caused by uneven removal of double-sided material. The finite element simulation results were in good agreement with the experimental results.

Double-sided lapping is an precision machining method capable of obtaining high-precision surface. However, during the lapping process of thin pure copper substrate, the workpiece will be warped due to the influence of residual stress, including the machining stress and initial residual stress, which will deteriorate the flatness of the workpiece and ultimately affect the performance of components. In this study, finite element method (FEM) was adopted to study the effect of residual stress-related on the deformation of pure copper substrate during double-sided lapping. Considering the initial residual stress of the workpiece, the stress caused by the lapping and their distribution characteristics, a prediction model was proposed for simulating workpiece machining deformation in lapping process by measuring the material removal rate of the upper and lower surfaces of the workpiece under the corresponding parameters. The results showed that the primary cause of the warping deformation of the workpiece in the double-sided lapping is the redistribution of initial residual stress caused by uneven material removal on the both surfaces. The finite element simulation results were in good agreement with the experimental results.

The surface quality of Lithium Niobate (LiNbO3) has a significant influence on photonics and optoelectronics components. However, the prediction and optimization models of surface roughness were not accurate due to the random parameters. Hence, a prediction model of surface roughness is established based on an improved neural network, and a new method is proposed to optimize the arguments in chemical mechanical polishing (CMP) process. In the model, the structure of the neural network is optimized according to data features rather than choosing networks randomly. To improve the model accuracy, the optimal number of hidden layers is 4 and the corresponding amounts of nodes in each layer are 46, 34, 28, and 33, respectively. ReLU function is chosen as activation function. Subsequently, the relationship between surface roughness and processing parameters is built and the variation process of surface roughness is particularly considered. The accuracy and generalization ability of the model is verified by the experiments with the Mean Absolute Percentage Error (MAPE) of 6.42% and Root Mean Square Error (RMSE) of 0.403. Furthermore, the Genetic Algorithm (GA) method based on the selected model is applied to optimize processing arguments under the target surface roughness value of 0.3 nm. The accuracy of the fusion model is also validated by experiments with an error of 13.3%.