| dc.description.abstract | Recently, laser has a great influence in the industry. It can produce products through different materials by different parameters. Searching for the best parameters often consumes a lot of materials and increases a lot of cost too much when cutting expensive materials like diamond, sapphire. In order to save the cost, transfer learning is an available option to use cheap material to train a model and then transfer to expensive material model.
According to CNN has significant success in images recognition and precedents for transfer learning, we encode the power, repetition rate and cutting speed into images as a dataset. In this study, an optical fiber laser cutting machine was used to cut stainless steel and silicon steel sheets, the stainless steel is cheaper than silicon. First, train a stainless steel training model, and transfer the stainless steel model to the silicon steel training model through transfer learning, which can reduce the training data and the silicon steel sheets. We use the CNN to construct a cutting quality prediction model. This model uses the images dataset as the input, and derives the average width of the cutting kerf and the standard deviation. In the neural network part, three kinds of structures are used to verify suitable models for this case, and the currently pretrained models on the market (VGG16, ResNet50, GoogleNet) are applied to these structure to accelerate the process of calculation.
This experiment uses 24 sets of different cutting parameter combinations, and actually cuts 120 stainless steel cutting lines and 48 silicon steel sheets cutting lines. In the training part of the neural network, the cutting quality of the 24 sets of combinations is well predicted, and the mean square error of the verification data is about 0.0263. We also got good results on the transfer learning mode, the mean square error of the verification data is about 0.0162l. After the above experiments, the prediction model of the laser kerf width established by this experiment can effectively predict on the different materials, it also can reduce the waste of new materials in the experiment, and reduce the cost. | en_US |