Single-Crystal Quality Detection and Evaluation Algorithms Applied to Roller Mill Orientation Instrument

Abstract

The roller mill orientation instrument (RMOI) integrates the processing and the orientation of single-crystal bars. This type of equipment realizes the integration of two processes: grinding of the single-crystal ingot and orientation of the single-crystal bar. This integration enhances production efficiency and the precision of the orientation. However, this type of approach has two significant constraints: 1) there is no quality detection algorithm for the plane of the crystal bar; it is, therefore, currently necessary to randomly find a crystal plane for orientation, which means that there is no guarantee of the quality of the plane after cutting and 2) there is a lack of evaluation methods to determine the overall quality of the crystal bar. This affects the production process and the requirements for the quality of the crystal bar and further affects the popularization and application of the equipment. Here, to overcome these constraints, we first propose a deep learning 1-D convolutional neural network (1-D-CNN) algorithm to perform feature extraction and supervised classification based on the rocking curve of the single crystal. In sharp contrast to the shallow learning method support vector machine (SVM), SVM feature extraction is insufficient, and the accuracy is low. The average accuracy of 1-D-CNN is 92.00%. This realizes the quality requirements for the successful detection of the crystal plane. Next, using the obtained quality detection results of each crystal plane, a hybrid algorithm combining improved Canopy (ICanopy) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means algorithms is presented, which improved the average accuracy by 10% compared with the traditional Canopy- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means algorithm, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> NN) algorithm is further utilized. Finally, an ICanopy- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> NN algorithm is formed, which realizes the overall quality evaluation of the crystal bar under various situations, and the average accuracy increases by 3.33%–90%. The effectiveness of the proposed algorithms is demonstrated by the analysis of results obtained from practical data.