A Prototype for Computing the Distance of Features of High-Pressure Die-Cast Aluminum Products

Abstract

Automotive manufacturers are changing their product models faster due to the customization of users’ demands. In response, suppliers must react by improving the flexibility of their means of production and making the changeover process more efficient and agile to avoid monetary losses. This article reports a prototype that uses computer vision, deep learning algorithms, and mathematical methods to derive the spatial position (x, y, z) of features of the machined parts of high-pressure die-casting (HPDC) aluminum products. It uses an RGB-D sensor to capture and process an image with the you only look once (YOLO) algorithm to determine the center of specific workpiece features. With this information, the feature depth of each center is obtained from the depth matrix and then introduced into a polynomial regression formula to acquire the spatial position (x, y, z) in millimeters. The prototype is a complementary tool for quickly sampling workpieces in the production line and verifying that they meet the requirements and specifications of spatial distances among features. With this evidence, only if necessary, the piece is sent for further and comprehensive measurement by a coordinate-measuring machine (CMM), in line with the accuracy demanded by the automotive industry.

Keywords

• noninvasive measurement
• deep learning
• computer vision
• YOLO