Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning

Abstract

This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection performed by a human operator with a computer vision application. We started by exploring different machine vision applications used in the manufacturing environment for several types of operations, and how machine learning is being used in robotic industrial applications. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The hardware will be used in the acquisition of the images, and for processing, a new system will be implemented with a human–machine interface, user controls, and communication with the main production line. Main results and conclusions highlight the efficiency of the CCD (charged-coupled device) sensors in the manufacturing environment and the robustness of the machine learning algorithms (convolutional neural networks) implemented in computer vision applications (thresholding and regions of interest).

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“…In the field of industrial production, an increasing number of visual detection systems employing deep learning are being applied [7][8][9]. The study presented in [10] explores the application of deep learning in the Automated Optical Inspection (AOI) of ceramic substrates in circuit images, aiming to develop an automated defect detection system capable of identifying types and locations of defects.…”

Advanced Intelligent Control in Robots

“…“Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning” [ 17 ] develops robotic industrial applications for automotive manufacturing with the main goal of replacing the visual inspection performed by a human operator with a computer vision application. A machine leaning algorithm which has conventional processing and a prediction method that uses a machine learning model is established.…”

An approach for recognizing surface defects in products with a complex helical shape

“…Solutions search processes are the most popular and successfully implemented in automated production. Visual control by computer vision has great potential for creating a unified digital industry in diferent field: metallurgy, automotive industry, mechanical engineering, machine tool industry [1][2][3][4][5][6][7]. Detection of defects on the surface at the initial stages of production allows you to reduce costs and save time during production [8][9][10][11][12].…”