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

Sensors (Basel, Switzerland), 2023-01, Vol.23 (2), p.785 [Peer Reviewed Journal]

2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23020785 ;PMID: 36679581

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  • Title:
    Detecting Machining Defects inside Engine Piston Chamber with Computer Vision and Machine Learning
  • Author: Abagiu, Marian Marcel ; Cojocaru, Dorian ; Manta, Florin ; Mariniuc, Alexandru
  • Subjects: Algorithms ; Automobile industry ; Cables ; Chambers ; Charge coupled devices ; Computer vision ; Computers ; Engine blocks ; Ethernet ; Humans ; Image acquisition ; Industrial applications ; industry ; Inspection ; Machine Learning ; Machine vision ; Machining ; Manufacturing ; Neural networks ; Neural Networks, Computer ; Production lines ; robotics ; Sensors ; Software
  • Is Part Of: Sensors (Basel, Switzerland), 2023-01, Vol.23 (2), p.785
  • Description: 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).
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23020785
    PMID: 36679581
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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