Abstract
Since small and medium enterprises (SME) generate the most of gross domestic product and employment opportunities in developed countries, further progress of the Industry 4.0 strongly relies on the development of dedicated solutions for SMEs-specific problems. Dimensional inspection of extruded rubber profiles represents one such an open issue in the automotive industry, which currently requires a manual measurement and comparison of profiles' cross section with the corresponding technical drawings. Starting from the requirements acquired from the industry practice, this study proposes a novel solution that automates all steps during the inspection process allowing an operator to make the final decision with respect to his observations. The proposed workflow includes the following steps: 1) image acquisition, 2) system calibration, 3) profile segmentation, 4) landmark registration and 5) augmentation of the referent technical drawing over the acquired image. The overall solution was developed by using a single camera and dedicated algorithms for profile detection and augmentation of the referent technical drawing. The extensive validation showed that the solution increased operators' productivity and reliability by a considerable margin (~6%), while it remains affordable, user-friendly and generic.
Highlights
There is no strict definition of the term Industry 4.0 (I4.0), in its wider form, it refers to the process of adaptation and transformation of the global industry under the influence of ongoing technological advance [1], [2]
Regarding the Quality 4.0 (Q4.0) pillar, which is the subject of this study, major challenges are related to the digitization and automation of the quality assurance (QA) process
This study focuses on the inspection of extruded rubber profiles produced for the needs of automotive industry
Summary
There is no strict definition of the term Industry 4.0 (I4.0), in its wider form, it refers to the process of adaptation and transformation of the global industry under the influence of ongoing technological advance [1], [2]. Despite the fact that the greatest progress has been made in the field of manufacturing automation [6], it is well known that shifting towards the paradigm of I4.0 will change the way how companies are carrying out their other business activities [7] With this in mind, as well as the complexity and speed of the technological development in the twenty-first century, it is more precise to discuss the branches of I4.0, such as: Quality 4.0 [8], Maintenance 4.0 [9], [10], Safety 4.0 [11], Cybersecurity 4.0 [11], Operator 4.0 [12], Logistics 4.0 [13], etc. CV uses various artificial intelligence (AI) algorithms for analysis and interpretation of images collected with digital cameras [15], [16]
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