Engineering Framework for Intelligent Vision Inspection Systems (EF-IVIS): A Systems Engineering Methodology

Abstract

Industrial vision inspection systems increasingly combine machine vision, industrial metrology, artificial intelligence, industrial communication, and manufacturing automation within integrated cyber-physical production environments. Although extensive research addresses individual technologies, comparatively limited methodological support is available for coordinating engineering decisions across the complete development lifecycle of such systems. This paper presents the Engineering Framework for Intelligent Vision Inspection Systems (EF-IVIS), a domain-specific systems engineering approach organized into seven interconnected engineering domains: inspection requirements, image acquisition, illumination engineering, measurement strategy, artificial intelligence, industrial integration, and validation and continuous improvement. EF-IVIS was developed through a bottom-up synthesis of established engineering principles and four complementary industrial investigations addressing AI-based defect detection, measurement strategies, illumination conditions, and PLC-supported integration. The empirical assessment distinguishes direct experimental evidence, direct industrial evidence, cross-cutting retrospective evidence, and methodological support. Direct evidence supports selected aspects of image acquisition and the illumination engineering, measurement strategy, artificial intelligence, and industrial integration domains, whereas inspection requirements and validation and continuous improvement are supported primarily through cross-study synthesis and established systems engineering principles. The framework introduces cross-domain dependencies, application-specific Decision Gates, and feedback mechanisms for iterative engineering assessment. A worked industrial-integration example demonstrates how reported technical evidence can be translated into Decision Gate criteria without treating the reported values as universal thresholds. The present study supports the internal consistency and practical relevance of EF-IVIS, while its usability, repeatability, transferability, and complete lifecycle effectiveness require independent prospective validation.