@Article{M-10336,
AUTHOR = {Agarwal, Aditya and Srivastava, Vineet and Ujawane, Raunak and Mali, Balaji},
TITLE = {Enhanced Vision-Based Surface Value Roughness Detection for Industry 4.0 Quality Assurance},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {3},
YEAR = {2025},
NUMBER = {1},
ARTICLE-NUMBER = {M-10336},
URL = {https://isrdo.org/journal/SRJSET/currentissue/enhanced-vision-based-surface-value-roughness-detection-for-industry-40-quality-assurance},
ISSN = {2584-0584},
ABSTRACT = {Surface roughness is a critical factor affecting friction, wear, and corrosion resistance of machined components. Traditional contact-based roughness testers provide reliable measurements (e.g., Roughness Average Ra), but manual reading and recording of results can be slow, error-prone, and lack traceability. This paper presents an enhanced automated surface roughness detection system built upon a prior vision-based pipeline that used YOLOv5 object detection and EasyOCR for reading Ra values. The new system extends the capability to five surface texture parameters – Ra, Rz, Rsk, Rpc, and Rpm – delivering a comprehensive roughness profile in real time. A Python-based user interface (UI) integrates the detection pipeline with real-time statistical process control charts (Six Sigma), process capability indices (Cp, Cpk), and data logging for Industry 4.0 traceability. The implementation includes a refined image preprocessing stage (resolution-specific letterboxing, dynamic padding to capture negative value signs), improved bounding box scaling and text region extraction, metric-specific text cleaning rules, and validation of readings against expected limits. Multithreading and caching optimizations reduce processing latency, enabling near-instant measurement updates. Over 2,000 test samples, the system achieved ~98.5% accuracy in correctly identifying all five metrics per sample, outperforming earlier OCR-only approaches (e.g., Tesseract) in speed and robustness. The paper provides a detailed literature review of vision-based OCR in industrial metrology and surface roughness evaluation, discusses the limitations of previous methods, and explains the architectural choices that enable high accuracy and industrial reliability. The proposed solution demonstrates significant improvements in quality monitoring, historical data capture, and process automation, illustrating a practical step toward smart manufacturing and Quality 4.0.},
DOI = {}
}