Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining

This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control.

Authors

Institutions

Publication Details

Journal
Journal of Manufacturing and Materials Processing
Published
2024-10-31
DOI
https://doi.org/10.3390/jmmp8060244
Citations
26
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
5.53
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining

Oğuzhan Der, Mücahit Çalışan, Fatih Akkoyun, Nevzat Olgun et al.
26 citations
Journal of Manufacturing and Materials Processing
Advanced machining processes and optimization
5.53
article

Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining

Oğuzhan Der, Mücahit Çalışan, Fatih Akkoyun, Nevzat Olgun, Ganesh R. Chate, K. N. Bharath, Ramazan Şener, Manjunath Patel Gowdru Chandrashekarappa, Ali Erçetin
article en
26 citations

Abstract

This paper systematically explores the applications of image processing techniques in machined surface analysis, a critical area in industries like manufacturing, aerospace, automotive, and healthcare. It examines the integration of image processing in traditional Computer Numerical Control (CNC) machining and micromachining, focusing on its role in tool wear analysis, workpiece detection, automatic CNC programming, and defect inspection. With AI and machine learning advancements, these technologies enhance defect detection, surface texture analysis, predictive maintenance, and quality optimization. The paper also discusses future advancements in high resolutions, 3D imaging, augmented reality, and Industry 4.0, highlighting their impact on productivity, precision, and challenges such as data privacy. In conclusion, image processing remains vital to improving manufacturing efficiency and quality control.

Journal of Manufacturing and Materials ProcessingVol. 8(6)
Izmir University (TR), University B.D.T College of Engineering (IN), Bingöl University (TR), PES University (IN), Bandırma Onyedi Eylül University (TR), İzmir Demokrasi Üniversitesi, Afyon Kocatepe University (TR), Visvesvaraya Technological University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Advanced machining processes and optimization
5.53
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining — Oğuzhan Der, Mücahit Çalışan, et al. · Journal of Manufacturing and Materials Processing (2024) | TGRS Research Map | TGRS