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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Automated Error Detection in Technical Drawings Using Machine Learning Algorithms: A Comprehensive Review |
| Authors Name: | Urmila Nagargoje , Amol Ghule , Sidhant Tarte , Khushi Varma , Mayur Todmal |
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IJRTI_203133
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| Published Paper Id: | IJRTI2504291 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Technical drawings play a pivotal role in the fields of engineering design, architecture, and manufacturing, where precision and accuracy are paramount. These drawings serve as the primary medium for communicating intricate design specifications, which must be error-free to ensure the integrity and functionality of the final product. However, manual error detection in technical drawings is a labor-intensive process that is susceptible to human oversight, particularly as the complexity and size of these drawings increase. In response to this challenge, recent advancements in machine learning (ML) have paved the way for automating error detection, offering the potential to significantly improve accuracy, efficiency, and consistency. This paper presents a comprehensive review of the application of machine learning algorithms for automated error detection in technical drawings. It explores a wide range of techniques, including supervised and unsupervised learning models, deep learning methods, and anomaly detection approaches, all of which have shown promising results in identifying various types of errors such as missing dimensions, incorrect scaling, and annotation discrepancies. The review also delves into key aspects of data preparation, such as the use of labeled datasets, feature extraction methods, and the integration of domain-specific knowledge for enhanced model performance. Furthermore, the paper evaluates the performance metrics commonly used to assess the efficacy of ML models in this domain, including accuracy, precision, recall, and F1 score, and discusses the challenges associated with these metrics in the context of technical drawings. Despite the promising progress, several challenges remain, such as improving the robustness of ML models to handle complex, real-world drawing errors and enhancing their interpretability to ensure that detected errors can be easily understood and corrected by engineers. Additionally, the generalization of these models across diverse industries and drawing styles remains an ongoing area of research. This study underscores the growing importance of integrating artificial intelligence (AI) technologies into engineering workflows, with the goal of streamlining design processes, enhancing quality control, and fostering innovation in the development of technical systems and products. The paper concludes by highlighting potential future directions for research, including the exploration of hybrid AI approaches, the development of more comprehensive and diverse datasets, and the use of explainable AI techniques to increase model transparency and trust. |
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| Cite Article: | "Automated Error Detection in Technical Drawings Using Machine Learning Algorithms: A Comprehensive Review", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c590-c595, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504291.pdf |
| Downloads: | 000294 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504291
Registration ID:203133
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: c590-c595 Country: Ahmadnagar, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504291 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504291 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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