Scholarlink

<p>Residual particles generated during automotive transmission manufacturing can compromise system reliability, functional stability, and driving safety if they are not effectively removed before assembly. Those particles – burr, cast, chip, debris - can be generated from casting, CNC milling, CNC turning, high pressure water jet deburring, brushing deburring or unknown sources. Identifying the production origin of these particles is therefore essential for cleanability control and corrective action in manufacturing. This study proposes a morphology-based particle-classification framework using an artificial neural network (ANN) and ten extracted descriptors, including geometric features, fractal dimension, and curvature-related parameters. A two-stage classification strategy was developed to evaluate performance under different levels of classification difficulty. In Stage A, particles in the 300–1000&#xa0;μm range were classified into seven representative classes, while five difficult subclasses were grouped into an “Unknown” category. Under this practical screening setting, the ANN achieved an overall accuracy of 91.8%. In Stage B, the original 12 classes were restored for particles in the 50–300&#xa0;μm range, and the overall accuracy decreased to 65.3% because of increased morphological overlap among fine-grained subclasses. The results show that ANN is effective for representative particle screening in automotive transmission manufacturing, but ANN alone is not sufficient for highly reliable recognition of small and morphologically similar particles. These findings provide a practical foundation for future integration of richer descriptors and complementary algorithms to support more intelligent cleanability analysis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Particle classification in automotive transmission manufacturing by artificial neural networks

  • 期刊论文

  • 发表日期：

    2026年5月27日

查看全文
  • Minh-Thuan Tran,
  • Van-Tien Truong,
  • Trong-Dat Huynh,
  • Van-Tron Tran,
  • Huy-Tuan Pham,
  • Quoc-Bao Phan

摘要

Residual particles generated during automotive transmission manufacturing can compromise system reliability, functional stability, and driving safety if they are not effectively removed before assembly. Those particles – burr, cast, chip, debris - can be generated from casting, CNC milling, CNC turning, high pressure water jet deburring, brushing deburring or unknown sources. Identifying the production origin of these particles is therefore essential for cleanability control and corrective action in manufacturing. This study proposes a morphology-based particle-classification framework using an artificial neural network (ANN) and ten extracted descriptors, including geometric features, fractal dimension, and curvature-related parameters. A two-stage classification strategy was developed to evaluate performance under different levels of classification difficulty. In Stage A, particles in the 300–1000 μm range were classified into seven representative classes, while five difficult subclasses were grouped into an “Unknown” category. Under this practical screening setting, the ANN achieved an overall accuracy of 91.8%. In Stage B, the original 12 classes were restored for particles in the 50–300 μm range, and the overall accuracy decreased to 65.3% because of increased morphological overlap among fine-grained subclasses. The results show that ANN is effective for representative particle screening in automotive transmission manufacturing, but ANN alone is not sufficient for highly reliable recognition of small and morphologically similar particles. These findings provide a practical foundation for future integration of richer descriptors and complementary algorithms to support more intelligent cleanability analysis.