CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks
Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become m...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/5/1575 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850031271556677632 |
|---|---|
| author | Mühenad Bilal Ranadheer Podishetti Tangirala Sri Girish Daniel Grossmann Markus Bregulla |
| author_facet | Mühenad Bilal Ranadheer Podishetti Tangirala Sri Girish Daniel Grossmann Markus Bregulla |
| author_sort | Mühenad Bilal |
| collection | DOAJ |
| description | Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features. |
| format | Article |
| id | doaj-art-46ef3e97e5004aff8715d9f33650f362 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-46ef3e97e5004aff8715d9f33650f3622025-08-20T02:59:01ZengMDPI AGSensors1424-82202025-03-01255157510.3390/s25051575CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification TasksMühenad Bilal0Ranadheer Podishetti1Tangirala Sri Girish2Daniel Grossmann3Markus Bregulla4Application Cluster “Digital Production” Progarm, AImotion Bavaria Instiutute, Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, GermanyApplication Cluster “Digital Production” Progarm, AImotion Bavaria Instiutute, Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, GermanyApplication Cluster “Digital Production” Progarm, AImotion Bavaria Instiutute, Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, GermanyApplication Cluster “Digital Production” Progarm, AImotion Bavaria Instiutute, Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, GermanyApplication Cluster “Digital Production” Progarm, AImotion Bavaria Instiutute, Technische Hochschule Ingolstadt (THI), Esplanade 10, 85049 Ingolstadt, GermanySustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features.https://www.mdpi.com/1424-8220/25/5/1575tool classificationmachining tools: Grad-CAM CNNResNet50training performanceneural network performancesustainability |
| spellingShingle | Mühenad Bilal Ranadheer Podishetti Tangirala Sri Girish Daniel Grossmann Markus Bregulla CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks Sensors tool classification machining tools: Grad-CAM CNN ResNet50 training performance neural network performance sustainability |
| title | CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks |
| title_full | CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks |
| title_fullStr | CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks |
| title_full_unstemmed | CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks |
| title_short | CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks |
| title_sort | cnn based classification of optically critical cutting tools with complex geometry new insights for cnn based classification tasks |
| topic | tool classification machining tools: Grad-CAM CNN ResNet50 training performance neural network performance sustainability |
| url | https://www.mdpi.com/1424-8220/25/5/1575 |
| work_keys_str_mv | AT muhenadbilal cnnbasedclassificationofopticallycriticalcuttingtoolswithcomplexgeometrynewinsightsforcnnbasedclassificationtasks AT ranadheerpodishetti cnnbasedclassificationofopticallycriticalcuttingtoolswithcomplexgeometrynewinsightsforcnnbasedclassificationtasks AT tangiralasrigirish cnnbasedclassificationofopticallycriticalcuttingtoolswithcomplexgeometrynewinsightsforcnnbasedclassificationtasks AT danielgrossmann cnnbasedclassificationofopticallycriticalcuttingtoolswithcomplexgeometrynewinsightsforcnnbasedclassificationtasks AT markusbregulla cnnbasedclassificationofopticallycriticalcuttingtoolswithcomplexgeometrynewinsightsforcnnbasedclassificationtasks |