Depth-Based Feature Recognition of Blind Pockets and Islands from STEP AP242 CAD Models for Milling Applications

Amirulihsan  Mohamad; Zammeri Abd Rahman; Saiful Bahri  Mohamed; Muhammad Haziq  Omar; Razwan  Rohimi; Muhammad Abdulrahim Rabbani  Md Sharizam; Tengku Mohd Sharir Tengku Sulaiman; Mohamad  Minhat; Wawan Trisnadi  Putra

doi:10.22153/kej.2026.06.002

المؤلفون

Amirulihsan Mohamad Sultan Zainal Abidin University https://orcid.org/0009-0001-9804-7821
Zammeri Abd Rahman Sultan Zainal Abidin University https://orcid.org/0000-0001-5582-8823
Saiful Bahri Mohamed Sultan Zainal Abidin University
Muhammad Haziq Omar Sultan Zainal Abidin University
Razwan Rohimi Sultan Zainal Abidin University https://orcid.org/0000-0003-0212-0914
Muhammad Abdulrahim Rabbani Md Sharizam Sultan Zainal Abidin University
Tengku Mohd Sharir Tengku Sulaiman Sultan Zainal Abidin University
Mohamad Minhat Technical University of Malaysia Malacca https://orcid.org/0009-0003-7347-5701
Wawan Trisnadi Putra Muhammadiyah University of Ponorogo

DOI:

https://doi.org/10.22153/kej.2026.06.002

الكلمات المفتاحية:

CNC; milling machine; geometric information; STEP AP242; feature recognition; blind pocket; island

الملخص

The identification of blind pocket and island features in modern computer-aided design (CAD) and in the manufacturing of 3D CAD models is crucial. It ensures the accuracy and efficiency in the computer numerical control (CNC) machining process. These features often pose challenges for automated detection. This study proposes an automated method for detecting blind pockets and islands in Standard for the Exchange of Product Model Data (STEP) AP242 files using a depth-based approach. The methodology involves analysing geometric information from the STEP file, arranging regions by depth (Z-values) and recognising between blind pocket and island features. The approach uses a series of geometric algorithms to detect edges, vertices and surfaces, followed by the identification of areas with different depths. Three case studies were employed to demonstrate the method’s ability and effectiveness in accurately detecting features. Results show that a feature recognition success rate of 100% was achieved for the case studies, implying that the developed method for blind pocket and island detection is effective and reliable.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

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