TY - JOUR
AU - Ravindra, A.
AB - Industrial organizations are increasingly adopting computer-aided manufacturing (CAM) and computer-aided design (CAD) tools to streamline manufacturing and product design processes, reducing time and costs. However, the area of 3D computer-aided process planning (3DCAPP) still faces challenges, particularly in the domain of 3D feature recognition technology. Feature recognition plays a crucial role in advanced computer-aided process planning (ACAPP) by bridging the gap between computer-aided manufacturing (CAM) and computer-aided design and drafting (CADD). In this study, an automated feature recognition (AFR) approach is employed based on a neutral file format. Initially, geometrical and topological data are extracted from the neutral files, specifically the STEP file format. Subsequently, the automatic feature recognition process is performed to identify the extracted features for smart manufacturing applications. The implementation of automatic feature recognition techniques is essential for seamless data transfer between computer-aided process planning (CAAP) and computer-aided design (CAD) systems. For the data extraction and feature recognition process in the proposed method, a Python program is developed. The program successfully identifies various feature forms, including blind and through features, as well as void features. In addition, the Python program recognizes feature form parameters, such as length, width, depth, position, and radius. The effectiveness of the developed feature recognition system is demonstrated through several case studies and investigations, including a specific example presented in the study. In conclusion, this study presents a designed and manufactured system that showcases the efficiency of the developed feature recognition system. By leveraging automatic feature recognition techniques and utilizing a Python program for data extraction and feature analysis, the proposed method enhances the transfer of product data between computer-aided process planning and computer-aided design, contributing to the advancement of smart manufacturing processes.
TI - Automatic feature recognition from STEP file for smart manufacturing
JF - Progress in Additive Manufacturing
DO - 10.1007/s40964-024-00583-3
DA - 2024-12-01
UR - https://www.deepdyve.com/lp/springer-journals/automatic-feature-recognition-from-step-file-for-smart-manufacturing-pFnryX7SOY
SP - 2291
EP - 2311
VL - 9
IS - 6
DP - DeepDyve
ER -