Automatic Detection of Axes for Turning Parts
 
More details
Hide details
1
Institute of Manufacturing, Chair of Forming Technology, TU Dresden, Germany
 
 
Submission date: 2024-03-30
 
 
Final revision date: 2024-05-16
 
 
Acceptance date: 2024-05-16
 
 
Online publication date: 2024-05-22
 
 
Publication date: 2024-06-10
 
 
Corresponding author
Martin Erler   

Institute of Manufacturing, Chair of Forming Technology, TU Dresden, Dresden, Germany
 
 
 
KEYWORDS
TOPICS
ABSTRACT
This paper delves into a critical aspect of Computer-Aided Production Planning (CAPP): the automated detection of the main rotational axis in turning parts within Computer-Aided Drawings (CAD). The identification of the principal turning axis in CAD models presents numerous opportunities in the field of CAPP. In this study, the authors employ advanced surface segmentation techniques to analyse the surface geometry, pinpointing rotational surfaces within the CAD model. Subsequently, significant features are extracted from these identified rotational surfaces, and the necessary data for rotational centers are gathered. By fine-tuning the weighting of the data gathered, the approach can be tailored to suit various planning strategies. This approach has the potential to significantly enhance both the efficiency and accuracy of the automated production planning process for turning parts in CAPP.
 
REFERENCES (35)
1.
XU X., WANG L., NEWMAN S.T., 2011, Computer-Aided Process Planning – A Critical Review of Recent Developments and Future Trends, Int. J. Comput. Integr. Manuf. 24, 1–31.
 
2.
HALEVI G., WEILL R.D., 1995, Principles of Process Planning, Springer Netherlands, Dordrecht, 317–332.
 
3.
ISNAINI M.M., SHIRASE K., 2014, Review of Computer-Aided Process Planning Systems for Machining Operation – Future Development of a Computer-Aided process planning system, Int. J. Autom. Technol., 8, 317–332.
 
4.
LIU L., HUANG Z., LIU W., WU W., 2018, Extracting the Turning Volume and Features for a Mill/turn Part With Multiple Extreme Faces, Int. J. Adv. Manuf. Technol., 94, 257–280.
 
5.
MEERAN S., TAIB J.M., AFZAL M.T., 2003, Recognizing Features From Engineering Drawings Without Using Hidden Lines: A framework To Link Feature Recognition and Inspection Systems, Int. J. Prod. Res., 41, 465–495.
 
6.
SHI Y., ZHANG Y., XIA K., HARIK R., 2020, A Critical Review of Feature Recognition Techniques, Comput.-Aided Des. Appl., 17, 861–899.
 
7.
WARDLE S.., BAKER C.I., 2020, Recent Advances in Understanding Object Recognition in the Human Brain: Deep Neural Networks, Temporal Dynamics, and Context, F1000Research, 9.
 
8.
WEGENER K., WEIKERT S., MAYR J., MAIER M., ALI AKBARI V.O., POSTEL M., 2021, Operator Integrated – Concept for Manufacturing Intelligence, J. Mach. Eng., 21, 5–28.
 
9.
XU Y., ELGH F., ERKOYUNCU J.A., BANKOLE O., GOH Y., CHEUNG W.M., BAGULEY P., WANG Q., ARUNDACHAWAT P., SHEHAB E., NEWNES L., ROY R., 2012, Cost Engineering for Manufacturing: Current and Future Research, Int. J. Comput. Integr. Manuf., 25, 300–314.
 
10.
MOURTZIS D., 2021, Towards the 5th Industrial Revolution: a Literature Review and a Framework for Process Optimization Based on Big Data Analytics and Semantics, J. Mach. Eng., 21/3, 5-39.
 
11.
CHOUGULE P.D., KUMAR S., RAVAL H.K., 2014, Relating Product Manufacturing Decisions to Environmental and Cost Performance Using CAPP, Procedia Mater. Sci., 6, 476–481.
 
12.
YIP-HOI D., DUTTA D., 1997, Finding the Maximum Turnable State for Mill/turn Parts, Computer-Aided Design, 29, 12, 879-894.
 
13.
YUSOF Y., LATIF K., 2014, Survey on Computer-Aided Process Planning, Int. J. Adv. Manuf. Technol., 75, 77–89.
 
14.
SOORI M., ASMAEL M., 2021, Classification of Research and Applications of the Computer Aided Process Planning in Manufacturing Systems, Indep. J. Manag. Prod., 12, 1250–1281.
 
15.
NEIBEL B.W., 1965, Mechanized Process Selection for Planning New Design, ASME paper, 737.
 
16.
KYPRIANOU L., 1980, Shape Classification in Computer-Aided Design, Ph.D. Dissertation, University of Cambridge.
 
17.
SHAH J.J., ANDERSON D., KIM Y.S., JOSHI S., 2001, A Discourse on Geometric Feature Recognition from CAD Models, J. Comput. Inf. Sci. Eng., 1, 41–51.
 
18.
ANSALDI S., DE FLORIANI L., FALCIDIENO B., 1985, Geometric Modeling of Solid Objects by Using a Face Adjacency Graph Representation, ACM SIGGRAPH Comput. Graph., 19, 131–139.
 
19.
HENDERSON M.R., ANDERSON D.C., 1984, Computer Recognition and Extraction of form Features: A CAD/CAM link, Comput. Ind., 5, 329–339.
 
20.
WOO T.C, 1982, Feature Extraction by Volume Decomposition, Proceedings of Conference on CAD/CAM Technology in Mechanical Engineering, Cambridge, MA, 76–94.
 
21.
WOO T.C., 1983, Interfacing Solid Modeling to CAD and CAM: Data Structures and Algorithms for Decomposing a Solid, IEEE Computer, 17, 44–49.
 
22.
SAHAY A., GRAVEST G.R., PARKS C.M., MANN L., 1990, A Methodology for Recognizing Features in Two-Dimensional Cylindrical Part Designs, Int. J. Prod. Res., 28, 1401–1416.
 
23.
BEHANDISH M., NELATURI S., VERMA C.S., ALLARD M., 2019, Automated Process Planning for Turning: A Feature-Free Approach, Prod. Manuf. Res., 7, 415–432.
 
24.
ZUBAIR A.F., ABU MANSOR M.S., 2019, Embedding Firefly Algorithm in Optimization of CAPP Turning Machining Parameters for Cutting Tool Selections, Comput. Ind. Eng., 135, 317–325.
 
25.
RICO C., SUAREZ C., MATEOS S., CUESTA E., DUARTE A., 1997, An Automatic CAPP System for Rotational Parts, Presented at the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation Proceedings, EFTA ’97, IEEE, Los Angeles, CA, USA, 19–23.
 
26.
ZUBAIR A.F., ABU MANSOR M.S., 2018, Automatic Feature Recognition of Regular Features for Symmetrical and Non-Symmetrical Cylinder Part Using Volume Decomposition Method, Eng. Comput. 34, 843–863.
 
27.
RABBANI T., HEUVEL F., 2005, Efficient Hough Transfrom for Automatic Detection of Cylinders in Point Clouds, ISPRS WG III/3, III/4, V/3 Workshop Laser scanning, 60–65.
 
28.
KREVELD M., LÖFFLER M., 2008, Approximating Largest Convex Hulls for Imprecise Points, Journal of Discrete Algorithms, 6, 583–594.
 
29.
KIM I., CHO K., 1994, An Integration of Feature Recognition and Process Planning Functions for Turning Operation, Computers Ind. Eng., 27, 107–110.
 
30.
SHAMIR A., 2008, A Survey on Mesh Segmentation Techniques, Computer Graphics forum 27, 1539–1556.
 
31.
LEIRMO T.L., SEMENIUTA O., MARTINSEN K., 2020, Tolerancing from STL Data: A Legacy Challenge, Procedia CIRP 92, 218–223.
 
32.
QIU Z.M., WONG Y.S., FUH J.Y.H., CHEN Y.P., ZHOU Z.D., LI W.D., LU Y.Q., 2004, Geometric Model Simplification for Distributed CAD, Comput.-Aided Des., 36, 809–819.
 
33.
BATURYNSKA I., 2018, Statistical Analysis of Dimensional Accuracy in Additive Manufacturing Considering STL Model Properties, Int. J. Adv. Manuf. Technol., 97, 2835–2849.
 
34.
VALENTAN B., BRAJLIH T., DRSTVENSEK I., BALIC J., 2008, Basic Solutions on Shape Complexity Evaluation of STL Data, J. Achiev. Mater. Manuf. Eng., 26.
 
35.
KARAGÜZEL U., UYSAL E., BUDAK E., BAKKAL M., 2015, Analytical Modeling of Turn-Milling Process Geometry, Kinematics and Mechanics, Int. J. Mach. Tools Manuf., 91, 24–33.
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top