A Python-Based GUI Approach for Efficient Component Design of Compound Die for Composite Material: 3D and 2D CAD Modeling
DOI:
https://doi.org/10.18311/jmmf/2024/45765Keywords:
API Based CAD, GUI Based CAD Modeling, Knowledge Based System, Python ProgrammingAbstract
This article presents a Python-based parametric Computer-Aided Design (CAD) system coupled with the AutoCAD API for the automation of compound die design. The system seeks to minimize the duration allocated to repeated operations, therefore promoting innovation. The graphical user interface facilitates the straightforward entry of design parameters and the automated production of precise manufacturing drawings. Advanced approaches, including Finite Element Analysis (FEA), topology optimization, and machine learning, are utilized to improve product design, reduce material consumption, and forecast component lifespan under operational stressors. The technology markedly decreases design time relative to conventional methods, rendering it optimal for sectors emphasizing bulk customization and batch production. Moreover, it enhances Time-To-Market (TTM) by providing an efficient process from design to manufacturing, while reducing errors and material waste. This tool has the potential for extensive utilization in mechanical design, especially within small and mediumsized enterprises.
Downloads
Metrics
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Kumar S, Singh R. A knowledge-based system for selection of progressive die components. J Achiev Mater Manuf Eng. 2007; 20(1-2):475-78. https://doi.org/10.1504/IJCMSSE.2007.013846
Kashid S, Kumar S. An expert system for selection of components of compound die. J Adv Manuf Syst. 2014; 13(3):181-95. https://doi.org/10.1142/S0219686714500115
Kumar S, Singh R. A low-cost knowledge base system framework for progressive die design. J Mater Process Technol. 2004; 153:958-64. https://doi.org/10.1016/j.jmatprotec.2004.04.236
Hambli R, Guerin F. Application of a neural network for optimum clearance prediction in sheet metal blanking processes. Int J Adv Manuf Technol. 2003; 22(1-2):20-5. https://doi.org/10.1007/s00170-0021437-5
Nee AYC, Foong KY. Some considerations in the design and automatic staging of progressive dies. J Mater Process Technol. 1992; 29(1-3):147-58. https://doi.org/10.1016/0924-0136(92)90431-Q
bin Ab Kadir AR, Wan NNI, bin Said MS, binti Zubir B, bin Ibrahim MZ, Krishnan P. Design, and analysis of punch and die of a micro blanking tool. Int J Recent Technol Eng. 2019; 8(4):827-33. https://doi.org/10.35940/ijrte.D7416.118419
Kumar S, Singh R. An automated design system for progressive die. Expert Syst Appl. 2011; 38(4):4482-9. https://doi.org/10.1016/j.eswa.2010.09.121
Shaheen W, Kanapathipillai S, Mathew P, Prusty BG. Optimization of compound die piercing punches and double cutting process parameters using finite element analysis. Proc Inst Mech Eng Part B J Eng Manuf. 2020; 234(1-2):3-13. https://doi.org/10.1177/0954405419855507
Kadarno P, Mori KI, Abe Y, Abe T. Punching process including thickening of hole edge for improvement of fatigue strength of ultra-high strength steel sheet. Manuf Rev. 2014; 1:4. https://doi.org/10.1051/mfreview/2014003
Kumar RM. Optimization of die design parameters in blanking operation using genetic algorithm. i-Manager’s J Mech Eng. 2016; 6(2):30. https://doi.org/10.26634/jme.6.2.4890
Skampardonis N, Tsirkas S, Grammatikopoulos S. Design, and analysis of an industrial, progressive die for cutting and forming. 2021. https://doi.org/10.21203/rs.3.rs-262802/v1
Shaheen W, Kanapathipillai S, Mathew P, Prusty BG. Optimization of compound die piercing punches and double cutting process parameters using finite element analysis. Proc Inst Mech Eng Part B J Eng Manuf. 2020; 234(1-2):3-13. https://doi.org/10.1177/0954405419855507
Mehta AM, Rus D. An end-to-end system for designing mechanical structures for print-and-fold robots. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA); 2014; 1460-5. https://doi.org/10.1109/ICRA.2014.6907044
Machado F, Malpica N, Borromeo S. Parametric CAD modeling for open-source scientific hardware:comparing OpenSCAD and FreeCAD Python scripts. PLoS One. 2019; 14(12):1-30. https://doi.org/10.1371/journal.pone.0225795
Tezzele M, Demo N, Mola A, Rozza G. PyGeM: Python geometrical morphing. Softw Impacts. 2021; 7:100047. https://doi.org/10.1016/j.simpa.2020.100047
Salunkhe S, Kumar S. Applications of artificial neural network to sheet metal work - A review. Am J Intell Syst. 2013; 2(7):168-76. https://doi.org/10.5923/j.ajis.20120207.03
Zuo ZH, Xie YM. A simple and compact Python code for complex 3D topology optimization. Adv Eng Softw. 2015; 85:1-11. https://doi.org/10.1016/j.advengsoft.2015.02.006
Agarwal D, Robinson TT, Armstrong CG. A CAD based framework for optimizing performance while ensuring assembly fit. In: Wang S, Price M, Lim M, Jin Y, Luo Y, Chen R, editors. Recent advances in intelligent manufacturing. Singapore: Springer; 2018; 1-12. https://doi.org/10.1007/978-981-13-2396-6_7
Mukundakrishnan B, Rajmohan N, Rajnarayan DG, Fugal S. A script-based CAD system for aerodynamic design. In: AIAA Aviation 2019 Forum. 2019. https://doi.org/10.2514/6.2019-3069
Mathur A, Pirron M, Zufferey D. Interactive programming for parametric CAD. Comput Graph Forum. 2020; 00(00):1-18.