Dimensional Synthesis of Four-Bar Mechanisms for the Generation of Rectilinear Motion Through Analytical and Graphical Programming and Optimization of the Straight Trajectory of the Coupling Point
Dimensional Synthesis of Four-Bar Mechanisms for the Generation of Rectilinear Motion Through Analytical and Graphical Programming and Optimization of the Straight Trajectory of the Coupling Point |
||
|
||
© 2024 by IJETT Journal | ||
Volume-72 Issue-2 |
||
Year of Publication : 2024 | ||
Author : BODIE NGUEMIENGO Abdel Axis, NGAYIHI ABBE Claude Valery, KOM Charles Hubert |
||
DOI : 10.14445/22315381/IJETT-V72I2P126 |
How to Cite?
BODIE NGUEMIENGO Abdel Axis, NGAYIHI ABBE Claude Valery, KOM Charles Hubert, "Dimensional Synthesis of Four-Bar Mechanisms for the Generation of Rectilinear Motion Through Analytical and Graphical Programming and Optimization of the Straight Trajectory of the Coupling Point," International Journal of Engineering Trends and Technology, vol. 72, no. 2, pp. 254-266, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I2P126
Abstract
This article deals with the dimensional synthesis of four-bar mechanisms for the generation of rectilinear motion through analytical and graphical programming and optimization of the straight trajectory of the coupling point. The work carried out to date on synthesizing four-bar mechanisms enables the objective function to be optimized, but the trajectory of the coupling point is always curvilinear when the mechanism is in motion. This work presents a method for generating a rectilinear motion of the coupling point by finding the interval in which the crank input angle must vary to obtain reciprocating rectilinear motion. The advantage of this method is that it is precise, given that it considers the global programming of all the blocks that define the various equations and relationships between angles and distances existing in the four-bar mechanism. Convergence is rapid, as we have verified with five precision points, which verify the equation of a straight line, with the coupling point whose coordinates verify the equations of a straight line to obtain rectilinear motion. Analytical and graphical programming allows us to treat the problem by subdividing it into function program blocks and highlighting their interactions. Trajectory optimization is achieved by forcing the coupling point to pass only on a straight line, thus obtaining rectilinear motion instead of a curvilinear curve, as encountered in the literature.
Keywords
Analytical synthesis, Four-bar mechanisms, Graphical programming, Rectilinear motion generation, Straight path optimization.
References
[1] George N. Sandor, “A General Complex Number Method for Plane Kinematic Synthesis with Application,” Columbia University, New York, pp. 1-24, 1959.
[Google Scholar] [Publisher Link]
[2] Arthur G. Erdman, and George N. Sandor, Advanced Mechanism Design: Analysis and Synthesis, Prentice-Hall, Upper Saddle River, NJ, vol. 2, pp. 1-688, 1984.
[Google Scholar] [Publisher Link]
[3] A.P. Morgan, and C.W. Wampler, “Solving a Planar Four-Bar Design Problem Using Continuation,” Journal of Mechanical Design, vol. 112, no. 4, pp. 544-550, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Han Chi-Yeh, “A General Method for the Optimum Design of Mechanisms,” Journal of Mechanisms, vol. 1, no. 3-4, pp. 301-313, 1966.
[CrossRef] [Google Scholar] [Publisher Link]
[5] S. Krishnamurty, and David A. Turcic, “Optimal Synthesis of Mechanisms Using Nonlinear Goal Programming Techniques,” Mechanisms and Machine Theory, vol. 27, no. 5, pp. 599-612, 1992.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jawaharlal Mariappan, and Sundar Krishnamurty, “A Generalized Exact Gradient Method for Mechanism Synthesis,” Mechanism and Machine Theory, vol. 31, no. 4, pp. 413-421, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yu Hongying, Tang Dewei, and Wang Zhixing, “Study on a New Computer Path Synthesis Method of a Four-Bar Linkage,” Mechanism and Machine Theory, vol. 42, no. 4, pp. 383-392, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jacek Buśkiewicz, Roman Starosta, and Tomasz Walczak, “On the Application of the Curve Curvature in Path Synthesis,” Mechanism and Machine Theory, vol. 44, no. 6, pp. 1223-1239, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Radovan R. Bulatovic, and Stevan R. Djordjevic, “Optimal Synthesis of a Four-Bar Linkage by Method of Controlled Deviation,” Theoretical and Applied Mechanics, vol. 31, no. 3-4, pp. 265-280, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wen-Yi Lin, “A GA–DE Hybrid Evolutionary Algorithm for Path Synthesis of Four-Bar Linkage,” Mechanism and Machine Theory, vol. 45, no. 8, pp. 1096-1107, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] S. Sleesongsom, and S. Bureerat, “Four-Bar Linkage Path Generation through Self-Adaptive Population Size Teaching-Learning Based Optimization,” Knowledge-Based Systems, vol. 135, pp. 180-191, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] S.K. Acharyya, and M. Mandal, “Performance of EAs for Four-Bar Linkage Synthesis,” Mechanism and Machine Theory, vol. 44, no. 9, pp. 1784-1794, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[13] J.A. Cabrera, A. Simon, and M. Prado, “Optimal Synthesis of Mechanisms with Genetic Algorithms,” Mechanism and Machine Theory, vol. 37, no. 10, pp. 1165-1177, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Xiong Zhang, Ji Zhou, and Yingyu Ye, “Optimal Mechanism Design Using Interior-Point Methods,” Mechanism and Machine Theory, vol. 35, no. 1, pp. 83-98, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[15] R. Sancibrian et al., “A General Procedure based on Exact Gradient Determination in Dimensional Synthesis of Planar Mechanisms,” Mechanism and Machine Theory, vol. 41, no. 2, pp. 212-229, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Seyedali Mirjalili, and Andrew Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Seyedali Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm,” Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Horacio Martínez-Alfaro, Four-Bar Mechanism Synthesis for n Desired Path Points Using Simulated Annealing, Advances in Metaheuristics for Hard Optimization, Springer Berlin, pp. 23-27, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[19] R.V. Rao, V.J. Savsani, and D.P. Vakharia, “Teaching–Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems,” Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xinsheng Lai, and Mingyi Zhang, “An Efficient Ensemble of GA and PSO for Real Function Optimization,” Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT, Beijing, pp. 651-655, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jong-Won Kim et al., “Numerical Hybrid Taguchi-Random Coordinate Search Algorithm for Path Synthesis,” Mechanism and Machine Theory, vol. 102, pp. 203-216, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Hamed Soleimani, and Govindan Kannan, “A Hybrid Particle Swarm Optimization and Genetic Algorithm for Closed-Loop Supply Chain Network Design in Large-Scale Networks,” Applied Mathematical Modelling, vol. 39, no. 14, pp. 3990-4012, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[23] C.K. Yogesh et al., “A New Hybrid PSO Assisted Biogeography-Based Optimization for Emotion and Stress Recognition from Speech Signal,” Expert Systems with Applications, vol. 69, pp. 149-158, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Arijit De et al., “Composite Particle Algorithm for Sustainable Integrated Dynamic Ship Routing and Scheduling Optimization,” Computers & Industrial Engineering, vol. 96, pp. 201-215, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Arijit De, Anjali Awasthi, and Manoj Kumar Tiwari, “Robust Formulation for Optimizing Sustainable Ship Routing and Scheduling Problem,” IFAC-PapersOnLine, vol. 48, no. 3, pp. 368-373, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Arijit De et al., “Sustainable Maritime Inventory Routing Problem with Time Window Constraints,” Engineering Applications of Artificial Intelligence, vol. 61, pp. 77-95, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Vimal Kumar Pathak et al., “A Modified Algorithm of Particle Swarm Optimization for form Error Evaluation,” Technisches Messen, vol. 84, no. 4, pp. 272-292, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Suraya Masrom et al., “Hybridization of Particle Swarm Optimization with Adaptive Genetic Algorithm Operators,” Proceedings of the 13th IEEE International Conference on Intelligent Systems Design and Applications, Salangor, Malaysia, pp. 153-158, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Dipankar Santra et al., “Hybrid PSO–ACO Algorithm to Solve Economic Load Dispatch Problem with Transmission Loss for Small Scale Power System,” Proceedings of the 2016 International Conference on Intelligent Control Power and Instrumentation, Kolkata, India, pp. 226-230, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Alex Alexandridis, Eva Chondrodima, and Haralambos Sarimveis, “Cooperative Learning for Radial Basis Function Networks Using Particle Swarm Optimization,” Applied Soft Computing, vol. 49, pp. 485-497, 2016.
[CrossRef] [Google Scholar] [Publisher Link]