Jaideep Badduri

Bio: Jaideep Badduri is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Rate of convergence. The author has an hindex of 1, co-authored 2 publications receiving 4 citations.

Coupler-Curve synthesis of a planar four-bar mechanism using NSGA-II

Abstract: This paper applies a genetic algorithm-based optimisation procedure, namely, NSGA-II, to the problem of synthesis of a four-bar mechanism. The internal parameters of ${\texttt{\rm NSGA-II}}$ are tuned using a Design of Experiments (DoE) procedure to enhance the quality of the final results. Constraints are handled through a penalty formulation. Further, a scaling function is introduced, which transforms the penalty terms in a manner that leads to faster convergence of the solutions. The theoretical developments are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis. A comparison of the results vis-a-vis existing ones shows that the proposed enhancements of the basic scheme of ${\texttt{\rm NSGA-II}}$ deliver promising improvements in terms of accuracy, and rate of convergence of the solutions.

Optimal Synthesis of Six-Bar Function Generators

Abstract: This paper introduces a new approach for the synthesis of planar six-bar mechanisms via multi-objective numerical optimisation. Using the input-output relationship of such a mechanism, the structural error is formulated. In addition to the structural error, its derivative is also included in the objectives, leading to results which are comparable in accuracy to the exact methods reported in existing literature, and better than the optimisation-based methods. Also, analytical conditions are derived for the identification of the candidate designs which are free of singularities, mobility or branch defects. The formulation and results are demonstrated in the context of a Stephenson-III mechanism, though these can be generalised to the six-bar mechanisms of other topologies.

Optimal kinematic synthesis of 6 bar rack and pinion Ackerman steering linkage

Abstract: In this article, synthesis and simultaneous optimization of a steering mechanism is proposed for enhancing the cornering performance of a Formula Students competition car. A planar six-bar steering...

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

Abstract: Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof’s criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.