Bio: Sujata Patekar is an academic researcher. The author has contributed to research in topic(s): Algorithm design & Multi-swarm optimization. The author has an hindex of 2, co-authored 3 publication(s) receiving 93 citation(s).
Topics: Algorithm design, Multi-swarm optimization, Firefly algorithm, Genetic algorithm scheduling, Quality control and genetic algorithms
••20 Mar 2020
Abstract: Deep drawing is a manufacturing process in which sheet metal is progressively formed into a three-dimensional shape through the mechanical action of a punch forming the metal inside die. The flow of metal is complex mechanism. Pots, pans for cooking, containers, sinks, automobile body parts such as panels and gas tanks are among a few of the items manufactured by deep drawing. Uniform strain distribution in forming results in quality components. The predominant failure modes in sheet metal parts are springback, wrinkling and fracture. Fracture or necking occurs in a drawn part, which is under excessive tensile loading. The prediction and prevention of fracture depends on the design of tooling and selection of process parameters. Firefly algorithm is one of the nature inspired optimisation algorithms and is inspired by firefly's behaviour in nature. The proposed research work presents novel approach to optimise fracture in automotive component-tail cap. The optimisation problem has been defined to optimise fracture within the constraints of radius on die, radius on punch and blank holding force. Fire fly algorithm has been applied to find optimum process parameters. Numerical experimentation has been conducted to validate the results.
TL;DR: The applications of PSO include optimal weight design of a gear train, Simultaneous Optimization of Design and Machining Tolerances, Process Parameter Optimization in Casting, and Machine Scheduling Problem.
Abstract: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behaviour of bird flocking or fish schooling. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). In past several years, PSO has been successfully applied in many research and application areas. This paper reviews the applications of PSO algorithm in mechanical domain. The applications of PSO include optimal weight design of a gear train, Simultaneous Optimization of Design and Machining Tolerances, Process Parameter Optimization in Casting, and Machine Scheduling Problem. The paper also describes the improved version of PSO algorithm namely: Hybrid PSO, Multiobjective PSO, Adaptive PSO and Discrete PSO.
TL;DR: Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
Abstract: Genetic Algorithm is optimization method based on the mechanics of natural genetics and natural selection. Genetic Algorithm mimics the principle of natural genetics and natural selection to constitute search and optimization procedures.GA is used for scheduling to find the near to optimum solution in short time. In a genetic algorithm representation is done with variable length of sub-chromosome.GA is developed to generate the optimal order scheduling solution. GA is used as tool in different processes to optimize the process parameters. This paper reviews the genetic algorithms that are designed for solving multiple problems in applications of material science and manufacturing in field of mechanical engineering. Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
Abstract: The design parameters of heat pumps are related to each other nonlinearly or in a complicated manner; therefore, it is difficult to determine the optimal combination of design parameters, such as superheat, subcooling, and refrigerant type, analytically. To address this limitation, three representative heuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), are applied to optimize a heat pump under the given process conditions. Heuristic algorithms are driven based on randomness; thus, the consistency of the calculation results and computational time represent the decision criteria for the appropriate optimizer. The GA is unsuitable as a heat pump optimizer because it requires an excessive number of iterations. In contrast, PSO and SA have a similar capability of consistency and calculation time with a rational number of iterations. In conclusion, PSO exhibits a slightly better consistency and use of computational resources; therefore, PSO is selected as the heat pump design optimization algorithm in this study. The novelty of this work lies in that the related design parameters of the heat pump are simultaneously globally optimized with minimal physical background, and the heuristic algorithm that is most applicable to heat pump design optimization is determined.
Abstract: In the five-axis CNC machining process, the dynamic tracking error due to servo dynamic performance deficiency is a main cause of processing inaccuracy during precise high-speed machining. The rotation tool center point (RTCP) test is commonly used to measure the dynamic performance of five-axis machine tools. The key to the capability of the RTCP test is axis motion planning in the test process. However, the axis motion plans for RTCP tests are usually based on simple motion instruction or engineering experience; the mechanism of the discrepancy between different axis motion plans is unclear. In this study, the axis motion planning process for RTCP dynamic performance tests is analyzed, and a novel axis motion planning method is proposed. The axis motion planning process is directly connected to the mechanism of dynamic tracking error; error observability is used as the index to guide RTCP axis motion planning. A modified genetic algorithm is used to select the sensitive rotary axis position and velocity combos; cubic spline interpolation is used to plan the axis motions based on the sensitive position and velocity combos.
TL;DR: A weighted impulse (WI) index is proposed to evaluate the performance of the adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and an intelligent optimization algorithm is used to obtain the optimal WI.
Abstract: Stochastic resonance (SR) provides the enhancement of fault characteristic signals with the assistance of noise. The performance of adaptive SR must be evaluated using an appropriate index to automatically enhance various characteristic signals. This paper proposes a weighted impulse (WI) index to evaluate the performance of the adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and uses an intelligent optimization algorithm to obtain the optimal WI. The comparison results in the simulation experiment revealed that the proposed WI-based AGSTSR method presented the fault characteristics more clearly than the adaptive SR method based on the impulse index or weighted kurtosis index. Moreover, the proposed method offered the best anti-noise performance in the simulation experiment. Finally, two experimental case studies validated that the proposed method can adaptively realize early fault diagnosis of rotating machinery through the analysis of weak fault characteristics.
18 Oct 2021
Abstract: This article describes the optimization of processing parameters for the surface roughness of AISI316 austenitic stainless steel. While experimenting, parameters in the process like feed rate (fd), speed (vc), and depth of cut (DoC) were used to study the outcome on the surface roughness (Ra) of the workpiece. The experiment was carried out using the design of experiments (DOE) on a computer numerical control (CNC) lathe. The surface roughness is tested for three conditions i.e. Dry, Wet, and cryogenic conditions after the turning process. Samples are step turned on CNC Lathe for all three conditions with a set of experiments designed. The response surface methodology is implemented, and mathematical models are built for all three conditions. The nature-inspired algorithm is the best way to get the optimal value. For the discussed problem in the paper, nature-inspired techniques are used for obtaining the optimum parameter values to get minimum surface roughness for all set conditions. The Grasshopper optimization algorithm (GOA) is the technique that is the most effective method for real-life applications. In this research, GOA is used to get optimum values for the surface roughness (Ra) at Dry, Wet and cryogenic conditions. Finally, results are compared, and it's observed that the values obtained from GOA are minimum in surface roughness value.
TL;DR: This comprehensive review presents state-of-the-art theory and application of the most widely used CI techniques such as GA, PSO, SA, DE, HTS, CRO, MOGA, and NSGA II in the optimization of various refrigeration systems.
Abstract: Refrigeration systems currently utilize 17% of total electric energy, and this consumption is expected to increase by more than 30% by 2050. Moreover, these systems significantly contribute to global warming (10%) and environmental degradation. Optimization of these systems can overcome these critical issues, increase thermal efficiency, and decrease the total cost. Refrigeration system optimization problems are complex, multi-modal, non-linear, and time-consuming. Both classic and non-classic (computational intelligence (CI)) methods are successfully applied to overcome these challenges. This comprehensive review presents state-of-the-art theory and application of the most widely used CI techniques such as GA, PSO, SA, DE, HTS, CRO, MOGA, and NSGA II in the optimization of various refrigeration systems. The properties of these algorithms, their computational efficiency, robustness, and applications are highlighted most. Additionally, the authors discuss several surrogate modelling techniques and their applications in refrigeration systems, including ANN, RSM, and regression analysis. According to trend analysis, the major cost function to optimize is the COP, followed by total cost, exergetic efficiency, energy consumption, and cooling capacity. Subsequently, the application of CI approaches has increased dramatically in the optimization of refrigeration systems, where GA and its variants are typically used, and authors are more interested in multi-objective optimization.