Characterization and Parametric Optimization of Micro-hole Surfaces in Micro-EDM Drilling on Inconel 718 Superalloy Using Genetic Algorithm

Dilip, Deepak G.; Panda, Satyananda; Mathew, Jose

doi:10.1007/s13369-019-04325-4

Cited by 36 publications

(8 citation statements)

References 47 publications

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“…Hence, the suggested technology could increase the stability and efficiency of machining while also being appropriate for the parameter adjustment of micro-EDM. Dilip et al [111] carried out the multi-objective optimization of process parameters on Inconel 718 using weighted objective summation and GA techniques to enhance the processing quality of drilling cooling holes for turbine blades. According to the experimental data, the inner wall's surface roughness could be reduced to 13,587 µm under the ideal cutting circumstances.…”

Section: Parameters Optimizationmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

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Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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Section: Parameters Optimizationmentioning

confidence: 99%

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen

et al. 2023

Metals

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“…e genetic algorithm can solve multiobjective optimization problems combining the strong global search ability and implicit parallel search characteristics [42,43]. It is a practical, efficient, and robust optimization technology, which is widely used in various fields [44][45][46]. For example, Marín Moreno et al [47] used genetic algorithms to solve the reality of the Colombian transportation system (2 warehouses and 719 services) in warehouse cargo transportation, and the fleet size has been reduced.…”

Section: Genetic Algorithm Applicationmentioning

confidence: 99%

Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data

Zhou

Guan

et al. 2021

Journal of Advanced Transportation

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Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.

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“…For instance, the GA has been successfully employed to determine parameter values in the Deep Deterministic Policy Gradient, resulting in accelerated learning for the agent [28]. Additionally, the GA has been utilized for the multiobjective optimization of process parameters, employing a weighted objective sum method [29]. Moreover, the GA has been applied to SVM parameter optimization, effectively addressing grid search problems [30].…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

et al. 2023

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One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address the issue of subject-specific features, a suitable approach is to employ projection dictionary learning, which enables the identification of emotion-relevant features across different subjects. To accomplish the objective of pattern representation and discrimination for subject-independent EEG emotion recognition, we utilized the fast and efficient projection dictionary pair learning (PDPL) technique. PDPL involves the joint use of a synthesis dictionary and an analysis dictionary to enhance the representation of features. Additionally, to optimize the parameters of PDPL, which depend on experience, we applied the genetic algorithm (GA) to obtain the optimal solution for the model. We validated the effectiveness of our algorithm using leave-one-subject-out cross validation on three EEG emotion databases: SEED, MPED, and GAMEEMO. Our approach outperformed traditional machine learning methods, achieving an average accuracy of 69.89% on the SEED database, 24.11% on the MPED database, 64.34% for the two-class GAMEEMO, and 49.01% for the four-class GAMEEMO. These results highlight the potential of subject-independent EEG emotion recognition algorithms in the development of intelligent systems capable of recognizing and responding to human emotions in real-world scenarios.

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Characterization and Parametric Optimization of Micro-hole Surfaces in Micro-EDM Drilling on Inconel 718 Superalloy Using Genetic Algorithm

Cited by 36 publications

References 47 publications

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

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