Optimization design for die-sinking EDM process parameters employing effective intelligent method

Tran, Van Tron; Le, Minh Huy; Vo, Minh Thai; Le, Quoc Trung; Hoang, Van Huong; Tran, Ngoc-Thien; Nguyen, Van-Thuc; Nguyen, Thi-Anh-Tuyet; Nguyen, Hoai Nam; Nguyen, Van Thanh Tien; Nguyen, Thanh Tan

doi:10.1080/23311916.2023.2264060

Cited by 17 publications

(7 citation statements)

References 58 publications

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“…It assesses the efficiency of this application and its potential impact on data security. The study [27] focuses on optimization design for diesinking Electrical Discharge Machining (EDM) process parameters. The paper employs effective intelligent methods to optimize the parameters, aiming to enhance the efficiency of the EDM process.…”

Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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The drone sector is witnessing a surge in demand for advanced models tailored to address critical applications such as disaster management and intelligent warehouse deliveries. Employing simulation-based experiments with virtual drone navigation is considered a best practice before deploying physical models. Nonetheless, the current state-of-the-art virtual drone navigation system lacks accuracy and introduces notable increments in simulation time. In order to mitigate these issues, this paper introduces a deep reinforcement learning-based drone agent, designed to autonomously navigate within a constrained virtual environment. The proposed drone agent utilizes realistic drone physics in order to ensure flight within the virtual environment. The work uniquely combines & optimizes both control algorithms and physical dynamics, making the model more robust and versatile than others. The integration of curiosity-driven learning with physics-based modeling potentially increases the model’s readiness for real-world application, compared to theoretical approaches. The extensive simulation results validate the remarkable speed and accuracy of the proposed scheme compared to baseline works. The trained agent exhibits strength and versatility, enabling it to deal with the numerous targets and obstacles encountered in human environments.

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Section: Existing Workmentioning

confidence: 99%

A proximal policy optimization with curiosity algorithm for virtual drone navigation

Das,

Khan,

Paul

2024

Eng. Res. Express

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“…Optimization of parameters is crucial in mechanical design [28,29]. The number of wheel legs largely determines the working performance of the walking wheel, and its reasonable selection requires comprehensive consideration of various factors.…”

Section: Design Of the Walking Wheelmentioning

confidence: 99%

A Bionic Walking Wheel for Enhanced Trafficability in Paddy Fields with Muddy Soil

Chen,

Xu,

Song

et al. 2024

Biomimetics

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To improve wheel trafficability in soft and muddy soils such as paddy fields, a bionic walking wheel is designed based on the structural morphology and movement mode of the feet of waders living in marshes and mudflats, similar to the muddy soil of paddy fields. The bionic walking wheel adopts the arrangement of double-row wheel legs and staggered arrays to imitate the walking posture of waders. The two legs move alternately, cooperate with each other, and improve the smoothness of movement. The cam inside the bionic walking wheel is used to control the movement mode of the feet. The flippers open before touching the ground to increase the contact area and reduce sinking, and the toes bend and grip the ground while touching the ground to increase traction. Multi-rigid-body dynamics software (Adams View 2020) is used to simulate the movement of the wheel during the wading process, and the movement coordination and interference between the wheel legs are analyzed. The simulation results show that there is no interference between the parts and that the movement smoothness is good. The interaction between the bionic walking wheel and muddy soil was analyzed via coupled EDEM–ADAMS simulation, and the simulation analysis and experiments were conducted and compared with those for a common paddy wheel. The results showed that the bionic walking wheel designed in this paper improved the drawbar pull by 113.56% compared with that of a common paddy wheel and had better anti-sinking performance. By analyzing the effect of toe grip on traction, it was found that the soil under the feet can be disturbed to provide greater traction when the toe is bent downward. This study provides a reference for improving the trafficability of walking mechanisms in soft and muddy soils, such as paddy fields.

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“…Notably, limited attention has been directed towards the combined utilization of the Grey Wolf Optimization Algorithm and Ensemble Learning techniques, including CatBoost, LightGBM, AdaBoost, and XGBoost, in the context of this specific application. The optimization is one of the popular methods used in different fields of study [80,81]. Consequently, there exists a discernible research gap that warrants exploration and investigation into the potential synergies and performance enhancements that may arise from the integration of the Grey Wolf Optimization Algorithm with the aforementioned Ensemble Learning methods.…”

Section: Introductionmentioning

confidence: 99%

Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

Wang,

Zhang,

et al. 2024

Buildings

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Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the Grey Wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy through the creation of four hybrid ensemble learning models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, and GWO-CatBoost. A comparative analysis was conducted between the results obtained from these hybrid models and their conventional counterparts. The evaluation of these models is based on five key indices: R2, RMSE, VAF, MAE, and bias, addressing an objective assessment of the predictive models’ performance and capabilities. The outcomes reveal that GWO-XGBoost, exhibiting R2 values of (0.971 and 0.978) for the train and test stages, respectively, emerges as the best predictive model for estimating the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) compared to other models. Consequently, the proposed GWO-XGBoost algorithm proves to be an efficient tool for anticipating CSFrRNSC.

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Optimization design for die-sinking EDM process parameters employing effective intelligent method

Cited by 17 publications

References 58 publications

A proximal policy optimization with curiosity algorithm for virtual drone navigation

A proximal policy optimization with curiosity algorithm for virtual drone navigation

A Bionic Walking Wheel for Enhanced Trafficability in Paddy Fields with Muddy Soil

Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

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