Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning

Lu, Fengyi; Zhou, Guanghui; Zhang, Chao; Liu, Yang; Chang, Fengtian; Xiao, Zhongdong

doi:10.1016/j.rcim.2022.102488

Cited by 19 publications

(2 citation statements)

References 43 publications

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“…In addition, the method guarantees that the average processing cost after optimization is close to the minimum processing cost obtained by the conventional optimization algorithm. Lu et al (2023) introduced a new multi-pass parametric optimization approach based on DRL. This method allows parameters to be modified in such a way as to maximize energy efficiency, taking into account variations in deformation limits at each machining pass.…”

Section: Literature Reviewmentioning

confidence: 99%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

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This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost ( Ctot) and energy consumption ( Etot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for Etot and Ctot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy.

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Section: Literature Reviewmentioning

confidence: 99%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

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show abstract

“…A small learning rate may result in protracted training progress, whereas a large one could trigger oscillations or even algorithm divergence, compromising training performance [17]. Integrating RL with neural networks has provided some solutions for this issue [18,19]. The robot interacts with the environment and learns through continuous trial and error to obtain a reward function to judge whether the skill is good or bad, and eventually learns the skill.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots

Mou,

Xue,

Liu

et al. 2023

Biomimetics

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Achieving omnidirectional walking for bipedal robots is considered one of the most challenging tasks in robotics technology. Reinforcement learning (RL) methods have proved effective in bipedal walking tasks. However, most existing methods use state machines to switch between multiple policies and achieve omnidirectional gait, which results in shaking during the policy switching process for bipedal robots. To achieve a seamless transition between omnidirectional gait and transient motion for full-size bipedal robots, we propose a novel multi-agent RL method. Firstly, a multi-agent RL algorithm based on the actor–critic framework is designed, and policy entropy is introduced to improve exploration efficiency. By learning agents with parallel initial state distributions, we minimize reliance on gait planner effectiveness in the Robot Operating System (ROS). Additionally, we design a novel heterogeneous policy experience replay mechanism based on Euclidean distance. Secondly, considering the periodicity of bipedal robot walking, we develop a new periodic gait function. Including periodic objectives in the policy can accelerate the convergence speed of training periodic gait functions. Finally, to enhance the robustness of the policy, we construct a novel curriculum learning method by discretizing Gaussian distribution and incorporate it into the robot’s training task. Our method is validated in a simulation environment, and the results show that our method can achieve multiple gaits through a policy network and achieve smooth transitions between different gaits.

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Integration of ANN and ANFIS Models to Predict Quality, Cost and Energy During Machining Alloy 2017A

Bousnina,

Hamza,

Yahia

2024

Lecture Notes in Mechanical Engineering

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Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning

Cited by 19 publications

References 43 publications

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

A Multi-Agent Reinforcement Learning Method for Omnidirectional Walking of Bipedal Robots

Integration of ANN and ANFIS Models to Predict Quality, Cost and Energy During Machining Alloy 2017A

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