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Energy-efficient mu...
Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning
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- Lu, Fengyi (författare)
- Xi An Jiao Tong Univ, Peoples R China
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- Zhou, Guanghui (författare)
- Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China
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- Zhang, Chao (författare)
- Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China
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- Liu, Yang (författare)
- Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten,Univ Oulu, Finland
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- Chang, Fengtian (författare)
- Xi An Jiao Tong Univ, Peoples R China
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- Xiao, Zhongdong (författare)
- Xi An Jiao Tong Univ, Peoples R China
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(creator_code:org_t)
- PERGAMON-ELSEVIER SCIENCE LTD, 2023
- 2023
- Engelska.
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0736-5845 .- 1879-2537. ; 81
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric opti-misation, significantly contributing to sustainable manufacturing.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Energy efficiency; Parametric optimisation; Workpiece deformation; Deep reinforcement learning; Sustainable manufacturing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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