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Informed machine le...
Informed machine learning-based machining parameter planning for aircraft structural parts
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- Deng, T. (författare)
- Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Peoples R China
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- Li, Y. (författare)
- Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Peoples R China
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- Chen, J. (författare)
- Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing, Peoples R China
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- Liu, X. (författare)
- Nanjing Tech Univ, Sch Mech & Power Engn, Nanjing, Peoples R China
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- Wang, Lihui (författare)
- KTH,Hållbara produktionssystem
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(creator_code:org_t)
- 2021-08-31
- 2021
- Engelska.
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Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Nature. - 0268-3768 .- 1433-3015. ; 117:11-12, s. 3563-3575
- Relaterad länk:
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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
- Aircraft structural parts are important and high-value parts used to constitute the frame of the aircraft, and are usually produced by NC machining, where the machining parameters are significant for the machining quality, efficiency, and cost. In the process planning, there are hundreds or even thousands of machining operations that require separate machining parameters, which is a huge task for the existing optimization-based methods that rely on iterative optimizations. Due to the complex structures and high requirements, the existing expert system-based methods require plenty of additional modifications. Recently, with the development of artificial intelligence, data-driven methods are used in machining parameter planning, which mines the knowledge and rules hidden in the historical data. However, the existing data-driven models require a large amount of training data and lack interpretability. To address this issue, this paper proposes an informed machine learning method for machining parameter planning, which introduces multiple prior constraints into the data-driven model. First, the part model is represented as an attribute graph, and the cutting area of each machining operation is correlated to a subgraph, which is used to obtain the vectorized representation of machining operation that covers cutting area and process information. Then, by fitting the mapping between the vectorized machining operation and the machining parameters, the knowledge and rules are learned. Next, to introduce prior constraints into the data-driven model, the constraint loss is designed and incorporated into the original loss function. The proposed method can generate machining parameters for all the machining operations in batch, thereby greatly reducing the human interactions. In the case study, the historical processing files of aircraft structural parts are used to train the proposed model for planning cutting width, cutting depth, spindle speed, and machining feedrate. The results show that the demand for training data is reduced and the prediction accuracy is improved with prior constraints.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
Nyckelord
- Aircraft structural parts
- Data driven
- Informed machine learning
- Machining parameter planning
- Aircraft parts and equipment
- Cutting
- Expert systems
- Iterative methods
- Machine learning
- Training aircraft
- Data-driven methods
- Iterative Optimization
- Machine learning methods
- Machining operations
- Machining parameters
- Optimization based methods
- Prediction accuracy
- Machining centers
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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