Abstract
As the core machining process element in the overall manufacturing process of a part, the machining process route plays an important role in improving final manufacturing quality. In the current CAPP system, the decision-making of the process route still depends on human-computer interaction and essentially depends on human intelligence. In the past decade, deep learning technology architecture has been gradually improved, which provides a new enabling technology for intelligent process planning. Recently, some researchers have applied deep learning to process route decision-making. However, due to the challenges of data representation and deep learning network construction, this promising solution is still at infancy. To address the two challenges, this paper presents a novel process route generation approach based on deep learning. First, we propose a fourth-order tensor model to represent the geometry and technological requirements of a part. And the relation matrix is constructed to represent the relationships among machining features. The process route is represented as a sequential set of one-hot vectors. Then, we construct an encoder-decoder neural architecture to automatically generate the machining process route for the part. The 3D convolution neuron network-based encoder converts the geometry, technological requirements, and the information of the relationships among machining features into a higher layer of vector representation, and the long short-term memory network-based decoder maps this representation to the process route. The whole neural architecture including the encoder and decoder is jointly trained to maximize the conditional probability of the target process route given the training part. Finally, the paper takes slot cavity parts as examples to verify the feasibility and effectiveness of the proposed approach.
Similar content being viewed by others
Availability of data and material
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
References
Zhou J, Li PG, Zhou YH, Wang BC, Zang JY, Meng L (2018) Toward new-generation intelligent manufacturing. Engineering 4(1):11–20
Al-Wswasi M, Ivanov A, Makatsoris H (2018) A survey on smart automated computer-aided process planning (ACAPP) techniques. Int J Adv Manuf Technol 97(1-4):809–832
Li XL, Zhang SS, Huang R, Huang B, Xu CH, Zhang YJ (2018) A survey of knowledge representation methods and applications in machining process planning. Int J Adv Manuf Technol 98(9-12):3041–3059
Huang B, Zhang SS, Huang R, Li XL, Zhang YJ, Liang JC (2019) A complex network based NC process skeleton extraction approach. Comput Ind 113:103142
Zhang C, Zhou GH, Hu JS, Li J (2020) Deep learning-enabled intelligent process planning for digital twin manufacturing cell. Knowl-Based Syst 191:105247
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature 521(7553):436–444
Mokhtar A, Xu X, Lazcanotegui I (2009) Dealing with feature interactions for prismatic parts in step-nc. J Intell Manuf 20(4):431–445
Knapp GM, Wang HP (1992) Acquiring, storing and utilizing process planning knowledge using neural networks. J Intell Manuf 3(5):333–344
Devireddy CR, Ghosh K (1999) Feature-based modelling and neural networks-based capp for integrated manufacturing. Int J Comput Integr Manuf 12(1):61–74
Deb S, Ghosh K, Paul S (2006) A neural network based methodology for machining operations selection in computer-aided process planning for rotationally symmetrical parts. J Intell Manuf 17(5):557–569
Amaitik SM, Kili SE (2007) An intelligent process planning system for prismatic parts using step features. Int J Adv Manuf Technol 31(9-10):978–993
Zhou DC, Dai X (2015) Combining granular computing and rbf neural network for process planning of part features. Int J Adv Manuf Technol 81(9-12):1447–1462
Leng JW, Chen QX, Mao N, Jiang PY (2017) Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl-Based Syst 143:295–306
Wu ZR, Song SR, Khosla A, Yu F, Zhang LG, Tang XO, Xiao JX (2015) 3D shapenets: a deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.1912-1920)
Garcia-Garcia A, Gomez-Donoso F, Garcia-Rodriguez J, Orts-Escolano S, Cazorla M, Azorin-Lopez J (2016) Pointnet: a 3d convolutional neural network for real-time object class recognition. In 2016 International Joint Conference on Neural Networks (pp. 1578-1584). IEEE
Ghadai S, Balu A, Sarkar S, Krishnamurthy A (2018) Learning localized features in 3d cad models for manufacturability analysis of drilled holes. Computer Aided Geometric Design 62(MAY):263–275
Zhang ZB, Jaiswal P, Rai R (2018) Featurenet: machining feature recognition based on 3d convolution neural network. Comput Aided Des 101:12–22
Ning F, Shi Y, Cai M, Xu W, Zhang X (2020) Manufacturing cost estimation based on the machining process and deep-learning method. J Manuf Syst 56:11–22
Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211
Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. In Advances in neural information processing systems (pp. 473-479)
Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). IEEE
Sutskever I, Vinyals O, Le Q V (2014) Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112)
Park C, Kim D, Yu H (2019) An encoder–decoder switch network for purchase prediction. Knowl-Based Syst 185:104932
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Polosukhin I (2017) Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008)
Zhao ZW, Li YG, Liu CQ, Gao J (2020) On-line part deformation prediction based on deep learning. J Intell Manuf 31(3):561–574
Zhou JT, Zhao X, Gao J (2019) Tool remaining useful life prediction method based on lstm under variable working conditions. Int J Adv Manuf Technol 104(11):1–12
Li Z, Li JY, Wang Y, Wang KS (2019) A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. Int J Adv Manuf Technol 103(1-4):499–510
Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3156-3164)
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, et al (2015) Show, attend and tell: neural image caption generation with visual attention. In International conference on machine learning (pp. 2048-2057)
Gezawa AS, Zhang Y, Wang Q, Yunqi L (2020) A review on deep learning approaches for 3D data representations in retrieval and classifications. IEEE Access 8:57566–57593
Kolda TG, Bader Brett W (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500
Huang R, Zhang SS, Bai XL, Xu CH (2014) Multi-level structuralized model-based definition model based on machining features for manufacturing reuse of mechanical parts. Int J Adv Manuf Technol 75(5-8):1035–1048
Huang R, Zhang SS, Bai XL (2013) Manufacturability driven interacting machining feature recognition algorithms for 3D CAD models. Journal of Comput-Aided Design & Computer Graphs 25(7):1089–1098 [in Chinese]
Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. ieee. Computational intelligenCe magazine 13(3):55–75
Hegde V, Zadeh R (2016) Fusionnet: 3d object classification using multiple data representations. arXiv preprint arXiv:1607.05695
Molchanov D, Ashukha A, Vetrov D (2017) Variational dropout sparsifies deep neural networks. In Proceedings of the 34 th International Conference on Machine Learning (ICML), 2017
Santurkar S, Tsipras D, Ilyas A, Madry A (2018) How does batch normalization help optimization. In Advances in Neural Information Processing Systems (pp. 2483-2493)
Funding
This work was financially supported by financial support from the National Science Foundation of China (No. 51875474, 52075148) and the Equipment Pre-Research Domain Foundation of China (61409230102).
Author information
Authors and Affiliations
Contributions
Yajun Zhang conceived of research methods, performed the experiments, and wrote the manuscript. All authors contributed to collect and analyze the data, also revisions.
Corresponding author
Ethics declarations
Ethics approval
Not applicable
Consent to participate
Not applicable
Consent for publication
Not applicable
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, Y., Zhang, S., Huang, R. et al. A deep learning-based approach for machining process route generation. Int J Adv Manuf Technol 115, 3493–3511 (2021). https://doi.org/10.1007/s00170-021-07412-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-021-07412-9