Abstract
The manufacturing of complex parts, such as aircraft structural parts and aero-engine casing parts, has always been one of the focuses in the manufacturing field. The machining process involves a variety of hard problems (e.g. tool wear prediction, smart process planning), which require assumptions, simplifications and approximations during the mechanism-based modelling. For these problems, supervised machine learning methods have achieved good results by fitting input–output relations from plenty of labelled data. However, the data acquisition is difficult, time consuming, and of high cost, thus the amount of data in a single enterprise is often limited. To address this issue, this research aims to realise the equivalent manufacturing data sharing based on federated learning (FL), which is a new machine learning framework to use the scattered data while protecting the data privacy. An enterprise-oriented framework is first proposed to find FL participants with similar data resources. Then, the machining parameter planning task for aircraft structural parts is taken as an example to propose an FL model, which mines the knowledge and rules in the historical processing files from multiple enterprises. In addition, to solve the data difference among enterprises, domain adaptation method in transfer learning is used to obtain domain-invariant features. In the case study, a prototype platform is developed, and to validate the performance of the proposed model, a data set is built based on the historical processing files from three aircraft manufacturing enterprises. The proposed model achieves the best performance compared with the model trained only with the data from a single enterprise, and the model without domain adaptation.
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Abbreviations
- \({\mathbf{G}}\) :
-
Graph
- \({\mathbf{V}}\) :
-
Vertex set
- \(v_{i} ,v_{j}\) :
-
Vertex \(i\), vertex \(j\)
- \(n\) :
-
Total number of vertexes
- \({\mathbf{E}}\) :
-
Edge set
- \({\mathbf{A}}\) :
-
Adjacency matrix
- \({\mathbf{H}}\) :
-
Attribute matrix
- \({\varvec{h}}_{i}\) :
-
Attribute vector of vertex \(v_{i}\)
- \(r\) :
-
Dimension of the attribute vector
- \({\mathbf{C}}\) :
-
Relation matrix
- \(m\) :
-
Total number of machining operations
- \({\mathbf{W}}\) :
-
Learnable matrix used for linear transformation
- \({\varvec{a}}\) :
-
Learnable vector in attention coefficients calculation
- \(a_{ij}\) :
-
Attention coefficients
- \({\mathbf{H^{\prime}}}\) :
-
Output attribute matrix of graph convolution layers
- \({\varvec{h}}_{i}^{\user2{^{\prime}}}\) :
-
Attribute vector in \({\mathbf{H^{\prime}}}\)
- \({\varvec{o}}_{i}\) :
-
Machining operation
- \({\varvec{o}}_{i}^{area}\) :
-
Machining area of \({\varvec{o}}_{i}\)
- \({\varvec{G}}_{i}^{sub}\) :
-
Subgraph corresponding to \({\varvec{o}}_{i}^{area}\)
- \({\varvec{h}}_{cond}\) :
-
Representing vector of process information
- \({{\varvec{\uptheta}}}\) :
-
Training parameters of FL model
- \(F\) :
-
Feature extractor
- \(C\) :
-
Label classifier
- \(D\) :
-
Domain discriminator
- \(\theta_{f}\) :
-
Model parameters of feature extractor
- \(\theta_{c}\) :
-
Model parameters of label classifier
- \(\theta_{d}\) :
-
Model parameters of domain discriminator
- \(N\) :
-
Number of training samples
- \(L_{c}\) :
-
The loss of label classifier
- \(L_{d}\) :
-
The loss of domain discriminator
- \(K\) :
-
Number of FL clients
- \(n_{k}\) :
-
Number of samples that \(k\)-th client used
- \(T\) :
-
Total number of samples in an FL round
- \(x,y\) :
-
Private tensors
- \({\text{S}}_{0} ,{\text{S}}_{1}\) :
-
Two servers that share the secrete
- \({\text{S}}_{2}\) :
-
The server that generate crypto
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Funding was provided by National Natural Science Foundation of China (Grant Nos. 51925505, U21B2081).
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Deng, T., Li, Y., Liu, X. et al. Federated learning-based collaborative manufacturing for complex parts. J Intell Manuf 34, 3025–3038 (2023). https://doi.org/10.1007/s10845-022-01968-3
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DOI: https://doi.org/10.1007/s10845-022-01968-3