A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing

Abstract

In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. The implementation of Internet of Things (IoT) makes it possible to take real-time production state as an important factor affecting PP. In the IoT-enabled workshop, a two-stage transfer learning-based prediction method using both historical production data and real-time state data is proposed to solve the problem of low-prediction accuracy and poor generalization performance caused by insufficient data of target order. The deep autoencoder (DAE) model with transfer learning is designed to extract the generalized features of target order in the first stage, which uses bootstrap sampling to avoid over fitting. The deep belief network (DBN) model with transfer learning is constructed to fit the nonlinear relation for PP prediction in the second stage. A real case from an IoT enabled machining workshop is taken to validate the performance of the proposed method over the other methods such as DBN, deep neural network.