# Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems
###### Abstract
However, these methods do not take into account the complexity of discrete manufacturing, which mainly relates to univariate prediction and control; therefore, the large volume of data in the MES system needs to extract data associated with quality characteristics for analysis, and in previous studies, no link between quality and raw data has been established. The module design of MES is divided into two main parts [17]: the basic software module part and the application software module part. So far, MES has 19 sub-modules, including 10 basic software modules and 9 application software modules. The Config module is a basic parameter-setting module. It is used to set user rights management and the system configuration for production-related data of various products. The Config module is used to configure the basic data of the system and establish the business model of an enterprise. In addition, the Production Management Module (PM), Production Site Monitoring Module (PMO), Quality Control Module (QC), and Repair Station Module (QS) also exist [18]. The QC module follows ISO9001 specifications [19] and fully inspects the finished products that come off the assembly station through testing and repair processes and releases the qualified products to the next station for production and, conversely, sends the unqualified products to the repair station. The QC module is very important for recording data regarding product quality inspection results and conducting statistical analysis. In recent years, there have been many new advancements in the field of deep learning that have been integrated into manufacturing systems, making it a promising area for extending the predictive capabilities of QC modules in order to optimize quality control and process efficiency. The authors of ref. [20] prevented the propagation of undetected defects down the production line and used the Extreme Gradient Boosting (XBoost) method integrated in the manufacturing system to improve the recall rate of defective automotive products. In ref. [21], based on deep learning, an intelligent monitoring system was used to select the most relevant features with respect to quality to predict the products' passing rates to improve a factory's quality. The application of neural networks offers innovative solutions for nonlinear prediction in manufacturing systems. In ref. [22], deep learning was applied for the quality management and quality extraction of aluminum castings. The approach used was a feature pyramid network, which was intended to enhance accuracy. Smart manufacturing device data were analyzed in [23], and the accuracy of device identification was improved by finding the correlation between the data through long- and short-term memory (LSTM) in a cloud server. A soft measurement method using supervised LSTM networks was proposed in [24] for modern low measurement accuracy and latency problems for application in a debutanizer column and penicillin fermentation process, using input and quality variables to learn shared dynamic hidden feature states. The 2 DConvLSTMAE method for metal can increase manufacturing speed; in [25], it was used to predict the performance of multiple machines simultaneously to reduce training time and improve prediction performance. The scheduling model of the workshop-cycle-scheduling problem using the Lagrangian Relaxation Recurrent Neural Network (LRNN) presented in [26] reduced the complexity of the aforementioned problem and makes it easier to use in real production. The contemporary challenge for discrete manufacturing quality prediction is the real-time monitoring of product quality for enterprises. To address this challenge, the use of high-performance, dynamic, and synchronous recurrent neural networks was proposed. This paper stems from the findings of an empirical survey conducted on the generative process of multi-dimensional inspection data extraction quality features within manufacturing systems.