# Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems
###### Abstract  
The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance. manufacturing execution system; quality prediction; discrete manufacturing; multivariate long- and short-term memory  
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supplier parts can significantly impede production, resulting in a divergence in both the supply chain and quality-related factors [3]. Predictive manufacturing systems are emerging as a new paradigm for solving quality problems caused by quality divergences in the production process that propagate downstream in the production chain and cannot be detected. From a technological perspective, the research related to the efficient processing of multivariate heterogeneous data in the past decade has been plagued by problems such as missing data, duplication, the nonlinearity of noisy data structures, and the concatenation of various stages. To improve product quality and ensure production stability, many key quality indicators often play an important role in quality control and forecasting [4; 5; 6]. Many published studies have been conducted on efficiently handling industrial multivariate heterogeneous data and the extraction of quality-related elements, including through the use of data-driven and deep learning methods [7; 8; 9; 10; 11]. For example, a method based on rough set data theory using the particle swarm algorithm and a least squares support vector machine (SVM) [12] to model the manufacturing data of 5M1E (man, material, machine, method, measurement, and environment) in discrete manufacturing helped manufacturing companies to accomplish quality prediction and quality control. A data-driven, real-time learning model was used to deal with nonlinear processes in the face of uncertain perturbations in industrial environments, which, in turn, improved fault detection accuracy [13]. Additionally, a framework was proposed for a data-driven approach [14] to bottleneck discovery in manufacturing execution systems, thus reducing the quality fluctuations brought about by uncertain perturbations. Combining a manufacturing execution system and a computerized maintenance system [15] for the prediction and decision support of core equipment maintenance in the production process can help to improve the digitalization of discrete processes. In [16], the manufacturing critical feature points were classified according to the assembly requirements by means of a comprehensive coordination model, and a distribution method for minimizing integrated errors in the manufacturing process was established and applied in the aircraft assembly process.