## 5 Maintenance
### Manufacturing Optimization  
This section introduces the key aspects and machine learning methods used for manufacturing optimization. It covers only the processes that are directly part of the production line and the manufacturing processes. The target variables of the manufacturing optimization process include the quality of the product, cost, time, power consumption or other product-specific parameters. Naturally, there are correlations between these optimization factors, yet, these are the most notable categories. The use of machine learning techniques is highly beneficial to pattern recognition, which is the core of the manufacturing optimization processes. With the help of ML techniques, correlations between different types of data or manufacturing domains can be identified and utilized to optimize the manufacturing process. For electricity optimization, ref. [170] uses Q-learning in an automation system to reduce electricity consumption. In ref. [171], a mixed online bipartite matching-based Deep Q-network algorithm is proposed for profit-maximizing smart manufacturing. The paper formulates a joint optimization of the block size, task scheduling, and supply-demand configuration to maximize customers' net profit with the probabilistic delay requirements, which addresses the critical issue of efficiency and latency in the blockchain-based live manufacturing process. Conversely, the study in [172] uses a support vector regression algorithm with an RBF kernel for troubleshooting production data to identify parameters responsible for high energy conversion efficiency variances. Ref. [173] propose LithGAN, an end-to-end lithography modeling framework based on a generative adversarial network (GAN), to map the input mask patterns directly to the output resist patterns. The results show that LithGAN can predict resist patterns with high accuracy while achieving a speed that is orders of magnitude greater than conventional lithography simulation and previous machine learning-based approaches. Besides GAN and Q-learning, there are CNN and RNN applications in the field of ultrasound imaging. Ref. [174] proposes an automatic fetal ultrasound standard plane recognition model in an IIoT environment which learns the spatial and temporal features of the ultrasound video stream by using multi-task learning. The CNN component identifies fetal key anatomical structures, while the RNN component obtains the temporal information between adjacent frames, and it realizes the precise localization and tracking of fetal organs across frames. Particle Swarm Optimization has been utilized in several optimization problems; ref. [175] proposes a PSO-based technique to optimize the hyperparameter settings of the LSTM in an FL environment, while in [176] combined multi-Objective particle swarm optimization (CMOPSO) is proposed for a green manufacturing energy system. Regression problems are also quite typical in the field of manufacturing optimization. Ref. [177] proposes optimizing semiconductor manufacturing processes through machine learning (ML) based on a regression algorithm. Ref. [178] compares the performance of different supervised machine learning methods for the field calibration of low-cost IoT sensors, including Linear Regression and Artificial Neural Network solutions. Furthermore, ref. [179] presents a method of using logistic regression to solve the inverse problem in electrical impedance tomography. However, there are generally fully connected NN solutions in the manufacturing industry. Ref. [180] proposes Finite Element Analysis and a NN model to optimize and create chip package design. Ref. [181] introduces an optimization method for Bipolar-CMOS-DMOS process development based on an Automatic Multi-objective Optimization solution and NN. There are a couple of survey works in the field of manufacturing optimization.