# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 1 Introduction  
Artificial intelligence (AI) is one of the most discussed and pursued topics of today's research. In particular, deep learning (DL)--a subfield of AI, see Figure 1--is showing impressive performance that is, in certain cases, superior to human beings. For instance, _AlphaGo_ defeated Go game masters, and _Google Vision_ posed higher accuracy than humans in image recognition [1; 2]. Consequently, the potential of DL has been highly recognized by both academia and industry and is now being applied to a number of domains such as personal entertainment, healthcare, and smart manufacturing. _Industry 4.0, Advanced Manufacturing Partnership_, and _Made in China 2025_ are political digitalization programs that intend to push modern industrial processes towards fully automated and self-adaptive levels. For this, the physical world of materials, machines, and products is connected to the virtual world of computers through networked sensors and actuators within the production processes using the concepts of the Internet of Things (IoT) and cyber-physical systems (CPS). Consequently, an unprecedented volume of data is continuously generated and calls for effective processing that enables fast response time, intelligent decision-making, and global optimization. Hence, data-driven AI techniques, such as deep learning, have great potential and have been applied to many smart manufacturing applications, as well as other industrial domains, such as mining and transportation. In the report provided by _Deloitte_[3], it was estimated that the market size of AI adoptionin manufacturing would grow to 2057 million dollars in China in the year 2025. They have also found that 83% of their surveyed companies think AI will make or has made an impact. Similarly, according to _Microsoft_'s report [4], 81% of the 86 companies they surveyed have seen AI become more important in 2020. The main application categories of AI in smart manufacturing include predictive maintenance, quality assurance, yield enhancement, collaborative robots, as well as supply chain and warehouse management. Early _predictive maintenance_ can shorten the downtime and reduce economical costs. AI enables predictive maintenance to avoid future faults and helps to automate fault diagnosis for manufacturing equipment [5; 6; 7]. _Quality assurance_ aims at automated product quality checks. In particular, image-based techniques (visual inspection) are widely applied [8; 9; 10]. At present, _yield enhancement_ AI applications are concentrated on the integrated circuit manufacturing industry due to its high digitalization level, complicated manufacturing technologies, and expensive yield loss costs. The main ideas for yield enhancement are to identify yield losses and remove the causes behind it [11; 12; 13]. _Industrial robots_ are key components for improving efficiency in manufacturing and are supposed to be adaptive to environmental changes instead of having hard-coded exact poses for each movement. This can be achieved through vision-guided motion control [14]. Moreover, robots are expected to work collaboratively with humans and other robots in a shared environment while maintaining human safety [15]. Applications for _supply chain management_ can be categorized into the domains of planning, supplier selection, logistics, and warehouse management. AI can forecast inventory and demand to improve planning with proactive and agile decision-making [16]. Similarly, AI can help to select suppliers by predicting their reliability [17]. Logistics places emphasis on route optimization for fast and economical transportation [18]. AI for _warehouse management_ is more general and aims to enable fully automated warehouse operation, including inventory forecasting, warehouse robots, and logistic optimization [19]. However, developers still encounter fundamental obstacles when building deep learning solutions for smart manufacturing applications. Deep learning is a data-hungry technique, which requires sufficient pre-collected data.