# Use of Machine Learning to Improve Additive Manufacturing Processes
###### Abstract  
Rapidly developing artificial intelligence (AI) can help machines and devices to perceive, analyze, and even make inferences in a similar way to human reasoning. The aim of this article is to present applications of AI methods, including machine learning (ML), in the design and supervision of processes used in the field of additive manufacturing techniques. This approach will allow specific tasks to be solved as if they were performed by a human expert in the field. The application of AI in the development of additive manufacturing technologies makes it possible to be assisted by the knowledge of experienced operators in the design and supervision of processes acquired automatically. This reduces the risk of human error and simplifies and automates the production of products and parts. AI in 3D technology creates a wide range of possibilities for generating 3D objects  
development of AM technologies now makes it possible to support younger engineers with the knowledge of experienced experts in designing and supervising automatically implemented processes. This reduces the risk of human error and simplifies and automates the production of products and their parts, also as part of multi-material 3D printing. It can also have a positive impact on optimizing parameters or reducing the amount of waste. Additionally, ML, based on the data, extracts the rules that bind them, even if they are not obvious or have not been previously extracted and described. Artificial intelligence in 3D technology creates wide possibilities of generating 3D objects and enables a machine equipped with a vision system used in ML processes to analyze data similar to human thought processes. Additive printing using such a printer allows the production of objects of increasingly higher quality. The process itself is also precise and fast. The 3D printing system with artificial intelligence allows the device to adapt, for example, to various material properties, because the printer examines the 3D-printed surface and automatically adjusts the print. In addition, AI can help people cope with the abundance of information by extracting data that is useful in a specific case [2; 3]. On the foundations of Industry 4.0, the Industry 5.0 paradigm is being created, in which design and manufacturing are human-centered, resilient, and sustainable. Industry 5.0 will translate into Society 5.0 with an integrated cyber-physical platform in which people play a significant role. Such a digital transformation will modify several social sectors: health care, disaster management, and innovative economic models that include nature, and therefore a reconstruction of industry and society towards further technological progress is required [2; 4; 5; 6]. In intelligent production, it is crucial to maintain a high level of production utilization. For this purpose, the production line must always have enough materials and semi-finished products, but too many materials stored in the warehouse would increase the factory's operating costs. Moreover, the efficiency of different production lines, and therefore the rate of material consumption, may vary. IIoT-based supervisory control and data acquisition (SCADA) systems are becoming increasingly popular, and performance estimation is performed using AI. In this way, a Customer-to-Business (C2B) safety stock management model is built in a manufacturing company. In this model, inventory is managed by the supplier. This is particularly important in cases where the life cycle of materials and products is short and susceptible to market fluctuations. In addition to effectively reducing inventory levels, we gain increased competitiveness of the entire production and sales ecosystem through the digital transformation of production with the support of AI and IIoT [3; 7; 8; 9; 10; 11]. AI is revolutionizing AM (3D printing) by improving various aspects of the process, from design through production to finishing and fixing prints as far as predicting and monitoring their life cycle assessment (LCA) [12; 13].