The Intent of this research work is to explore the current applications of artificial intelligence (AI) in Additive manufacturing. Artificial Intelligence is the simulation of human intelligence processes by machines, especially, computer-based systems. With the advent of the Internet, Sensors, Big-Data, communication networks, with the development of e-commerce, Artificial Intelligence has been changed profoundly, leading to the new phase of AI -2.0.
Manufacturing is the main pillar of a country’s economy. With the advent of Artificial Intelligence (AI), people started using AI in manufacturing to improve productivity. This has resulted in smart manufacturing. This will have a profound effect on Country’s growth. Artificial Intelligence (AI) is being used in all the three area of Additive manufacturing. The Application area of AI in Additive manufacturing can be broadly classified as follows:
- Printability
- Improving Efficiency in Pre-fabrication
- Defect detection and classification
- Real time build control
- Predictive maintenance
- Material waste reduction
- Minimizing energy consumption
- Spare parts manufacturing
Lu proposed an improved method based on machine learning to achieve better reliability for implementing automatic rule adjustment. In the manufacturing industry printability is judged by many factors, such as, time, cost, raw material, component size, etc. With the increase part complexity, researchers have started working on Improving Efficiency in Pre-fabrication. Slicing is one of the important steps in 3D printing. The function of a slicer is to produce tool path so as to produce physical realization of the 3D input model by the AM system. Service oriented architecture forms heart of cloud-based manufacturing. SOA provides necessary technologies for efficiently meeting client requests.
One of the major advantages of cloud-based manufacturing is the scaling up of resources based on customer demand. This will make cloud-based manufacturing very flexible with respect to meeting customer’s expectations. Many of the AM produced parts have better mechanical properties (e.g. tensile strength) than conventional manufacturing processes. Though much advances have been done in 3D printing technology, not much attention is given to quality assurance. Thus, it is mandatory to post inspect the component before being put into service.
Product quality in additive manufacturing largely depends upon melt-pool, scan track and layer characteristics. But control strategies based on measurement of these process variables are limited. In many cases, adjustment of these process parameters is based on heuristics and past experience. This highlights the importance of having systems for accurate measurement of process variables and also closed loop control systems for maintaining consistent product quality. So that, additive manufacturing could produce products of high quality required for high performing products especially in aerospace and automobile sectors.
To summarize, AI has been used successfully in all the stages of additive manufacturing. This paper provides a comprehensive review of the usefulness of AI in additive manufacturing and also directions for future work and hence is useful for both practitioners and academicians.
Keywords
Artificial Intelligence, Industry 4.0, Additive manufacturing, AM, Smart manufacturing