The integration of AI and 3D printing in manufacturing can help increase unit production rate, detect defects, and provide real-time control over the manufacturing process.
3D printing and additive manufacturing are often used interchangeably as the former is a subset of the latter. As the name suggests, additive manufacturing is a method of building products by adding layers of components on one another. 3D printing in manufacturing is an approach to build light, strong parts, and systems for industrial production. 3D printing comes with many different advantages over other traditional manufacturing methods, like reduction in costs, faster rates production, and provide creative design freedom. AI, on the other hand, as everyone knows, can automate monotonous tasks and bring accuracy in those tasks. The manufacturing sector has many repetitive labor tasks that make AI a perfect match for the manufacturing and 3D printing process.. AI can increase the production rate and accuracy of 3D production. Using computer vision, manufacturers can reverse engineer the existing models and create a new and improved product design. The improved product design can then help accelerate the production rate. Through many such similar AI applications, manufacturers can further increase the efficiency of 3D printing operations.
Benefits and Challenges of 3D Printing in Manufacturing
The automated process of creating designs with the help of 3D printing can help reduce the production cost in the manufacturing industry. For other printing methods like offset and flexo, labor is required to stand beside the machine for the entire printing process to collect and put together the pieces of product. Whereas in 3D printing, labor has to start the machine and upload the design. Once the design is uploaded, 3D printing machines will auto-create the products and assemble them. The automated process will eliminate the need for skilled labor to remain by the side of the machine for the entire process, thereby reducing the costs. If allowed to think creatively and create some experimental designs, manufacturing designers can come up with many new designs. The 3D printing process can give manufacturing designers the freedom they need to explore and experiment. A 3D prototype is easy to alter as compared to the prototypes created by other printing methods. Easy alteration of design can increase flexibility in designer’s creativity and help them create innovative designs. Further, geometric designs like holes and square cavities that were once difficult to create can now easily be created with the help of 3D printing. Although there are many benefits of using 3D printing in manufacturing, its limitations cannot be neglected. Limitations like detecting defects in design, efficiency in the prefabrication process, and real-time control are yet to be overcome. However, with the use of AI technology, manufacturers can overcome the limitations and increase the overall efficiency of the 3D printing process.
The Amalgamation of AI and 3D Printing in Manufacturing
Manufacturing involves many complex variables to be monitored throughout the process to ensure acceptable levels of product quality. The trial and error method is not enough to get the perfect lattice position or design. AI technology, with the help of generative design and testing the prefabrication stage, can help improve efficiency.
Efficiency in Prefabrication Stage
Machine learning systems can evaluate and optimize design files for 3D printing. Manufacturers can input design files with the desired output parameters into a machine learning system. Analysis can then be performed on the design files to get the most efficient path to design the product described in the file. Machine learning tools can also potentially assist in optimizing the lattice structure in the input files itself before uploading the files to create the end product. For instance, a French-based company has launched a software suite called Agile Metal Technology. The company says that agile metal technology software can accept CAD files from users and assist the user in improving the 3D design. Another application of AI to improve efficiency in the prefabrication stage is generative design. Manufacturers and engineers can input the needs of their end products into generative design software. Generative design software can then, with the use of previous design data from the cloud, generate many possible designs that can meet the needs of the user. For instance, Netfabb is a generative design software created by an American company that can rapidly convert 3D models to printed parts.
A New York lab has created software that uses computer vision to detect defects in the additive layers of 3D printing. The computer vision system created can spot defects that cannot be seen by human eyes. First, the printing process of each layer is captured with the use of a high-resolution camera. The camera captures the streaks, pits, and divots of each layer that cannot be seen by a human. Images are then passed into a machine learning software. The software uses machine learning to match recorded patterns to defect patterns revealed by CT scanners. Machine learning software is trained to detect defects from the differences in the recorded and CT scanner patterns. Whenever manufacturers handle the manufacturing powder bed, there is a chance of contamination of the powder. Computer vision can help detect the contamination caused by manufacturers. With content monitoring, computer vision can help detect defects, and the option to stop or continue the process is left with the user. Detecting defects at an early stage can further help to achieve real-time control over the manufacturing process.
3D printing is already being used to manufacture metal parts. The metal parts, however, needs to be in perfect structural integrity to be ready for use. For instance, large and crucial parts like jet turbine blades need to be precisely shaped. Manufacturing jet turbine blades requires a lot of time. And, if the end product has even a marginal defect, the entire product has to be thrown out. Defect in such large parts like jet turbine blades can waste both time and materials. Artificial intelligence and computer vision can together make a linkage between what is happening with the powder used to manufacture products and the end product itself. Defect detection allows a user to control and stop something that can make the end product waste.
Manufacturing companies need to keep a stock of spare parts that are used for the functioning of machinery. These spare parts can be costly and cannot be kept for a long time as there possibilities of rusting. On the other hand, manufacturers cannot take the risk of not having a stock of spare parts as then they will have to wait for new parts to arrive if a machine breakdown occurs. Hence, it becomes necessary for manufacturers to maintain spare parts inventory, which can be easily done with the help of predictive analysis. Machine learning algorithms can predict the life span of spare parts. For instance, metal parts can get rusted after a certain time. ML algorithms can predict such possibilities and alert the manufacturer before anything like that may happen. Predicting the life span of spare parts can help manufacturers take appropriate and necessary actions quickly. ML algorithms can further determine the ideal time for part replacement with the use of spare part life span prediction.
AI and 3D printing in manufacturing will make autonomous printing factories a possibility. And once autonomous printing becomes a reality, manufacturing companies can put their human resources to focus on other decision-making tasks. Machine learning and cognitive computing can allow AI-enabled robotic arms to take small decisions themselves, and create viable parts, without any problems. Further, with the introduction of other technologies like IoT in manufacturing, AI and 3D printing applications can be the next wave to sweep the manufacturing industry.