Using Artificial Intelligence to Enhance Flexibility of Digital Manufacturing
In order to ensure an industrial enterprise works successfully, it is necessary to make the production processes as efficient as possible. Each and every step needs to be optimized for minimizing the chance of errors and maximizing production speed and product quality.
The digitalization of manufacturing provides many additional opportunities. Particularly, the business gets the ability to conduct a complete assessment of the entire production chain in real time. Digital communication connects all processes and allows for their improvement by introducing new technologies, such as AI.
For example, Protolabs – a company producing technical components from a variety of materials – has digitalized its production processes and already started implementing AI solutions. AI analyzes huge streams of data and helps operators make decisions. In particular, the use of AI increases the speed of decision-making and helps to better manage resources.
The coronavirus pandemic has spurred the development of fully digital manufacturing. Such a system requires far fewer operators to be present on the shop floor. Experienced specialists can solve problems at the early stages – the computer vision technology provides this opportunity. Along with optimizing all production processes, digitalization adds another important dimension to it – data.
In digital manufacturing, it becomes possible to automate processes by learning from all previous experience. Data analysis allows figuring out what works (and how well) and what doesn’t. The next step is to use AI to see how to improve what works ineffectively. The more qualitative data there is, the more efficient the processes become.
Sometimes engineers can help with optimization based on their personal experience. But quite often, on the basis of similar processes that took place earlier, new and improved production chains are created automatically. Final decisions are always made by specialists, but AI helps in making them at all stages.
When parts are being produced, the AI creates a digital equivalent of each model for evaluation. Then, AI recommends the best way to produce a particular part. Recommendations can include the positioning of the product in the mold, the tools needed to make it, the machining path, etc. These calculations will allow manufacturers to achieve fast and high-quality production.
The main goal of AI in manufacturing is not to replace humans, but to simplify the interaction of humans with automated processes. Digital communication between a network of machines allows prioritizing production queues, setting up calibration, and then monitoring the processes on each machine in real time. Each manufactured part is compared with the reference digital twin, and in case of discrepancies, operational changes are made.
In the past, it was possible to identify production errors only at the final stage, after receiving a finished part. But now, it is possible at the very moment when something starts to go wrong. And this is just one of the many possible applications of AI, not only in manufacturing but also in FinTech, eCommerce, and other fields. The need for investing in AI and other IT technologies is obvious; the only question is whether to create your own IT department or approach professionals.