At present, metal 3D printing is rapidly evolving from design verification to mass production of final parts. Metal 3D printing can now be applied to aerospace, medical, mold, automotive, energy power, rail transit, electronics and many other industries, and is still infiltrating more industries, and the overall market capacity is rapidly increasing.
However, great opportunities are often accompanied by severe challenges. Metal 3D printing has high barriers to entry. In the traditional mode, to enter this field, a novice first needs to spend months or even a year of theoretical study to establish an understanding of design, technology, materials, equipment operation, post-processing and other aspects. In the subsequent practice sessions, there will be a large number of printing failures, as well as the high cost of equipment, materials and time invested during this period.
In this mode, many people are unable to cross this steep learning curve, or cannot afford such a high price, and are ultimately blocked from the door of metal 3D printing, thus missing such a huge business opportunity, which is undoubtedly a people regret.
So, is there a better model that makes starting the path to metal 3D printing simple and efficient?
Artificial intelligence escorts the road to metal 3D printing
Many people have the experience of learning to swim as a child. No matter how much time you spend on shore learning theory, the real mastering of the technique starts from the moment you jump into the water. But the courage to jump into the water is largely due to the sense of security that the coach brings around. On the one hand, the coach can give guidance, but the more important role is to ensure that there are no accidents.
The same goes for the process of learning metal 3D printing. Novices who rely solely on theoretical learning can only stay at the stage of talking on paper. The key to speeding up the learning process is to enter the actual combat link as soon as possible. However, in the process, most people give up halfway because they cannot bear the huge frustration.
Imagine learning how to 3D print metal if you had a “coach” to guide you through the process and help avoid all the risks that could lead to failure.
At Oqton, we believe that AI can play this “coach” role. The unique advantage of artificial intelligence is that it can form knowledge by learning from massive data, and use this knowledge to make decisions. So, from the very beginning of Oqton, AI has been the mainstay of the production platform we’ve built.
Under the escort of the “coach” of artificial intelligence, users can step by step, build confidence from little success, and with the improvement of technology and the growth of experience, more and more enjoy the fun and sense of achievement brought by exploration, and finally reach The other side of success.
How the Oqton platform solves the placement problem
A big challenge in metal 3D printing is how to find the best placement angle for the part, which has a decisive impact on printing quality and efficiency. The ideal placement angle can reduce the amount of support material used, improve the surface quality of the part, and greatly reduce the time and complexity of the post-processing process.
However, mastering this skill requires long-term experience. Novices are often at a loss when faced with such open-ended questions.
To address this challenge, the Oqton platform uses an AI-based placement algorithm. The algorithm has been trained on a large number of parts and validated with numerous customers, ensuring that it is reliable enough even when the user is first getting started.
The Oqton platform can automatically identify the part type, and can also determine the placement angle according to the important surface of the feature identification part, thus ensuring the surface quality of the part. While providing recommended placement, the platform will also give reasons for choosing this placement method. For beginners, this method is not only convenient and fast, but also a good tool for learning the principle of placement.
As experience grows, users may express different opinions about the placement given by the Oqton platform. The platform will learn according to the user’s preference to adapt to the user’s usage habits and grow together with the user.
The Oqton platform always starts with one or more recommended placements and lets users choose. If users want to change the placement method, they only need to make manual adjustments, and the platform will recommend this new placement method for similar parts in the future. This approach not only helps improve efficiency, but also achieves a perfect combination of personalization and automation.
