This is already where companies can benefit from artificial intelligence. Intelligent designĪt the beginning of every 3D-printed component is a file, in most cases a CAD file. For this reason, more and more companies are trying to offer a comprehensive software solution with which the advantages of artificial intelligence can be exploited in the best possible way for the additive manufacturing process. For example, the design already influences the subsequent component quality and the desired component quality influences the design. In this context, it is also important to understand that all steps along the additive value chain influence each other, which is why an isolated view is not expedient in most cases. The next step is to find and integrate the appropriate measurement tool for capturing the values before defining a suitable model or algorithm for data collection and processing. This decision depends in each case on the process used. In doing so, it is first important for companies to define which data is relevant at all. Since countless data are collected and processed (in real time) along the additive value chain, they can be used to analyze the ACTUAL state and subsequently redefine the TARGET state. How is machine learning used in additive manufacturing?Īs a digital production process, additive manufacturing benefits from the capabilities of machine learning. Users must therefore choose the appropriate method based on the raw data and the target variable. For example, there is still semi-supervised learning, which uses only a small amount of predefined data in a large amount of raw data to train the model, and reinforcement learning, in which the system learns itself based on predefined rules. This type of Machine Learning is used, among other things, in marketing to identify customer segments, so-called “clustering”. The software does not have a target variable (output data), but must recognize patterns or suggest solutions based on the input data. In Unsupervised Machine Learning, the opposite is true as a starting point. From speech recognition to intelligent chatbots to personalized treatment plans, Machine Learning is being used in a variety of applications. In the meantime, we encounter artificial intelligence every day in all areas of life. ![]() Since then, the foundation was laid and researchers became fascinated by the possibilities and potential of the technology. In doing so, the device learned from mistakes made in previous attempts, which improved the classification over time. In 1957, the Mark I Perceptron was the first major success in this field: the machine was able to classify input data independently. Contrary to a widespread belief that machine learning is a newfangled phenomenon, it can be said that its beginnings date back to the 1940s, when the first researchers started to recreate the neurons of the brain with electrical circuits. ![]() Machine Learning is a subcategory of AI and is defined as a system or software that uses algorithms to examine data and subsequently recognize patterns or determine solutions. We explain what machine learning is and why this form of AI is helping to shape the future of additive manufacturing. Artificial intelligence (AI) is able to process a large amount of complex data in a very short time, which is why it is becoming increasingly important as a decision maker. ![]() As a digital process itself, 3D printing is part of Industry 4.0 and thus an important component of an era in which artificial intelligence, such as machine learning, is increasingly being used to optimize the value chain. Thus, more and more manufacturers are relying on cloud-based solutions and integrating various algorithms into their 3D printing solutions in order to exploit the full potential of the technology. For many companies, digitization and automation are the keys to the further development of additive manufacturing.
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