May 24, 2018
Additive Manufacturing—No Chance without Automation
Schunk manufactures customized fingers for its grippers made of plastic, steel and aluminum additively and provides its customers with a continuous digital design and ordering process via the web tool eGrip for this, so that processing can also take place quickly.
The production of components manufactured additively usually does not fulfill industrial standards yet. Experts warn to keep an eye on the entire process chain – and to automate to a large extent.
He is 1.30 meters tall and has been a welcome guest at many trade fairs and events in recent years: the humanoid robot Roboy, which was developed at the Technical University of Munich. He not only seems likable thanks to his large innocent eyes, but also because he learns and thinks similar to people thanks to the use of artificial intelligence. In addition, he can also move like people.
Researchers achieved this by improving the hardware iteratively, i.e., step by step. In doing so, the simulation reached its limits; the mechatronic complexity of Roboy was too high in part for this. For example, to test hands, forearms and fingers in the real environment, these components were produced additively. This ultimately enabled rapid development cycles.
However, additive production is used for manufacturing automation products not only in research. For example, Schunk manufactures customized fingers for its grippers made of plastic, steel and aluminum additively and provides its customers with a continuous digital design and ordering process via the web tool eGrip for this, so that processing can also take place quickly.
The 3D print service provider Protolabs also works similarly.
Despite these examples from industrial practice, Rainer Gebhardt, Head of the Additive Manufacturing work group in VDMA, notes: “The production of printed components does not yet correspond to the long-established industrial manufacturing methods. In addition, there is still improvement potential in process automation of the printing process.”
The crux of the matter: Today, stand-alone machines dominate in additive manufacturing that are not able to achieve the productivity of complete process chains. “Although there are initial approaches of many different plant manufacturers, the various procedures are not yet considered as part of a process chain,” Professor Volker Schulze stated, one of the heads of the wbk Institute for Production Technology at KIT in Karlsruhe. “And even process chains do not fully exploit the possibilities of increasing efficiency. We need to go a step further and consider complete logistics and automation in the process chain. Only when we have created entire production systems for additive processes can we operate efficient additive manufacturing.”
Consequently, the Additive Manufacturing work group in VDMA has developed an automation roadmap for metal-processing processes in additive manufacturing, which points the way to automated manufacturing processes. In this, the current state-of-the-art of technology and ongoing developments were collected and the technological gaps identified that are still required for continuous automation along the process chain. Partial automation, full automation, and finally embedding in fully networked smart factories are possible in the sense of Industry 4.0.
This involves all data and physical processes. This starts in data preparation, conversion and control of the data records and continues with the working steps of the digital production preparation, in which it is a question of the quality, flow behavior and handling of the powders used, among other things. Then the actual layer construction process and the subsequent treatment of the printed components follow.
At the end of last year, members of the VDMA work group for the roadmap developed a possible definition for a “digital component file” as an information carrier. It contains data that are needed throughout the complete production process and which also ensure documentation in the context of quality assurance.
In addition, industry and research are working on solutions for automating additive manufacturing. For example, Balluff has developed sensors for fill level monitoring for granules and powders as well as for valve control and pressure measurement. At Frauhofer IPA, a retrofittable inline quality control system for powder-based processes has been developed, which automatically identifies defects and faults both in the powder layer as well as in the sintered layers directly in the production process by means of industrial machine vision.
Professor Schulze is certain: “Only production systems covering the entire value chain from material delivery and the actual additive process up to the post-processing and automatic quality control can help generative technology to achieve a permanent breakthrough in series production.”