Siemens Aktiengesellschaft (20240246151). Methods for Additive Manufacturing of a Component simplified abstract
Methods for Additive Manufacturing of a Component
Organization Name
Inventor(s)
Raven Thomas Reisch of München (DE)
Tobias Hauser of Bubesheim (DE)
Methods for Additive Manufacturing of a Component - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240246151 titled 'Methods for Additive Manufacturing of a Component
Simplified Explanation: The patent application describes a method for additive manufacturing of a component using sensors and a digital twin to identify and eliminate anomalies during the manufacturing process.
- Key Features and Innovation:
* Creation of a machine code and transmission to a controller. * Monitoring the manufacturing process with sensors. * Establishing a digital twin of the component from sensor data. * Predicting the position of the print head using the machine code. * Analyzing the working area around the predicted position to detect anomalies. * Adjusting process parameters to eliminate anomalies in real-time.
Potential Applications: This technology can be applied in various industries such as aerospace, automotive, and healthcare for the additive manufacturing of complex components with high precision and quality control.
Problems Solved: This technology addresses the challenge of detecting and eliminating anomalies during the additive manufacturing process, ensuring the production of high-quality components.
Benefits: The benefits of this technology include improved manufacturing efficiency, reduced waste, enhanced quality control, and the ability to produce complex components with minimal defects.
Commercial Applications: Potential commercial applications of this technology include the production of customized medical implants, lightweight aerospace components, and high-performance automotive parts.
Prior Art: Prior art related to this technology may include research on real-time monitoring and control systems for additive manufacturing processes, as well as studies on digital twins in manufacturing.
Frequently Updated Research: Researchers are continuously exploring new sensor technologies, machine learning algorithms, and process optimization techniques to enhance the capabilities of additive manufacturing systems.
Questions about Additive Manufacturing: 1. How does the use of sensors and digital twins improve the additive manufacturing process? 2. What are the potential challenges in implementing real-time anomaly detection and correction in additive manufacturing?
Original Abstract Submitted
various embodiments of the teachings herein include a method for additive manufacturing of a component. an example method includes: creating a machine code and transmitting the machine code to a controller; starting an additive manufacturing process to build the component using a print head; monitoring the process with sensors; evaluating sensor data to identify anomalies in the component during the manufacturing process; establishing a parallel digital twin of the component from sensor data comprising position data of the anomaly; predicting a position of the print head at a specific time using the machine code; analyzing a working area around the predicted position with respect to anomalies present using the digital twin; and adjusting process parameters of the manufacturing process when the working area is reached to eliminate the anomaly.
