Unknown Organization (20240255915). METHOD, SYSTEM AND DEVICE FOR ACQUISITION AND PROCESSING OF ELASTIC WAVES AND FIELD SENSOR DATA FOR REAL-TIME IN-SITU MONITORING OF ADDITIVE MANUFACTURING simplified abstract
METHOD, SYSTEM AND DEVICE FOR ACQUISITION AND PROCESSING OF ELASTIC WAVES AND FIELD SENSOR DATA FOR REAL-TIME IN-SITU MONITORING OF ADDITIVE MANUFACTURING
Organization Name
Inventor(s)
Cetin Cetinkaya of Potsdam NY (US)
METHOD, SYSTEM AND DEVICE FOR ACQUISITION AND PROCESSING OF ELASTIC WAVES AND FIELD SENSOR DATA FOR REAL-TIME IN-SITU MONITORING OF ADDITIVE MANUFACTURING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240255915 titled 'METHOD, SYSTEM AND DEVICE FOR ACQUISITION AND PROCESSING OF ELASTIC WAVES AND FIELD SENSOR DATA FOR REAL-TIME IN-SITU MONITORING OF ADDITIVE MANUFACTURING
Simplified Explanation: The patent application describes a system that uses multi-mode elastic wave generating and detecting devices to monitor the quality of a specially designed article made by an additive manufacturing machine in real-time.
- The system involves transmitting and receiving waves into a test artifact while it is being built.
- It processes data from narrow and wide field-of-view sensors and correlates the waveforms and sensor data using physics-based and machine learning models.
- The system can initiate control and corrective actions based on the properties and characteristics of the obtained waveforms and sensor data.
Key Features and Innovation:
- Utilization of multi-mode elastic wave generating and detecting devices.
- Real-time in-situ monitoring system for quality assessment of additive manufacturing products.
- Transmission and reception of waves into a test artifact while it is being built.
- Processing of data from sensors and correlating waveforms and sensor data.
- Initiation of control and corrective actions based on data analysis.
Potential Applications: The technology can be applied in additive manufacturing, quality control processes, industrial automation, and real-time monitoring systems.
Problems Solved: The system addresses the need for real-time quality assessment of products during the additive manufacturing process, enabling immediate corrective actions to be taken.
Benefits:
- Improved quality control in additive manufacturing.
- Real-time monitoring for early detection of defects.
- Enhanced efficiency and productivity in manufacturing processes.
Commercial Applications: The technology can be utilized in industries such as aerospace, automotive, healthcare, and electronics for quality assurance and process optimization.
Prior Art: Readers can explore prior patents related to additive manufacturing quality control systems and real-time monitoring technologies to understand the existing landscape.
Frequently Updated Research: Stay updated on advancements in additive manufacturing quality control systems, real-time monitoring technologies, and machine learning applications in manufacturing processes.
Questions about the Technology: 1. What are the potential limitations of using multi-mode elastic wave generating and detecting devices in additive manufacturing quality control? 2. How does the system differentiate between normal variations and actual defects in the test artifacts during the manufacturing process?
Original Abstract Submitted
a set of multi-mode elastic wave generating and detecting devices and field sensors are utilized in a real-time in-situ monitoring system based on the quality assessment of a specially designed article made by an additive manufacturing machine. the original invention disclosed in u.s. patent application ser. no. 15/731,366 involves the transmission and reception of waves into a periodic test artifact while it is being built. the current invention involves the transmission and reception of multi-mode waves into a test artifact, the processing of data from narrow and wide field-of-view sensors, and correlating and relating the waveforms and sensor data while it is being built using physics-based and machine learning models. the disclosed system may initiate control and real-time corrective actions based on the properties and characteristics of the obtained waveforms and sensor data and their correlations and functional relationships.
