Real-Time Additive Manufacturing Irregularity Detection
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Solution Overview
Problem
Current 3-D printing processes lack real-time monitoring and control, leading to potential manufacturing errors and structural irregularities that can compromise the integrity and safety of printed parts, especially in applications requiring high structural integrity like aerospace and construction.
Innovation Solution
A system that captures data arrays of freshly manufactured layers using sensors and processes them through computational algorithms, machine learning, and artificial intelligence to detect irregularities, assess risk, and terminate the manufacturing process if thresholds are exceeded, allowing for non-destructive evaluation and minimizing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional post-manufacturing quality inspection methods are used, then parts can be evaluated for quality, but all material is wasted when parts are rejected
Solution Approach 1:
The system performs quality assessment actions during the manufacturing process itself rather than after completion. Sensors detect irregularities in real-time as layers are being printed, and the system can terminate the process early if defects are found, preventing waste of remaining materials while ensuring quality control.
2Reliability
If real-time monitoring systems are implemented, then manufacturing irregularities can be detected early, but device complexity increases
Solution Approach 1:
The patent uses sensors as intermediary devices that capture data about the manufacturing process, and computational algorithms as intermediaries that process this data to detect irregularities. This mediation approach allows complex monitoring functionality to be implemented through modular components rather than a monolithic complex system.
Solution Approach 2:
The system replaces traditional mechanical inspection methods with sensor-based detection and computational analysis. Instead of physical inspection after manufacturing, the system uses optical, electrical, or other sensor types to detect irregularities during the printing process, reducing mechanical complexity while improving reliability.
3Strength
If advanced materials with fillers are used, then structural integrity is improved, but manufacturing precision becomes more difficult to control
Solution Approach 1:
The system continuously monitors the extrusion process and provides real-time feedback about material flow, layer formation, and potential irregularities. When advanced materials with fillers are used, the feedback mechanism detects deviations from expected behavior and can alert operators or automatically adjust parameters to maintain manufacturing precision despite the complex material properties.
Data Source
AI summary
A system for detecting irregularities in three-dimensional parts being manufactured using an additive manufacturing process. Data arrays (e.g., images) of the manufactured cross section are obtained via a sensor (e.g., camera, LiDAR sensor, laser measuring sensor, ultrasonic sensor), then processed for irregularities as the object manufacturing cycle progresses. This data is processed through computational algorithms in order to identify areas of compromised integrity or quality. This data is then used to determine the risk of the part as manufactured, and an assessment is performed to determine if the process should continue. Should the manufacturing process be determined to proceed, the data is stored to be further assessed later by technicians, operators, and/or engineers. Additionally, the data can be utilized to perform a structural analysis to determine if the part is sufficient with the flaws present.