Scrap Material Identification with Filtration and Reuse in Additive Manufacturing
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Solution Overview
Problem
The challenge in 3D printing is the inefficient management of scrap materials, which vary in type, shape, and dimension, leading to unnecessary wastage and increased production costs, especially in industries like aerospace and automotive.
Innovation Solution
A system that integrates robotics, machine learning, and computer vision to identify, filter, and classify scrap materials generated during 3D printing, enabling their systematic and precise reuse in subsequent printing jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If scrap materials are reused without filtration and classification, then production costs are reduced, but material purity and quality consistency deteriorate
Solution Approach 1:
The system segments scrap materials into different categories based on their properties (material type, size, shape, contamination level) using computer vision and machine learning algorithms. This segmentation enables targeted filtration and classification approaches for different scrap types, maintaining quality consistency while maximizing reuse potential.
Solution Approach 2:
The patent introduces an intermediary filtration and classification system between scrap generation and reuse. This intermediary system includes sensors, filters, and sorting mechanisms that clean and categorize scrap materials before they are fed back into the 3D printing process, ensuring material purity is maintained.
2Adaptability or versatility
If a comprehensive filtration and classification system is implemented, then material quality and sustainability are improved, but device complexity increases
Solution Approach 1:
The system employs multi-functional sensors and processing units that can handle multiple scrap material types and properties simultaneously. The computer vision system, for example, can detect material composition, geometric features, and contamination levels using the same imaging infrastructure, reducing the need for separate specialized devices.
Solution Approach 2:
The filtration and classification system uses automated machine learning algorithms and computer vision to autonomously identify, categorize, and sort scrap materials without human intervention. The system self-adjusts filtration parameters based on real-time analysis of scrap properties, reducing the need for complex manual control mechanisms.
3Measurement precision
If real-time analysis and filtering of scrap materials is performed, then material classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary sorting and pre-filtration of scrap materials before detailed analysis. Quick initial assessments based on basic properties (size, shape, obvious contamination) filter out clearly unsuitable materials, allowing more detailed and time-consuming analysis to be applied only to materials that require closer inspection.
Solution Approach 2:
The computer vision system uses periodic sampling and batch processing strategies where scrap materials are analyzed in cycles rather than continuously one-by-one. This allows the system to maintain high identification accuracy through thorough analysis while improving overall processing throughput by handling multiple items in efficient batches.
Data Source
AI summary
Systems and methods are provided for recovering LED light in indoor multi-level farms. A plurality of cameras and sensors capture in real time in a vertical farm environment a plurality of camera images and sensor data. The camera images include physical images of the vertical farm environment, and the sensor data includes light data. The camera images and sensor data are analyzed to identify a pattern of light origin and direction in the vertical farm environment. The plant characteristics are identified based on comparing the captured camera images, using image recognition, against a model. Based on the identified plant characteristics, manipulating the light data. The plant characteristics include plant species growth state and general health. The light is manipulated by breaking it into component visible light wavelengths. The model predicts optimizing the redirecting of the optimal wavelengths for improved plant growth.