Nano Dimension Technologies Ltd (20240255916). NOZZLE MONITORING AND MANAGEMENT IN 2D AND/OR 3D INKJET PRINTING SYSTEMS simplified abstract
Contents
NOZZLE MONITORING AND MANAGEMENT IN 2D AND/OR 3D INKJET PRINTING SYSTEMS
Organization Name
Nano Dimension Technologies Ltd
Inventor(s)
Dany Rovinsky of Givat Shmuel (IL)
Eri Rubin of Kibbutz Ma'ale Ha'hamisha (IL)
NOZZLE MONITORING AND MANAGEMENT IN 2D AND/OR 3D INKJET PRINTING SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240255916 titled 'NOZZLE MONITORING AND MANAGEMENT IN 2D AND/OR 3D INKJET PRINTING SYSTEMS
Simplified Explanation: The patent application describes methods, computer program products, and modules for monitoring nozzles in 2D and/or 3D inkjet printing systems using neural networks trained on images of printed products.
Key Features and Innovation:
- Monitoring and evaluating nozzle performance in inkjet printing systems.
- Applying a neural network trained on registered images of printed products to manage nozzles.
- Utilizing a deep learning model to improve performance over manual analysis.
- Implementing the methods in both 2D inkjet printing and 3D additive manufacturing systems.
Potential Applications: The technology can be used in various industries that utilize inkjet printing, such as packaging, textiles, and 3D printing.
Problems Solved: The technology addresses the challenges of manually monitoring and managing nozzles in inkjet printing systems, leading to improved efficiency and quality control.
Benefits:
- Enhanced performance and accuracy in monitoring nozzle performance.
- Increased productivity and reduced downtime in inkjet printing processes.
- Improved quality control and consistency in printed products.
Commercial Applications: The technology can be applied in commercial printing services, manufacturing industries, and research institutions to optimize inkjet printing processes and enhance product quality.
Prior Art: Readers can explore prior research on inkjet printing systems, neural networks, and deep learning models in the field of additive manufacturing and quality control.
Frequently Updated Research: Stay updated on advancements in neural network applications in inkjet printing systems, deep learning models for quality control, and innovations in additive manufacturing technologies.
Questions about Nozzle Monitoring: 1. How does the neural network approach improve the monitoring of nozzles in inkjet printing systems? 2. What are the potential challenges in implementing this technology in different types of inkjet printing systems?
Original Abstract Submitted
methods, computer program products and nozzle monitoring modules are provided, to monitor nozzles in 2d and/or 3d inkjet printing systems. nozzle monitoring comprises registering an image of a printed product with respect to a corresponding raster file and evaluating nozzle performance and managing nozzles by applying a neural network (nn) trained on a plurality of registered images of the printed product and corresponding raster files. the disclosed nn approach may apply a deep learning model, and has been shown to improve performance over manual analysis of images of printed products (which is the current method of monitoring nozzles). disclosed methods and modules may be implemented in 2d inkjet printing system and/or in 3d additive manufacturing (am) inkjet printing system, monitoring nozzles that deposit layers of the 3d product.
