3M INNOVATIVE PROPERTIES COMPANY (20240286166). ADHESIVE DISPENSING SYSTEMS AND METHODS simplified abstract
Contents
ADHESIVE DISPENSING SYSTEMS AND METHODS
Organization Name
3M INNOVATIVE PROPERTIES COMPANY
Inventor(s)
John A. Merchant of Apple Valley MN (US)
Brianna L. Mccord of Shoreview MN (US)
Alissa P. Wenner of Woodbury MN (US)
Aline Serrao De Filippo of Falcon Heights MN (US)
ADHESIVE DISPENSING SYSTEMS AND METHODS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240286166 titled 'ADHESIVE DISPENSING SYSTEMS AND METHODS
Simplified Explanation: The patent application describes apparatus, systems, and methods for predicting parameters, dispensing materials, and calibrating dispenser systems using machine learning algorithms and environmental variables.
- Predict parameters of dispenser systems
- Dispense dispensable materials
- Calibrate dispenser systems
- Use machine learning algorithms based on environmental variables
- Adjust operational parameters based on process parameters
- Provide settings for dispenser components based on calibration models
- Operate on remote or local software and control systems
Key Features and Innovation: The innovation lies in the use of machine learning algorithms to predict parameters, dispense materials, and calibrate dispenser systems based on environmental variables and other factors.
Potential Applications: This technology can be applied in various industries where adhesive dispensers are used, such as manufacturing, construction, and electronics.
Problems Solved: This technology addresses the challenges of accurately predicting parameters, dispensing materials efficiently, and calibrating dispenser systems effectively.
Benefits: The benefits of this technology include improved accuracy in dispensing materials, increased efficiency in dispenser systems, and enhanced calibration processes.
Commercial Applications: "Predicting Parameter of Dispenser System" technology can be utilized in industries such as automotive manufacturing, electronics assembly, and construction for precise dispensing of adhesives, sealants, and other materials.
Questions about Predicting Parameter of Dispenser System: 1. How does machine learning improve the accuracy of predicting parameters in dispenser systems? 2. What are the potential cost-saving benefits of using this technology in manufacturing processes?
Frequently Updated Research: Researchers are constantly exploring new machine learning algorithms and environmental variables to further enhance the predictive capabilities of dispenser systems.
Original Abstract Submitted
apparatus, systems (), and methods for predicting a parameter of dispenser system, dispensing a dispensable material, calibrating a dispenser system are described. apparatus and systems can use machine learning algorithms based on environmental variables and other factors in a system where the adhesive dispenser () is used. furthermore, apparatus and systems can adjust an operational parameter of the dispenser system based on at least one process parameter. still further, apparatus and systems can provide one or more settings for one or more dispenser components based on a calibration model. algorithms () can operate on remote or local software and control systems, or as part of an edge computing system or internet of things (iot) system.
