18368699. Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device simplified abstract (Siemens Aktiengesellschaft)
Contents
- 1 Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device
Organization Name
Inventor(s)
Anja Von Beuningen of Erfurt (DE)
Martin Bischoff of Aying Großhelfendorf (DE)
Hans-Dimitri Papdo Tchasse of N¿rnberg (DE)
Georgios Vasiadis of Erlangen (DE)
Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device - A simplified explanation of the abstract
This abstract first appeared for US patent application 18368699 titled 'Control Method, Control System and Computer Implemented Method for Determining a Predicted Weight Value of a Product Produced by an Injection Molding Device
Simplified Explanation
The patent application describes a method for determining the predicted weight value of a product produced by an injection molding device using a machine learning model trained with production parameters and weight values of previous products.
- The method involves recording production parameters of the injection molding device during the production of a product, as well as the weight values of predecessor products.
- The machine learning model is trained using supervised learning with the recorded parameters and weight values to predict the weight value of the product.
Potential Applications
This technology could be applied in industries using injection molding processes to optimize product quality and reduce manufacturing costs.
Problems Solved
This technology helps in predicting the weight value of products accurately, which can lead to better quality control and cost efficiency in manufacturing processes.
Benefits
The benefits of this technology include improved product quality, reduced manufacturing costs, and enhanced efficiency in production processes.
Potential Commercial Applications
One potential commercial application of this technology could be in the automotive industry for producing high-quality and cost-effective plastic components.
Possible Prior Art
One possible prior art could be the use of machine learning models in manufacturing processes for quality control and optimization.
Unanswered Questions
How does this technology compare to traditional methods of predicting product weight values in injection molding processes?
This article does not provide a direct comparison between this technology and traditional methods of predicting product weight values in injection molding processes.
What are the limitations of using machine learning models for predicting product weight values in injection molding processes?
This article does not discuss the limitations of using machine learning models for predicting product weight values in injection molding processes.
Original Abstract Submitted
Control method, control system, computer-implemented method for determining a predicted weight value of a product produced by an injection molding device and a computer-implemented method for training a machine learning (ML) via an ML method, wherein the trained ML model is configured to determine the predicted weight value of the product produced via the injection molding device, where the method comprises recording and/or determining first production parameters of the injection molding device during production of a first product, recording and/or determining predecessor production parameters of the injection molding device during production of at least one predecessor product and each predecessor weight value of the at least one predecessor product, recording and/or determining a first weight value for the first product, and training the ML model, via a supervised learning method, with the first product parameters, further product parameters, at least one predecessor weight value, and the first weight value.
