17936492. MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S) simplified abstract (International Business Machines Corporation)
Contents
- 1 MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S) - 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
MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S)
Organization Name
International Business Machines Corporation
Inventor(s)
John S. Werner of Fishkill NY (US)
Arkadiy O. Tsfasman of Wappingers Falls NY (US)
Dane Warren of Highland NY (US)
Charles Bene of Poughkeepsie NY (US)
Ryan Mulhern of Mahopac NY (US)
Thomas C. Reed of Tucson AZ (US)
MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S) - A simplified explanation of the abstract
This abstract first appeared for US patent application 17936492 titled 'MACHINE LEARNING-BASED PART SELECTION BASED ON ENVIRONMENTAL CONDITION(S)
Simplified Explanation
The abstract describes a machine learning-based process for selecting parts based on environmental conditions for use in products.
- Training a machine learning model to evaluate parts for use in products based on environmental conditions.
- Receiving measurement data for the part.
- Establishing a score for the part by comparing the measurement data to a specification.
- Using the machine learning model and the established score to determine whether to use the part in the product based on the environmental condition.
Potential Applications
This technology could be applied in industries where parts selection based on environmental conditions is crucial, such as automotive, aerospace, and electronics manufacturing.
Problems Solved
This technology helps in automating the part selection process based on environmental conditions, ensuring that only suitable parts are used in products, which can lead to improved product performance and reliability.
Benefits
The benefits of this technology include increased efficiency in part selection, reduced risk of product failure due to environmental factors, and overall improved product quality.
Potential Commercial Applications
The technology could be used in various industries for optimizing part selection processes, leading to cost savings, improved product performance, and customer satisfaction.
Possible Prior Art
One possible prior art could be traditional methods of part selection based on manual evaluation and testing, which may not be as efficient or accurate as machine learning-based approaches.
Unanswered Questions
How does this technology handle complex environmental conditions that may affect part performance?
The machine learning model is trained to consider a wide range of environmental conditions and their impact on part performance, allowing for more accurate and reliable part selection decisions.
What types of parts and products can be evaluated using this technology?
The technology can be applied to a variety of parts and products across different industries, as long as there are specific environmental conditions that need to be considered for optimal performance.
Original Abstract Submitted
Machine learning-based part selection in relation to one or more end use environmental conditions is provided. The process includes training a machine learning model to facilitate evaluation of a part for use in a product based on an environmental condition. Further, the process includes receiving measurement data for the part, and establishing a score for the part by comparing the measurement data for the part to a specification for the part. In addition, the method includes using the machine learning model and the established score for the part in determining whether to use the part in the product based on the environmental condition.
