17986572. SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT simplified abstract (Ford Global Technologies, LLC)
Contents
- 1 SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT - 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
SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT
Organization Name
Inventor(s)
Ranjanikar Vinay of Chennai (IN)
Chekuri Prudhveeraju of Chennai (IN)
Chhapparghare Hivraj Vijay of Nagpur (IN)
SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 17986572 titled 'SYSTEMS AND METHODS FOR COMPONENT DETECTION IN A MANUFACTURING ENVIRONMENT
Simplified Explanation
The method described in the abstract involves segmenting point cloud data of an image into clusters, filtering the data to identify production component cloud data, aligning and classifying the data, generating a bounding box, and determining parameters associated with the components.
- Segmentation of point cloud data into clusters
- Filtering to identify production component cloud data
- Aligning and classifying the data
- Generating a bounding box
- Determining parameters associated with the components
Potential Applications
This technology could be applied in industries such as manufacturing, construction, and robotics for object recognition, quality control, and automation processes.
Problems Solved
This technology helps in automating the process of analyzing and classifying point cloud data, which can be time-consuming and error-prone when done manually.
Benefits
The benefits of this technology include increased efficiency, accuracy, and consistency in analyzing and classifying point cloud data, leading to improved decision-making and productivity.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of automated quality control systems for manufacturing processes.
Possible Prior Art
Prior art in this field may include existing methods for segmenting and analyzing point cloud data, as well as technologies for object recognition and classification in images.
What are the specific asset types that can be classified based on the rotated PCCD?
The abstract mentions that the given set of production components can be classified into one or more asset types based on the rotated PCCD. It would be interesting to know the specific asset types that can be identified using this method.
How does the method determine the parameters associated with the production components?
The abstract mentions that the method determines one or more parameters associated with the production components based on the production clusters and asset types. It would be helpful to understand the specific parameters that are considered and how they are calculated.
Original Abstract Submitted
A method includes segmenting point cloud data of an image into a plurality of input clusters, wherein each of the plurality of input clusters includes a given set of point cloud data from among the point cloud data. The method includes, for each of the plurality of input clusters: selectively filtering the given set of point cloud data to identify production component cloud data (PCCD), aligning the PCCD with a predefined axis to generate a rotated PCCD, classifying the given set of the plurality of production components into one or more asset types based on the rotated PCCD, generating a three-dimensional bounding box based on the rotated PCCD, segmenting the rotated PCCD into a plurality of production clusters, and determining one or more parameters associated with the given set of the plurality of production components based on the plurality of production clusters and the one or more asset types.
