Automated Component Identification via Dual-Classifier ML
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Solution Overview
Problem
Complex inventories in modern manufacturing operations face challenges in accurately documenting and locating component parts due to technical jargon, informal language, and different nomenclatures, leading to delays and disruptions in supply chains.
Innovation Solution
An automated system using image-based and text-based machine learning models to identify objects as component parts by applying digital images to a classifier trained on component part images and textual information to a classifier trained on textual data, updating or retrieving data records accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual documentation methods are used to track component parts, then system complexity is low, but identification accuracy and documentation reliability deteriorate due to technical jargon, informal language, and different nomenclatures
Solution Approach 1:
The patent replaces manual documentation systems with an automated machine learning-based identification system. Image-based and text-based classifiers automatically identify component parts, eliminating human error from manual documentation while handling technical jargon and varying nomenclatures through trained algorithms.
Solution Approach 2:
The system creates digital copies of component parts through image capture and textual description. These digital representations are processed by classifiers to identify parts, enabling accurate tracking without physical handling or manual recording of each component.
2Measurement precision
If automated identification systems are implemented, then identification accuracy improves, but processing time increases due to multiple classification steps
Solution Approach 1:
The identification process is divided into separate image-based classification and text-based classification pathways. These parallel segmentation approaches process different types of input data simultaneously, improving overall identification accuracy while maintaining efficient processing through specialized classifiers for each data type.
3Reliability
If comprehensive data records are maintained for each component part, then inventory tracking reliability improves, but data management complexity increases
Solution Approach 1:
The patent implements a universal data record structure that handles multiple component part types and documentation requirements through a single standardized system. The classifier outputs and data record format are designed to accommodate diverse component parts, technical jargon, and nomenclatures uniformly, simplifying data management while maintaining comprehensive tracking reliability.
Data Source
AI summary
A method for identifying a component part includes receiving a digital image of an object and textual information about the object and accessing images of component parts and textual information about the component parts. The method further includes applying the digital image to a first classifier trained on the images of the component parts to classify the object as a first of the component parts and applying the textual information about the object to a second classifier trained on the textual information about the component parts to recognize the textual information as information about the first of the component parts or a second of the component parts. The method further includes identifying the object as a component part that is the first of the component parts or the second of the component parts and accessing a data record with information about the component part.