Manufacturing Process Control Using ML Quality Prediction

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Solution Overview

Problem

Many assembly tasks in manufacturing processes remain difficult to automate due to cost and logistical challenges, leading to reliance on human operators, which can result in inconsistent quality metrics and inefficiencies.

Innovation Solution

A manufacturing system that includes a monitoring platform and control module to dynamically adjust processing parameters using image data and machine learning models to predict and achieve a desired final quality metric, allowing for real-time correction and optimization of manufacturing processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If human operators perform assembly tasks, then flexibility and adaptability are maintained, but manufacturing precision and consistency deteriorate

Engineering Contradiction:

ImproveflexibilityVSAvoidquality consistency

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The system enables self-service through autonomous quality assessment. Machine learning models automatically evaluate assembly quality from images, eliminating the need for human inspectors while maintaining consistent precision. The system serves itself by continuously learning from new data and improving its assessment accuracy over time.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces human mechanical assembly and inspection with automated robotic systems and computer vision. Robotic arms perform assembly tasks with repeatable precision, while imaging systems capture and analyze assembly quality, substituting human operators entirely for both manufacturing and quality assessment functions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If traditional process control is used, then operational simplicity is maintained, but measurement precision and quality prediction capability deteriorate

Engineering Contradiction:

Improveoperational simplicityVSAvoidquality metric accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system introduces an intermediary layer between simple operation and complex quality analysis. A control module acts as the intermediary, automatically capturing images, processing them through machine learning models, and translating complex quality metrics into simple pass/fail decisions that maintain operational simplicity while achieving high measurement precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates digital copies of the assembly process through imaging. Instead of physically measuring components, the system captures images that serve as digital replicas, allowing virtual analysis and quality assessment without interfering with the physical manufacturing process, thereby maintaining operational simplicity.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12165353B2Systems, methods, and media for manufacturing processes

Publication Date: 2024.12.10 NANOTRONICS IMAGING INC

AI summary

A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.