ML-Guided Manufacturing Control for Real-Time Quality Correction

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Solution Overview

Problem

Current manufacturing processes often rely on human operators for assembly tasks due to challenges in automating low-quantity production runs, leading to inconsistencies in final product quality and inefficiencies in quality control, particularly in achieving desired quality metrics without destructive testing.

Innovation Solution

A manufacturing system comprising stations, a monitoring platform, and a control module that uses image data and machine learning to predict final quality metrics, adjusts processing parameters, and generates updated instructions for downstream stations to correct errors and ensure quality, leveraging tools like LSTM and GRU models for predictive analytics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If human operators perform assembly tasks, then flexibility in low-quantity production is maintained, but consistency in final product quality deteriorates

Engineering Contradiction:

Improveflexibility in low-quantity productionVSAvoidconsistency in final product quality

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The system captures images at each processing station, uses machine learning models to predict final quality metrics, and provides real-time feedback to adjust processing parameters. This closed-loop feedback mechanism ensures consistent quality while maintaining flexibility for low-quantity production runs.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces human operators with an automated system comprising image capture devices, machine learning models (LSTM and GRU), and dynamic parameter adjustment mechanisms. This substitution eliminates human variability while maintaining adaptability through software-based control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If standard process control is used, then ease of operation is maintained, but manufacturing precision deteriorates

Engineering Contradiction:

Improveease of operationVSAvoidquality metric consistency

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The system performs self-monitoring and self-correction by automatically capturing images, predicting quality metrics, and adjusting processing parameters without human intervention. This autonomous operation maintains ease of use while significantly improving quality consistency.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Real-time feedback from machine learning predictions enables automatic adjustment of processing parameters, eliminating the need for complex manual quality control processes while achieving superior precision.

Inventive Principle:

Principle #23Feedback

3Manufacturing precision

If destructive testing is used to ensure quality, then manufacturing precision is improved, but productivity deteriorates

Engineering Contradiction:

Improvequality verification accuracyVSAvoidproduction efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system performs preliminary quality assessment during the manufacturing process using machine learning predictions, rather than relying on post-production destructive testing. This allows for early detection and correction of quality issues, maintaining high precision while preserving productivity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces destructive physical testing with non-destructive image-based machine learning analysis, enabling quality verification without compromising the product and eliminating the need to halt production for testing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Manufacturing precision

If real-time quality control is implemented, then manufacturing precision is improved, but device complexity increases

Engineering Contradiction:

Improvereal-time quality control accuracyVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system uses multi-functional components that perform multiple tasks: image capture devices monitor quality while providing data for machine learning training, and the same infrastructure supports both quality control and process optimization, reducing overall system complexity despite enhanced capabilities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250093853A1Systems, Methods, and Media for Manufacturing Processes

Publication Date: 2025.03.20 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.