Machine Learning Feedback Engine for Consistent Manufacturing Inspection

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Solution Overview

Problem

Current manufacturing processes rely heavily on manual interventions, leading to variability and inefficiencies due to human involvement, necessitating frequent inspections and disparate systems for observation and analysis to improve quality, productivity, and reduce recall rates.

Innovation Solution

A feedback generation system that combines observation stations equipped with sensors and a computation engine using machine learning to analyze data, providing real-time feedback for process optimization across various phases of manufacturing, from product design to post-factory activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual operations are used in manufacturing, then human operators can perform tasks with flexibility, but variability and errors increase due to human nature

Engineering Contradiction:

Improvehuman operator flexibilityVSAvoidprocess consistency

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements continuous feedback loops where observation data from sensors is analyzed by machine learning models, and results are fed back to operators via user interfaces. This real-time feedback mechanism helps operators maintain consistency while preserving their flexibility to handle varied situations.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual inspection and analysis with automated observation stations equipped with sensors and machine learning systems. This substitution reduces human variability in measurement and analysis while maintaining operational flexibility through the intelligent system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If frequent inspections are conducted to ensure quality, then product quality improves, but productivity and throughput decrease due to time loss

Engineering Contradiction:

Improveproduct qualityVSAvoidthroughput

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The observation stations operate continuously without interrupting the manufacturing process. Sensors continuously collect data while the machine learning system continuously analyzes observations, enabling quality assurance to occur in parallel with production rather than as a sequential interruption.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system introduces an intermediary automated observation and analysis layer between the manufacturing process and quality decision-making. This intermediary handles inspection functions autonomously, eliminating the need for operators to stop production for manual inspections.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Extent of automation

If multiple disparate systems are used for observation and analysis, then specific functions can be performed, but system complexity increases and integration becomes difficult

Engineering Contradiction:

Improveobservation and analysis capabilityVSAvoidsystem integration

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent merges observation, data collection, analysis, and feedback delivery into a single integrated system. The observation station combines sensors, processing units, and user interface components that work together seamlessly, eliminating the need to integrate multiple separate systems.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The observation station is designed as a universal platform capable of performing multiple functions including data collection from various sensors, machine learning analysis, result visualization, and feedback delivery. This multi-functional design replaces multiple specialized systems with one versatile unit.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Adaptability or versatility

If manual analysis of inspection results is performed, then flexibility in decision-making is maintained, but time consumption and human error increase

Engineering Contradiction:

Improvedecision-making flexibilityVSAvoidanalysis time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The machine learning system performs preliminary analysis of observation data automatically and continuously, preparing insights before they are needed for decision-making. This preliminary processing eliminates the time required for manual analysis while presenting pre-digested information to operators for final decisions.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11348033B2Computational analysis of observations for determination of feedback

Publication Date: 2022.05.31 SRI INTERNATIONAL

AI summary

A system comprises one or more observation stations. Each observation station of the one or more observation stations comprises a corresponding set of one or more sensors. Additionally, the system comprises one or more physical machines that implement a computation engine configured to receive first observation data from the one or more observation stations. The computation engine may use the first observation data to train a machine learning system. The computation engine may subsequently use the trained machine learning system to provide feedback regarding an additional instance of the observation subject. The computation engine outputs the feedback.