Optical Surface Roughness Measurement With Speckle Imaging

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Quality control procedures in manufacturing are labor-intensive, reliant on human judgment, and inconsistent, leading to inefficiencies and increased costs due to the need for trained personnel to manually inspect products for surface roughness.

Innovation Solution

An automated surface roughness measurement system utilizing an imaging system, coherent light source, light sensor, and trained machine learning models to determine surface roughness, depth, and material type, enabling accurate and reliable quality control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual inspection by technicians is used to measure surface roughness, then human judgment and experience can be applied to identify defects, but the process becomes labor-intensive, time-consuming, and inconsistent

Engineering Contradiction:

Improvequality control accuracyVSAvoidinspection speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical inspection with an automated optical measurement system that uses a camera to capture speckle patterns, a coherent light source to illuminate the surface, and a processor with machine learning models to automatically analyze the patterns and determine surface roughness values, eliminating the need for manual technician inspection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service quality control by using machine learning models that automatically analyze captured images and generate surface roughness measurements without requiring trained technicians to manually inspect each product, allowing the system to independently perform quality assessment

Inventive Principle:

Principle #25Self-service

2Measurement precision

If multiple trained technicians perform quality control inspections, then accurate defect identification is possible, but training costs and operational costs increase

Engineering Contradiction:

Improvesurface roughness measurement accuracyVSAvoidquality control system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces the complex human training and expertise system with a machine learning model that has been trained on surface roughness data, transferring the knowledge and measurement capability from human technicians to an automated computational system that can be deployed without extensive training programs

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the operational parameters from human cognitive processes to computational algorithms, using machine learning models that process image data and generate measurements based on learned patterns rather than human judgment, thereby standardizing the measurement process

Inventive Principle:

Principle #35Parameter changes

3Reliability

If manual quality control procedures are implemented, then surface roughness can be assessed, but the process becomes costly and time-consuming

Engineering Contradiction:

Improvequality control consistencyVSAvoidinspection time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The automated system enables continuous quality control inspection by capturing images and generating measurements without interruption, allowing multiple products to be inspected in sequence without the breaks, fatigue, and variability inherent in manual inspection processes

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent replaces time-consuming manual inspection procedures with rapid automated image capture and processing, where the camera quickly captures speckle patterns and the processor generates measurements in real-time, dramatically reducing the time required per inspection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The system allows for automated, efficient, and consistent quality control by accurately measuring surface roughness and material type, reducing human error and costs while ensuring uniformity across products.

Implementation Method 1

illuminate, with the coherent light source, an area of interest of the target surface to create a speckle light pattern on the area of interest

Methodology Applied

Scientific EffectSpeckle pattern:

Implementation Method 2

receive, from the light sensor, a waveform related to light reflected by the target surface

Methodology Applied

Scientific EffectPhotoelectric effect: Photoelectric Effect

Data Source

PatentUS12372349B2Surface roughness measurement system

Publication Date: 2025.07.29 EATON INTELLIGENT POWER LTD

AI summary

It is known for a product to be mass produced by way of a manufacturing process. Typically, a quality control step is used in a manufacturing process to monitor the quality of manufactured products. However, quality control procedures in manufacturing are typically labour intensive. A technician or other person must inspect the product and carry out any necessary tests. The present disclosure provides a surface roughness measurement system and method for determining a surface roughness of a product with an imaging system, a coherent light source, a light sensor and several trained machine learning algorithms.