Automated Optical Surface Roughness Measurement With Machine Learning
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Solution Overview
Problem
Existing quality control procedures in manufacturing are labor-intensive, costly, and subject to human judgment, making them time-consuming and inconsistent.
Innovation Solution
A surface roughness measurement system utilizing an imaging system, coherent light source, light sensor, and processor with trained machine learning models to automate the measurement of surface roughness, depth, and material identification, enabling accurate and reliable quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality control procedures are used, then human judgment and experience can identify defects, but the process becomes labor-intensive, time-consuming, and subject to human error
Solution Approach 1:
The patent replaces manual visual inspection and mechanical roughness gauges with an automated optical imaging system. The system uses a camera to capture surface images, processes them through machine learning models for depth perception and roughness analysis, eliminating the need for human technicians to perform manual measurements and visual inspections.
Solution Approach 2:
The system enables self-service quality control by using machine learning models that automatically analyze surface images and determine roughness values without human intervention. The trained models independently perform depth perception, material identification, and roughness measurement, making the system autonomous and eliminating dependency on human operators.
2Adaptability or versatility
If multiple technicians perform quality control, then diverse expertise can be utilized, but consistency and uniformity of quality standards become difficult to maintain
Solution Approach 1:
The patent applies homogeneity by using a standardized machine learning-based measurement system that treats all surfaces uniformly. The same algorithms and criteria are applied consistently to every measurement, eliminating variations in judgment between different technicians and ensuring uniform quality standards across all inspections.
3Measurement precision
If traditional roughness gauges are used, then direct surface roughness measurement is achieved, but the process requires significant training and experience to operate accurately
Solution Approach 1:
The patent replaces complex mechanical roughness gauges that require skilled operation with an automated optical system. The complexity of operation is transferred to the machine learning models, which automatically perform the analysis without requiring human expertise in interpretation or technique.
4Productivity
If automated systems are implemented, then productivity and consistency are improved, but the system complexity and initial cost increase
Solution Approach 1:
The patent achieves multi-functionality by using a single imaging system that performs multiple tasks: capturing surface images, generating depth maps through machine learning, identifying materials, and measuring roughness. This consolidates what would traditionally require multiple separate devices and processes into one unified system, reducing overall complexity while maintaining high productivity.
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, accurate, and reliable surface roughness measurement, reducing human error and ensuring uniform quality control processes across various materials.
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
Implementation Method 2
detect light from the coherent light source that is reflected by the target surface
Data Source
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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.