Thermal Spray Feedback Control Using Acoustic and Image Signals
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Solution Overview
Problem
Conventional thermal spray systems face challenges in accurately controlling coating quality due to variations in process parameters and equipment degradation, leading to inconsistent coating characteristics and increased costs from re-coating and operator intervention.
Innovation Solution
A system utilizing acoustic and image sensors to generate time-dependent data signals, which are transformed into frequency-domain spectra and analyzed using machine learning to determine relationships between control parameters and process outputs, enabling real-time adjustment of thermal spray system parameters to maintain predetermined coating specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional thermal spray systems operate without real-time monitoring and control, then the system complexity is low, but the coating quality consistency deteriorates due to process parameter variations and equipment degradation
Solution Approach 1:
The patent implements real-time feedback control by monitoring acoustic emissions and optical signals during thermal spray processing. The system continuously compares actual process parameters against target values and automatically adjusts spray parameters to maintain coating quality consistency, resolving the contradiction between manufacturing precision and device complexity through intelligent closed-loop control.
Solution Approach 2:
The patent replaces conventional mechanical and manual monitoring methods with acoustic sensing and optical detection systems. By using acoustic emissions analysis and optical signal processing to detect process variations, the system achieves precise coating quality control without requiring complex mechanical intervention mechanisms, thereby improving manufacturing precision while managing device complexity.
2Manufacturing precision
If real-time control systems with multiple sensors are implemented, then coating quality consistency improves, but the cost of equipment and operation increases
Solution Approach 1:
The patent employs acoustic sensors and optical detectors that serve multiple functions: monitoring spray plume characteristics, detecting coating deposition quality, identifying process anomalies, and providing feedback for control adjustments. This multi-functionality reduces the need for separate specialized devices, thereby improving coating quality consistency while managing equipment costs through versatile sensor utilization.
Solution Approach 2:
The system incorporates automated analysis and control capabilities that reduce the need for manual inspection and operator intervention. By using machine learning algorithms to analyze acoustic and optical signals, the system automatically detects quality issues and adjusts parameters, improving coating consistency while reducing operational costs associated with manual quality control.
3Productivity
If manual inspection and operator intervention are used to control coating quality, then the system complexity is low, but productivity decreases due to re-coating operations and operator involvement
Solution Approach 1:
The real-time feedback system continuously monitors coating deposition through acoustic and optical signals, enabling immediate detection of quality deviations. This allows the system to automatically correct process parameters before defects occur, eliminating the need for manual inspection and re-coating operations, thereby improving productivity while managing system complexity through automated control.
Solution Approach 2:
The patent replaces manual inspection and operator intervention with automated acoustic and optical monitoring systems. By using signal processing and pattern recognition to detect coating quality in real-time, the system eliminates productivity losses from manual quality checks and re-coating operations, achieving higher productivity through intelligent automation.
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
This approach provides accurate, real-time control of thermal spray systems, reducing variations in coating quality, minimizing re-coating needs, and decreasing operator intervention, thereby enhancing efficiency and reducing costs.
Implementation Method 1
at least one acoustic sensor configured to generate at least one time-dependent acoustic data signal indicative of sound generated by a thermal spray system
Implementation Method 2
at least one optical sensor configured to generate at least one image data signal indicative of the thermal spray system performing the process
Data Source
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AI summary
An example system includes at least one acoustic sensor and one optical sensor to monitor a thermal spray system controlled by a plurality of control parameters and performing a process associated with a plurality of process outputs. The system includes a computing device including a machine learning module and a control module. The machine learning module is configured to determine, based on at least the plurality of control parameters, an at least one time-dependent acoustic data signal, an at least one image data signal, and the plurality of process outputs, a relationship between the plurality of control parameters and the plurality of process outputs by machine learning. The control module is configured to control the thermal spray system to adjust the plurality of process outputs toward a plurality of respective operating ranges.