Adaptive Coating Thickness Control via Iterative Spray Law Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-temperature mechanical systems, such as gas turbine engines, face challenges in maintaining geometric tolerances and controlling coating thickness due to process drift and variability in coating material properties, leading to inefficiencies and increased manufacturing costs.
Innovation Solution
A system and method that uses a computing device to measure and control the thickness of coatings on components by iteratively adjusting spray law parameters based on measured and simulated geometries, allowing for adaptive control of coating processes to achieve target thicknesses and correct for process drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional coating processes are used without adaptive control, then the coating process is simple to operate, but the coating thickness precision deteriorates due to process drift and material variability
Solution Approach 1:
The system implements feedback control by measuring the actual coating thickness after application, comparing it to the target thickness, and using this information to adjust spray law parameters for subsequent coating operations. This closed-loop feedback mechanism corrects for process drift and material property variations, maintaining coating thickness precision without requiring overly complex manual intervention
Solution Approach 2:
The system dynamically changes spray law parameters (such as spray rate, nozzle position, or material flow rate) based on measured coating thickness and simulated geometry comparisons. By adjusting these parameters iteratively, the system adapts to process drift and material variability, achieving precise coating thickness control while managing complexity through automated parameter optimization
2Manufacturing precision
If iterative adjustment of spray law parameters is implemented, then coating thickness control improves, but computational time and processing complexity increase
Solution Approach 1:
The system performs preliminary simulations of coating geometry using predicted spray law parameters before actual coating application. By pre-calculating expected coating outcomes and comparing them to target geometry, the system reduces iterative adjustments needed during actual coating operations, thereby improving thickness control while minimizing computational time loss
Solution Approach 2:
The system applies partial iterative adjustments by performing simulations and parameter optimizations only for critical coating zones or when threshold deviations are detected, rather than continuously adjusting all parameters throughout the entire coating process. This selective approach maintains coating thickness control while significantly reducing computational time and processing overhead
3Reliability
If adaptive control systems are used to correct process drift, then coating quality improves, but system complexity and initial costs increase
Solution Approach 1:
The adaptive control system performs self-calibration by automatically detecting process drift through geometry measurements and adjusting spray law parameters without external intervention. The system serves itself by identifying deviations from target coating thickness and autonomously correcting them, improving coating quality consistency while managing complexity through automated self-regulation rather than requiring complex external control infrastructure
Solution Approach 2:
The system creates a digital twin or virtual model of the coating process that simulates coating geometry and thickness based on spray law parameters. By copying and testing parameter adjustments in the virtual model before applying them to the physical coating process, the system validates changes safely, improving coating quality while managing complexity through virtual experimentation rather than requiring extensive physical trial-and-error equipment
Data Source
AI summary
An example method that includes receiving a first geometry of a component in an uncoated state and a second geometry of the component in a coated state; determining a first difference between the second geometry and a first simulated geometry based on the first geometry and a first spray law comprising a plurality of first spray law parameters; iteratively adjusting at least one first spray law parameter to determine a respective subsequent spray law; iteratively determining a respective subsequent difference between the second geometry and a subsequent simulated geometry based on the first geometry and the subsequent respective spray law; selecting a subsequent spray law from the respective subsequent spray laws based on the respective subsequent differences; and controlling a coating process based on the selected subsequent spray law.