Ceramic Glaze Mixing Control for Multi-Property Recipe Matching
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Solution Overview
Problem
Ceramic technicians face difficulties in determining if desired physical and chemical properties of a glaze can be achieved with available raw ingredients, as adjusting one ingredient affects all properties, making it tedious to find the optimal recipe.
Innovation Solution
A reverse calculation method using a solver engine that takes goal descriptors for glaze properties and available ingredients to automatically generate a recipe, allowing control over multiple variables and constraints, and indicating the feasibility of the solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a ceramic technician manually adjusts ingredient ratios to achieve desired glaze properties, then the ability to control multiple properties is improved, but the time and complexity of the process increases significantly
Solution Approach 1:
The patent replaces the manual mechanical process of trial-and-error recipe adjustment with an automated computer-based system. The solver engine uses algorithms to automatically calculate ingredient ratios based on desired glaze properties, substituting the technician's manual iterative adjustments with computational automation. This resolves the contradiction by maintaining precise control over multiple glaze properties while dramatically reducing the time required for recipe development.
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform the recipe calculation and optimization process. The solver engine takes desired property targets and automatically determines the ingredient composition without requiring continuous human intervention or manual trial-and-error experimentation. This automates the intellectually demanding aspect of glaze formulation, resolving the time loss contradiction.
2Manufacturing precision
If the number of available raw ingredients is expanded to achieve exact property matching, then the precision of glaze properties is improved, but the complexity of the recipe and mixing process increases
Solution Approach 1:
Instead of starting with available ingredients and trying to achieve desired properties (forward approach), the system inverts the process by starting with the desired property targets and working backward to determine the required ingredient composition. The solver engine performs reverse calculation from goal descriptors to ingredient ratios, allowing precise property matching while the computer optimizes the ingredient selection to avoid unnecessary complexity.
Solution Approach 2:
The system dynamically adjusts ingredient ratio parameters based on the desired property targets. The solver engine modifies the composition parameters iteratively to achieve the closest possible match to the goal properties, using computational optimization rather than manual parameter adjustment. This allows precise property matching while the system manages the complexity of multiple ingredients through automated calculation.
3Adaptability or versatility
If manual trial-and-error methods are used to determine feasible recipes, then flexibility in exploring different ingredient combinations is maintained, but the productivity and efficiency of the process decreases
Solution Approach 1:
The system implements feedback by using the solver engine to evaluate whether desired property targets are achievable with available ingredients, and to provide guidance on feasible recipe options. The computer analyzes the relationship between ingredient compositions and resulting glaze properties, providing feedback on what combinations will meet the goals. This maintains adaptability in exploring different combinations while dramatically improving productivity through automated evaluation rather than manual trial-and-error.
Solution Approach 2:
The system performs preliminary analysis by evaluating the feasibility of achieving desired properties before actual recipe development begins. The solver engine pre-calculates which ingredient combinations can meet the property targets, allowing the technician to focus only on feasible options. This preliminary computational work maintains versatility in exploring combinations while improving efficiency by eliminating futile trial-and-error attempts.
Data Source
AI summary
In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.