AI Parameter Determination for Sheet Processing Control
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Solution Overview
Problem
Conventional image forming apparatuses determine control parameters by secondary calculation or reference to pre-defined tables, leading to errors and suboptimal settings due to stepwise variations, making it difficult to achieve high-quality printing on diverse sheets.
Innovation Solution
A parameter determination apparatus using a hardware processor that acquires multiple sheet physical properties and determines processing parameters through a program incorporating artificial intelligence or statistical methods, directly from detection data without secondary calculations or table references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If control parameters are determined by secondary calculation from multiple sheet physical property values or by referencing pre-defined tables, then the determination process is simplified and can be implemented with conventional methods, but errors easily occur in the calculated values and optimal control parameter values cannot be determined due to stepwise variations
Solution Approach 1:
The patent replaces conventional mechanical calculation methods and table-based lookup systems with an artificial intelligence-based determination model. The hardware processor executes a program that uses AI/ML algorithms to directly determine control parameters from sheet physical property values, eliminating the need for secondary calculations and pre-defined tables. This substitution enables continuous, precise parameter determination without stepwise variations while maintaining implementation feasibility through programmed systems.
2Ease of operation
If a table prepared in advance is used to determine control parameters, then the determination process is straightforward and can be implemented conventionally, but the control parameter value varies stepwise for each predetermined section and optimal values cannot be determined
Solution Approach 1:
The patent transitions from a static table-based system to a dynamic AI-driven determination model. The hardware processor executes a program that continuously adapts control parameter determination based on actual sheet physical property values, enabling smooth, continuous parameter adjustment rather than stepwise changes. This dynamic approach maintains operational simplicity through automated processing while achieving precise, optimal parameter values tailored to each specific sheet.
Solution Approach 2:
The patent changes the fundamental parameter determination approach from discrete table lookup to continuous AI-based calculation. The determination model processes sheet physical property values through learned relationships to generate continuous, optimized control parameters, eliminating the stepwise variations inherent in table-based systems while maintaining ease of operation through automated program execution.
3Adaptability or versatility
If secondary calculation is used to obtain sheet physical property values from multiple detection data, then the system can utilize multiple sensor inputs, but errors easily occur in the calculated values
Solution Approach 1:
The patent introduces an AI-based determination model as an intermediary between multiple sensor inputs and control parameter determination. Instead of directly calculating sheet physical property values from multiple detection data sources, the determination model processes these inputs through learned relationships, filtering and integrating the data to produce accurate results without error-prone secondary calculations. This intermediary approach maintains versatility in utilizing multiple sensors while significantly improving measurement precision.
Data Source
AI summary
A parameter determination apparatus includes: a hardware processor that: acquires a value related to a plurality of types of sheet physical properties; and determines a parameter related to sheet processing from an acquired value related to the plurality of types of sheet physical properties, based on a program using at least any of a learning function including artificial intelligence or a statistical method.