Veneer Sorting Control Using Defect Simulation for Grade Tuning
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Solution Overview
Problem
Existing systems for sorting veneers into quality ranks based on defects require repetitive adjustments of determination parameters, leading to inefficiencies and prolonged sorting times, as the number of veneers sorted into each grade varies significantly with different parameter settings, even when defects are similar.
Innovation Solution
A system that analyzes veneer image data to detect defects, determines quality ranks based on set conditions, totals the number of veneers in each rank, and displays the results on a screen, allowing for rapid adjustment of sorting conditions to optimize the distribution of veneers into face, back, and core veneers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If strict condition values are set for determination parameters, then the number of veneers sorted into high-quality grade decreases, but the sorting time increases due to repetitive adjustments
Solution Approach 1:
The system performs preliminary analysis of historical veneer image data to pre-determine optimal determination parameter values before actual sorting begins. This preliminary action establishes accurate sorting criteria in advance, eliminating the need for repetitive adjustments during production and reducing sorting time while maintaining high-quality grade accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically adjust determination parameters based on real-time sorting results and quality metrics. This closed-loop control enables the system to self-optimize parameter settings without manual intervention, resolving the contradiction between maintaining strict quality standards and minimizing adjustment time.
2Productivity
If loose condition values are set for determination parameters, then the number of veneers sorted into high-quality grade increases, but the sorting precision decreases
Solution Approach 1:
The system dynamically changes determination parameter values based on the specific characteristics of the veneer batch being processed. By analyzing image data and adjusting parameters such as defect threshold values, size criteria, and pattern recognition sensitivity, the system optimizes the balance between productivity and precision for each sorting scenario.
Solution Approach 2:
The sorting system transitions from static, fixed parameter settings to dynamic, adaptive parameter adjustment. The determination parameters are continuously optimized based on real-time analysis of veneer quality data, allowing the system to maximize high-quality output while maintaining accurate grade classification.
3Manufacturing precision
If repetitive manual adjustments of determination parameters are performed, then the sorting accuracy can be optimized, but the sorting efficiency decreases
Solution Approach 1:
The system performs self-service by automatically analyzing veneer image data and determining optimal determination parameter values without requiring manual intervention. The automated algorithm processes historical and real-time data to establish accurate sorting criteria, eliminating the need for operators to repeatedly adjust parameters and thereby maintaining both high accuracy and efficient throughput.
Solution Approach 2:
The system replaces manual mechanical adjustment of determination parameters with automated computational analysis. Image processing algorithms and data analysis software substitute for human operators, automatically optimizing sorting criteria based on visual defect detection and statistical analysis, thus maintaining precision while dramatically improving sorting efficiency.
Data Source
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AI summary
Provided is a veneer sorting control device including: a sorting condition setting unit 11 that sets sorting conditions for each of a plurality of kinds of defects so as to sort a veneer into a plurality of quality ranks; a defect detection unit 13 that detects the plurality of kinds of defects with respect to each of a plurality of pieces of veneer image data acquired from an image storage unit 100; a quality rank sorting unit 14 that sorts a plurality of the veneers into a plurality of quality ranks in correspondence with the sorting conditions which are set and defect detection states; a first totalization unit 15 that totalizes the number or a number ratio of the veneers in the plurality of quality ranks which are sorted; and a display control unit 17 that displays the totalization result on a screen. The number of the veneers sorted into the plurality of quality ranks can be confirmed by a simulation using the veneer image data stored in the image storage unit 100.