Knit Data Grading via Characteristic Point Interpolation
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Solution Overview
Problem
The existing methods for converting pattern data to knit data for knitted products, especially for flechage knitting, are complex and require significant manual effort and trial-and-error, making it difficult to generate knit data for intermediate sizes without corresponding pattern data.
Innovation Solution
A grading method and system that converts graphical pattern data of knitted products into knit data for driving knitting machines, generating knit data for intermediate sizes by interpolating or extrapolating characteristic points and intermediate shapes from two known sizes, allowing for the allocation of knitted stitches within specified patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pattern data is converted to knit data for each size through trial and error, then shoe uppers can fit the pattern data, but the workload increases significantly
Solution Approach 1:
The patent applies preliminary action by creating a master knit data file from pattern data before actual production. This master file serves as a template that can be reused and adjusted for different sizes, eliminating the need to perform trial-and-error conversions for each size individually. The key steps include: generating master knit data from pattern data, creating a master size file with characteristic points, and then using these masters to generate size-specific knit data through automated adjustments rather than repeated trial-and-error processes.
Solution Approach 2:
The patent applies copying by creating master templates (master knit data and master size files) that can be copied and adapted for multiple sizes. Instead of converting pattern data to knit data separately for each size, the system creates one master version and then generates size variations by copying and adjusting the master file. This significantly reduces the conversion workload while maintaining fitting accuracy across all sizes.
2Manufacturing precision
If knit data is revised through trial and error for various sizes, then fitting accuracy improves, but time consumption increases
Solution Approach 1:
The patent performs the complex trial-and-error revision process in advance by creating a master knit data file that encapsulates the correct fitting parameters. Once this master file is established through the necessary revisions, it serves as a time-saving template for all subsequent size productions. The master size file with characteristic points is created beforehand, enabling rapid generation of size-specific knit data without repeating the time-consuming revision process.
Solution Approach 2:
The master knit data and master size files serve multiple functions: they can be used to generate knit data for any size, they encapsulate the fitting accuracy achieved through trial and error, and they can be stored for future reuse. This universal template approach eliminates the need to repeat the time-consuming revision process for each size, making the system efficient for producing multiple sizes of shoe uppers.
3Adaptability or versatility
If pattern data for each size is available, then grading can be performed, but it becomes difficult when pattern data is not available for intermediate sizes
Solution Approach 1:
The patent introduces master size files with characteristic points as an intermediary between pattern data and size-specific knit data. These master files contain key geometric information (characteristic points) that serve as a bridge, allowing the system to generate knit data for intermediate sizes even when original pattern data for those sizes doesn't exist. The characteristic points act as mediators that enable interpolation and extrapolation to create accurate knit data for sizes that weren't originally designed.
Solution Approach 2:
The system performs preliminary action by creating master size files that contain characteristic points representing the essential geometry of the design. These master files are prepared in advance and can be used to generate knit data for any size through automated processes. This preliminary creation of master templates eliminates the need to have original pattern data for every intermediate size, as the master files contain all the necessary information to derive size-specific knit data.
Data Source
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AI summary
Pattern data for at least two sizes are converted to knit data, regarding knitted products to be graded. Regarding the knit data for at least two sizes, characteristic points specifying shapes of the knitted products and intermediate shapes specifying shapes of the knitted products between the characteristic points are generated. By interpolating or extrapolating the characteristic points and the intermediate shapes, according to a desired size of the knitted products, characteristic points and intermediate shapes for the desired size are generated. Closed loops are generated by connecting the characteristic points and the intermediate shapes, and knit data for the desired size is generated by allocating knitted stitches within patterns specified by the closed loops. Without pattern data for intermediate sizes, knit data for the intermediate sizes are generated from the two size knit data.