Packaging Sketch Segmentation for Rapid Box Prototyping
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Solution Overview
Problem
The existing systems for packaging design are time-consuming, especially when creating simple boxes, as they require manual input and specialized CAD software, which can be inefficient for rapid prototyping and design.
Innovation Solution
A machine learning system utilizing a scanner, convolutional neural network, and controller to convert hand-drawn packaging sketches into pixelated images, segmenting fold lines and cut lines, and transforming them into control commands for folding and cutting machines, enabling rapid packaging prototyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional CAD software is used for packaging design, then design precision can be maintained, but design time increases significantly
Solution Approach 1:
The patent replaces manual CAD operations with an automated machine learning system that uses image recognition to detect sketch lines and automatically generates packaging designs, eliminating the time-consuming manual drawing process while maintaining design precision through algorithmic line detection and interpretation
Solution Approach 2:
The system enables self-service packaging design by allowing users to simply sketch their ideas on paper or digital surfaces, which are then automatically converted into precise packaging designs by the ML model without requiring professional CAD skills or extensive manual intervention
2Manufacturing precision
If specialized CAD software is used for packaging design, then design accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces complex CAD software operations with simple sketching actions that the ML system automatically interprets, converting difficult technical drawing tasks into intuitive freehand sketching that anyone can perform while maintaining professional-grade design accuracy
Solution Approach 2:
The system creates a digital copy of the user's hand sketch and automatically transforms it into a precise packaging design, allowing users to work with simple visual copies of their ideas rather than requiring them to master complex CAD tools
3Productivity
If automated machine learning systems are used for packaging design, then productivity increases, but device complexity increases
Solution Approach 1:
The patent segments the packaging design process into distinct stages: sketch input, image scanning, line detection, design interpretation, and manufacturing output. This modular approach enables high productivity through automation while managing system complexity by breaking down the ML system into separate functional components that can be independently optimized
Data Source
AI summary
A system includes a scanner to convert a packaging sketch into a pixelated image and a convolutional neural network configured to segment the pixelated image into bounded objects including fold lines and cut lines. A controller is configured to transform the fold lines and the cut lines into control commands to a folding machine and a cutting machine. A method includes converting a packaging sketch into a pixelated image using a scanner and segmenting, using a convolutional neural network, the pixelated image into bounded objects including fold lines and cut lines. The method also includes transforming, using a controller, the fold lines and the cut lines into control commands to a folding machine and a cutting machine.