Visual-Guided Bandsaw Portioning for Precise Bone-Aware Cuts
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Solution Overview
Problem
Existing meat cutting systems lack the ability to accurately analyze uncut meat and calculate optimal cutting depths for different cuts, leading to inefficiencies and inconsistencies in meat portioning.
Innovation Solution
An automated saw system equipped with visual sensors and a controller that analyzes uncut meat using visual sensors to locate bone and meat configurations, categorize cuts, and calculate cutting depths based on preferences, enabling precise meat portioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual meat cutting methods are used, then operational simplicity is maintained, but manufacturing precision and consistency of meat portioning deteriorate
Solution Approach 1:
The patent replaces manual mechanical cutting operations with an automated system that uses visual sensors to capture images of meat, processes these images through software algorithms to identify bone structures and meat portions, and controls a cutting mechanism to make precise cuts. This substitution of mechanical manual operations with optical-digital-control mechanisms resolves the contradiction by achieving high cutting precision while managing system complexity through modular architecture.
Solution Approach 2:
The system performs self-analysis of meat characteristics by automatically capturing images, identifying bone configurations and meat portions, calculating optimal cutting depths, and executing cuts without continuous human intervention. The controller autonomously processes visual data and adjusts cutting parameters, enabling the system to serve itself in the cutting process while maintaining high precision.
2Productivity
If automated cutting systems are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single automated platform: visual sensing for image capture, image processing for bone and meat identification, calculation algorithms for cutting depth determination, and mechanical control for execution. This multi-functional integration improves productivity by automating the entire portioning workflow while managing complexity through unified system architecture rather than separate independent systems.
Solution Approach 2:
The patent introduces an image processing software module as an intermediary between visual sensing and mechanical cutting. This intermediary layer analyzes captured images, identifies meat portions and bones, calculates optimal cutting parameters, and translates these into control signals for the cutting mechanism. This intermediary enables automated high-speed operation while isolating the complexity of decision-making algorithms from the mechanical execution system.
3Measurement precision
If visual sensors and image processing are added, then measurement precision of meat characteristics is improved, but device complexity increases
Solution Approach 1:
The patent replaces physical measurement devices with optical visual sensors that capture images of meat characteristics. Instead of using complex mechanical probes or contact-based measurement systems, the system uses non-contact image capture followed by digital image processing to identify bone structures, meat portions, and their spatial relationships. This substitution achieves high measurement precision while reducing mechanical complexity.
Solution Approach 2:
The system creates a digital copy of the meat's physical characteristics through image capture. The visual sensors generate digital representations of bone configurations and meat portions, which are then processed through software algorithms to extract measurement information. This copying approach enables precise measurement of complex geometries without requiring equally complex physical measurement devices.
Data Source
AI summary
An automated saw wherein: the automated saw comprises one or more visual sensors, a meat positioning assembly. The automated saw is configured to analyze an uncut meat and calculate one or more cutting depths for one or more cut portions from the uncut meat. The uncut meat comprises a first end and a second end. The first end of the uncut meat can be analyzed by the automated saw by: capturing a first slice image of the first end, locating a first bone configuration and a configuration of one or more meat portions in relation to the first bone configuration, and measuring portions of the one or more meat portions to categorize which among one or more cuts of meat is presented at the first end of the uncut meat. The automated saw calculates a first cutting depth for a first meat portion.