Crawfish Peeling Mechanism for Variable Size and Tail Curling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated crawfish peeling devices fail to account for inconsistent shapes and sizes of crawfish and the tight curling of tails after cooking, leading to inefficiencies and concerns over quality and safety, while relying heavily on human labor for processing.
Innovation Solution
A machine vision system with a machine-learning algorithm is developed to monitor and adjust for crawfish size and orientation, combined with a mechanical system to separate heads from tails and extract meat, using a clamping, cutting, and extraction subsystems driven by a single actuator and tunable cam profile, with integrated cameras for guidance and performance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated peeling devices are used, then labor intensity is reduced, but the devices fail to account for inconsistent shapes and sizes of crawfish leading to processing inefficiencies
Solution Approach 1:
The device employs a cam mechanism that dynamically adjusts the peeling force and angle based on the position of the crawfish tail in the peeling zone. This dynamic adjustment allows the automated device to adapt to inconsistent crawfish shapes and sizes, maintaining high processing efficiency while reducing labor intensity.
Solution Approach 2:
The device incorporates sensors that detect the position, size, and shape of crawfish as they enter the peeling zone. This feedback information is used to adjust the peeling parameters in real-time, ensuring consistent processing across varying crawfish dimensions while maintaining automated operation.
2Ease of operation
If automated peeling devices are used, then human labor is reduced, but the devices cannot handle tight curling of tails after cooking resulting in quality and safety concerns
Solution Approach 1:
The cam mechanism dynamically adjusts the peeling force and angle based on the position of the crawfish tail, enabling the device to handle tight curling tails effectively. This dynamic adjustment ensures complete and safe peeling while maintaining automated operation, eliminating quality and safety concerns associated with manual peeling of cooked crawfish.
Solution Approach 2:
The device performs peeling action immediately after cooking while the tail is still pliable, before it fully curls tight. This preliminary action ensures complete peeling and removes the tail tip cleanly, preventing quality and safety issues that would arise from attempting to peel fully cooked, tightly curled tails.
3Device complexity
If conventional peeling methods are used, then simple processing is maintained, but 7 pounds of crawfish shells are needed to produce 1 pound of meat indicating low yield
Solution Approach 1:
The cam mechanism dynamically optimizes the peeling force and angle to maximize meat extraction from each crawfish tail. This dynamic adjustment ensures complete removal of meat while minimizing shell waste, improving the yield ratio from approximately 1:7 to a more efficient proportion while maintaining relatively simple processing.
Solution Approach 2:
The device adjusts peeling parameters such as force magnitude, angle, and duration based on the specific characteristics of each crawfish tail. These parameter changes optimize the extraction of meat from the shell, improving yield while keeping the processing method relatively simple and automated.
Data Source
AI summary
Disclosed herein is a system and method for processing crawfish, specifically peeling crawfish shells. The device comprises a meat extraction portion, further comprising a clamping subsystem, cutting subsystem, and extraction subsystem designed to extract the crawfish meat from the shell without human interference. In a preferred embodiment of the device, the device further comprises a head/tail separation portion designed to decapitate the crawfish without human interference. The device further incorporates a single-board computer comprising a machine-learning system and related image database designed for improved identification of the crawfish shell components by the device, resulting in greater accuracy in processing individual crawfish of varying sizes.