Automated Workpiece Transfer With ML-Guided Pickability Control
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Solution Overview
Problem
Existing automated workpiece transfer systems face challenges in efficiently sorting and diverting workpieces of varying sizes, shapes, and materials, leading to reduced pickability and increased processing times.
Innovation Solution
The system employs an autonomous robot equipped with a transfer component, actuator components, and an end-of-arm-tooling component, in conjunction with a processor that applies a machine-learning model to identify workpiece types and define operating commands to increase pickability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feeders are used to transfer workpieces, then workpiece transfer can be achieved, but the number of pickable workpieces is reduced and processing time increases
Solution Approach 1:
The system dynamically adjusts actuator operating commands based on real-time image analysis of workpiece density and orientation. The processor continuously monitors the pre-processing area and modifies actuator commands to optimize workpiece positioning, enabling the system to adapt to varying workpiece configurations and maximize pickability while maintaining high transfer speeds
Solution Approach 2:
The system implements a feedback loop where images of the pre-processing area are captured, analyzed by the processor to determine workpiece density and orientation, and used to generate optimized actuator commands. This closed-loop control ensures that the feeder continuously adjusts its operation to maintain optimal pickability and processing efficiency
2Manufacturing precision
If feeders sort and divert individual workpieces, then workpiece positioning can be achieved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical sorting and positioning mechanisms with an image-based control system. The processor uses image analysis to identify workpiece locations and orientations, then sends commands to actuators to position only the necessary workpieces, eliminating the need for intricate mechanical sorting structures while maintaining high positioning accuracy
Solution Approach 2:
The feeder system is designed to handle multiple workpiece types and configurations using a single integrated platform. The image-based control system can adapt to different workpiece densities, orientations, and positions, allowing the same device to perform various sorting and positioning tasks without requiring complex mechanical reconfiguration
Data Source
AI summary
Automated workpiece transfer systems and methods of implementing thereof are disclosed. The system includes an autonomous robot and a processor in communication with the robot. The autonomous robot includes a transfer component operable to transfer the workpieces to a picking area, one or more actuator components operable to increase a number of pickable workpieces transferred to the picking area according to a set of actuator operating commands; and an end-of-arm-tooling component operable to retrieve pickable workpieces from the picking area and to transfer the pickable workpieces to a receiving area. The processor is operable to apply a workpiece identification machine-learning model to an image to identify a reference workpiece type associated with the workpieces shown within the image; identify a set of reference operating commands associated with the reference workpiece type; and define the set of actuator operating commands based on the image and the set of reference operating commands.