Swarm Mobile Agents for Multi-Material Manufacturing Throughput
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Solution Overview
Problem
Traditional gantry style manufacturing equipment is stationary, restricts movement, and requires manual swapping of end effectors, leading to inefficiencies and increased manufacturing time, especially in additive manufacturing processes.
Innovation Solution
A collaborative system of mobile agents using swarm manufacturing techniques and machine learning, where multiple mobile agents with detachable components and wireless communication work together to optimize manufacturing processes, allowing for simultaneous multi-material deposition and efficient fabrication of complex parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional gantry style manufacturing equipment is used, then structural stability is maintained, but manufacturing time increases and productivity decreases
Solution Approach 1:
The manufacturing system is divided into multiple independent mobile agents instead of a single stationary gantry system. Each agent can independently traverse and manufacture different portions of the end product simultaneously, transforming one complex stationary system into multiple simpler mobile units that work in parallel.
Solution Approach 2:
The equipment transitions from a stationary gantry structure to mobile agents that can dynamically move throughout the manufacturing area. This mobility allows agents to access different locations and adapt their positions based on manufacturing requirements, improving productivity without requiring complex fixed infrastructure.
2Productivity
If manual swapping of end effectors is performed, then tooling flexibility is achieved, but manufacturing time increases
Solution Approach 1:
Each mobile agent is equipped with its own end effector that can be independently selected and configured for specific manufacturing tasks. The agents autonomously perform their manufacturing functions without requiring manual intervention for tooling changes, eliminating downtime associated with manual effector swapping.
Solution Approach 2:
The mobile agents are designed with versatile end effectors capable of performing multiple manufacturing operations. This multi-functionality allows a single agent to handle various tooling requirements throughout the manufacturing process, reducing the need for frequent tooling changes and improving operational efficiency.
3Ease of operation
If large support columns and gantry equipment are used, then structural stability is ensured, but movement freedom of SCARA arms is restricted
Solution Approach 1:
The large support columns and fixed gantry infrastructure are completely removed from the system. Instead of relying on heavy stationary structures, the patent uses mobile agents that carry their own support and manufacturing capabilities, freeing SCARA arms from spatial constraints and allowing unrestricted movement within the manufacturing area.
Solution Approach 2:
The system transitions from a two-dimensional planar manufacturing area constrained by gantry rails to a three-dimensional mobile workspace. Agents can move freely in multiple dimensions, allowing SCARA arms to operate without the restrictions of fixed gantry paths and achieve greater movement freedom.
Data Source
AI summary
Embodiments of the present invention include a collaborative manufacturing system utilizing a plurality of mobile agents. The mobile agents operate dual robotic arms to improve single and multi-material builds' efficiency. In some embodiments, the dual robotic arms work together in the same area to create multi-functional components. In addition, the mobile agents can change tool heads on the arms to allow for hybrid manufacturing such as pick and place, additive, and subtractive manufacturing. One or more mobile agents interact with other mobile agents, thereby increasing the end product's efficiency and quality. Mobile agents utilize swarm manufacturing techniques to improve the manufactured product's time efficiency further and use machine learning to adjust and re-assign mobile agents constantly.