On-Site 3D Printed Robotic Assembly for Flexible Transport Structures

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Solution Overview

Problem

Traditional manufacturing facilities for vehicles and other transport structures are inflexible, leading to high costs and inefficiencies due to fixed infrastructure and the need for extensive tooling, limiting their ability to produce a variety of models without significant downtime and financial burden.

Innovation Solution

A flexible and modular robotic manufacturing system that includes automated constructors with 3-D printing capabilities, allowing for the assembly and reconfiguration of robotic assembly stations to produce different vehicle models without the need for extensive retooling, using a combination of 3-D printed and commercial off-the-shelf parts, and enabling machine-learning for optimized production processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional fixed robotic assembly systems are used to achieve efficient production at volume, then productivity is improved, but adaptability deteriorates due to inflexible factory infrastructure and extensive tooling requirements

Engineering Contradiction:
Improveproduction efficiencyVSAvoidmanufacturing flexibility
Core Design Contradiction:
ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent divides the manufacturing system into modular robotic assembly stations that can be independently configured and repositioned. Each station performs specific assembly tasks and can be relocated or reconfigured without affecting the entire factory infrastructure, enabling both high-volume production and model changes.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The system employs dynamic reconfigurability where robotic assembly stations can be programmatically repositioned and retooled between production runs. The factory infrastructure is designed to accommodate changes in layout and configuration, allowing the same facility to efficiently produce different transport structure models.

Inventive Principle:
Principle #15Dynamics

2Manufacturing precision

If factory infrastructure is permanently configured to produce specific models, then manufacturing precision is improved for those models, but adaptability deteriorates due to tooling amortization costs and retooling requirements

Engineering Contradiction:
Improveassembly precisionVSAvoidmodel change flexibility
Core Design Contradiction:
Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The robotic assembly stations are designed with universal end-effectors and programmable control systems that can be configured for different assembly tasks. The same robotic station can produce multiple transport structure models by changing software parameters and tooling attachments, eliminating the need for dedicated infrastructure per model.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system utilizes programmable parameters and software-controlled configurations to adapt robotic assembly stations for different models. By changing control parameters, tooling specifications, and assembly sequences through software rather than physical reconfiguration, the system maintains precision while enabling flexible model changes.

Inventive Principle:
Principle #35Parameter changes

3Manufacturing precision

If additive manufacturing is conducted at a dedicated location away from the assembly line, then manufacturing precision is improved for 3-D printed components, but adaptability deteriorates due to lack of flexibility to modify AM capabilities

Engineering Contradiction:
Improvecomponent printing precisionVSAvoidAM capability flexibility
Core Design Contradiction:
Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The patent integrates additive manufacturing capabilities directly within the robotic assembly stations. The 3-D printers are positioned at the assembly locations, allowing components to be printed and immediately assembled without transfer. This merging of functions enables both precise component manufacturing and flexible adaptation of AM capabilities at the point of use.

Inventive Principle:
Principle #5Merging (Combining)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables efficient and cost-effective production of various vehicle models by allowing for real-time reconfiguration of manufacturing processes, reducing downtime and tooling costs, and enhancing production flexibility and efficiency through automated and machine-learning enabled systems.

Implementation Method 1

a first one of the automated constructors includes a three-dimensional (3-D) printer to print at least a portion of a component

Methodology Applied
Scientific EffectAdditive manufacturing (3-D printing): 3D Printing

Data Source

PatentUS11358337B2Robotic assembly of transport structures using on-site additive manufacturing
Publication Date: 2022.06.14 DIVERGENT TECHNOLOGIES INC
  • US11358337B2 patent drawing
  • US11358337B2 patent drawing
  • US11358337B2 patent drawing

AI summary

Techniques for flexible, on-site additive manufacturing of components or portions thereof for transport structures are disclosed. An automated assembly system for a transport structure may include a plurality of automated constructors to assemble the transport structure. In one aspect, the assembly system may span the full vertically integrated production process, from powder production to recycling. At least some of the automated constructors are able to move in an automated fashion between the station under the guidance of a control system. A first of the automated constructors may include a 3-D printer to print at least a portion of a component and to transfer the component to a second one of the automated constructors for installation during the assembly of the transport structure. The automated constructors may also be adapted to perform a variety of different tasks utilizing sensors for enabling machine-learning.