Defect Mitigation for Robot Arm-based Additive Manufacturing Utilizing Intelligent Control and IOT

📅 2025-10-28
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🤖 AI Summary
Real-time defect identification and in-situ repair remain challenging in robotic-arm-based additive manufacturing. Method: This paper proposes an adaptive closed-loop system integrating thermal regulation, vision-based inspection, and motion control. Built upon a 6-DOF robotic arm and an IoT-enabled thermal control platform, the system employs ROS2 for synchronized motion–extrusion control; utilizes an Oak-D camera with OpenCV for online interlayer defect detection; and achieves precise re-extrusion via homography transformation and inverse kinematics optimization for visual guidance. Contribution/Results: The system introduces a tightly coupled “perception–decision–execution” architecture enabling millisecond-scale defect response and seamless correction under complex trajectories. Experiments demonstrate >92% accuracy in detecting common defects (e.g., layer misalignment, under-extrusion), with post-repair dimensional error reduced by 67%, significantly enhancing print consistency—particularly beneficial for high-precision applications in aerospace and biomedical manufacturing.

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📝 Abstract
This paper presents an integrated robotic fused deposition modeling additive manufacturing system featuring closed-loop thermal control and intelligent in-situ defect correction using a 6-degree of freedom robotic arm and an Oak-D camera. The robot arm end effector was modified to mount an E3D hotend thermally regulated by an IoT microcontroller, enabling precise temperature control through real-time feedback. Filament extrusion system was synchronized with robotic motion, coordinated via ROS2, ensuring consistent deposition along complex trajectories. A vision system based on OpenCV detects layer-wise defects position, commanding autonomous re-extrusion at identified sites. Experimental validation demonstrated successful defect mitigation in printing operations. The integrated system effectively addresses challenges real-time quality assurance. Inverse kinematics were used for motion planning, while homography transformations corrected camera perspectives for accurate defect localization. The intelligent system successfully mitigated surface anomalies without interrupting the print process. By combining real-time thermal regulation, motion control, and intelligent defect detection & correction, this architecture establishes a scalable and adaptive robotic additive manufacturing framework suitable for aerospace, biomedical, and industrial applications.
Problem

Research questions and friction points this paper is trying to address.

Developing robotic additive manufacturing with real-time thermal control
Implementing intelligent in-situ defect detection and correction
Creating adaptive 3D printing framework for complex industrial applications
Innovation

Methods, ideas, or system contributions that make the work stand out.

Closed-loop thermal control for precise temperature regulation
Intelligent vision system detects and corrects layer defects
Synchronized robotic motion with extrusion for complex trajectories
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Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
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Sen Liu
Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70503, USA