arXiv subjects

Xavier Guidetti

Publications and source records attributed to Xavier Guidetti.

8 recordsLinked to original sources

arXiv2025

One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication

Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between printhead motion and material extrusion systems, notably corner over-extrusion. Existing methods require specialized hardware, extensive calibration, or firmware modifications that are inaccessible to most users. This work presents a practical, end-to-end optimization framework that enhances high-speed printing using only standard 3D printers and a phone camera, without requiring additional complex setup. The method employs a one-shot calibration approach in which two simple printed patterns, captured by a phone camera, enable identification of extrusion dynamics and cornering behavior. The identified systems enable a model-based constrained optimal control strategy that generates optimized G-code, synchronizing motion and extrusion. Experiments show reduced width tracking error, mitigated corner defects, and lower surface roughness, achieving surface quality at 3600 mm/min comparable to conventional printing at 1600 mm/min, effectively doubling production speed while maintaining print quality. This accessible, hardware-minimal approach enables a wide range of fused filament fabrication users to achieve high-quality, high-speed additive manufacturing.

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arXiv2024

Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing

In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress produced in a loaded part. Agent trajectories are then used as print trajectories. With this approach, the complex global trajectory generation problem is subdivided into a set of sequential and computationally efficient quadratic programs. Through comprehensive evaluations in both simulation and experiments, we compare our method with state-of-the-art approaches. Our results highlight a remarkable improvement in computational efficiency, achieving a 115x faster computation speed than existing methods. This efficiency, combined with the possibility to tune the trajectories spacing to match the deposition process constraints, makes the potential integration of our approach into existing 3D printing processes seamless. Additionally, the open-hole tensile specimen produced on a conventional fused filament fabrication set-up with our algorithm achieve a notable ~10% improvement in specific modulus compared to existing trajectory optimization methods.

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arXiv2024

Data-Driven Extrusion Force Control Tuning for 3D Printing

The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned controllers resulting in inconsistent and suboptimal results. This work employs Bayesian optimization to identify the optimal controller parameters. Additionally, we explore transfer learning in the context of 3D printing by leveraging prior information from past trials. By integrating optimized extrusion force control and transfer learning, we provide a novel framework for closed-loop 3D printing and propose an automated calibration routine that produces high-quality prints for a desired combination of print settings, material, and shape.

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arXiv2024

Force Controlled Printing for Material Extrusion Additive Manufacturing

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing location is pre-computed by a planning software. This approach is inherently unable to adapt the extrusion to external and unexpected disturbances, and the quality of the results strongly depends on a number of modeling and tuning parameters. To overcome these limitations, we propose the first framework for Force Controlled Printing for material extrusion additive manufacturing. We utilize a custom-built extruder to measure the extrusion force in real time, and use this quantity as feedback to continuously control the material flow in closed-loop. We demonstrate the existence of a strong correlation between extrusion force and line width, which we exploit to deposit lines of desired width in a width range of 33 % up to 233 % of the nozzle diameter. We also show how Force Controlled Printing outperforms conventional feed-forward extrusion in print quality and disturbance rejection, while requiring little tuning and automatically adapting to changes in the hardware settings. With no adaptation, Force Controlled Printing can deposit lines of desired width under severe disturbances in bed leveling, such as at layer heights ranging between 20 % and 200 % of the nominal height.

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arXiv2023

Stress Flow Guided Non-Planar Print Trajectory Optimization for Additive Manufacturing of Anisotropic Polymers

When manufacturing parts using material extrusion additive manufacturing and anisotropic polymers, the mechanical properties of a manufactured component are strongly dependent on the print trajectory orientation. We conduct non-planar slicing and optimize the print trajectories to maximize the alignment between the material deposition direction and the stress flow induced by a predefined load case. The trajectory optimization framework considers manufacturability constraints in the form of uniform layer height and line spacing. We demonstrate the method by manufacturing a load bearing mechanical bracket using a 5-axis 3D printer and a liquid crystal polymer material. The failure strength and stiffness of the optimized bracket are improved by a factor of 44 and 6 respectively when compared with conventional printing.

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arXiv2022

Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements

The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We use a laser sensor to evaluate the surface roughness of a printed part. We then correlate the roughness to the mechanical properties of the part, and show how print quality affects mechanical performance. Finally, we use Bayesian optimization to search for optimal print parameters. We demonstrate our method by printing liquid crystal polymer samples, and successfully find parameters that produce high-performance prints and maximize the manufacturing process efficiency.

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arXiv2022

Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. \cmtb{The novel acquisition function is demonstrated, analyzed and compared on state-of-the-art benchmarking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling.} Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.

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arXiv2022

Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization

Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes. In complex manufacturing processes, the possibility to run extensive sequences of experiments with the goal of finding good process parameters is severely limited by the time required for quality evaluation of the produced parts. To accelerate the process parameter optimization, we introduce a parallel acquisition procedure tailored on the process characteristics. We further propose an algorithm that adapts to equipment status to improve run-to-run reproducibility. We validate our optimization method numerically and experimentally, and demonstrate that it can efficiently find input parameters that produce the desired outcome and minimize the process cost.

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