Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an active machine learning framework to optimize multi-photon polymerization 3D printing. The method significantly reduces experimental effort for achieving high dimensional accuracy in micro/nanoscale additive manufacturing.
Area Of Science
- Additive Manufacturing
- Machine Learning
- Optical Engineering
Background
- Multi-photon polymerization (MPP) is a key micro/nanoscale additive manufacturing technique.
- Optimizing process parameters for dimensional accuracy in MPP is challenging and time-consuming.
- Existing methods often require extensive experimental trials.
Purpose Of The Study
- To develop an active machine learning framework for optimizing process parameters in high-speed, continuous projection 3D printing.
- To reduce the experimental data collection effort required for achieving high geometric accuracy.
- To create a surrogate model for predicting optimal parameters for target geometries.
Main Methods
- Utilized an active learning framework with Bayesian optimization.
- Employed Gaussian-process-regression for the machine learning model.
- Tested the framework on three representative 2D shapes at different scales.
Main Results
- Achieved significant improvements in geometric accuracy.
- Reduced errors to within measurement accuracy.
- Demonstrated effectiveness in just four Bayesian optimization iterations with limited data.
Conclusions
- The active learning framework efficiently optimizes additive manufacturing processes.
- It drastically reduces experimental effort for parameter optimization.
- The approach is broadly applicable to various additive manufacturing techniques.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...