Indirect Temperature Measurement in High Frequency Heating Systems

3D printing of multi-unit gastro-retentive tablets for the pulsatile release of artesunate

Pulsatile drug delivery is hardly achieved by conventional gastro-retentive dosage forms. Artesunate as a typical anti-malaria medicine needs oral pulsatile release. Here, artesunate-loaded pulsatile-release multi-unit gastro-retentive tablets (APGTs) were prepared with a semi-solid extrusion three-dimensional (3D) printing method. An APGT was composed of three units: artesunate-loaded immediate and delayed release units and a block unit. The matrix of the immediate/delayed release units consisted of polyvinylpyrrolidone (PVP) K30 and croscarmellose sodium, which improved the rapid release of artesunate when contacting water. The block unit consisted of octadecanol, hydroxypropyl methyl cellulose K15M, PVP K30, and poloxamer F68. APGTs showed multi-phase release in simulated gastric liquids (SGLs). The first immediate release phase continued for 1 h followed by a long block phase for 7 h. The second rapid release phase was initiated when the eroded holes in the block unit extended to the inner delayed release unit, and this phase continued for about 14 h. Low-density APGTs could ensure their long-term floating in the stomach. Oral APGTs remained in the rabbit stomach for about 20 h. 3D printing provides a new strategy for the preparation of oral pulsatile-release tablets.

Mathematical Model of the Layer-by-Layer FFF/FGF Polymer Extrusion Process for Use in the Algorithm of Numerical Implementation of Real-Time Thermal Cycle Control

An approach for improving and maintaining a consistent weld quality of the deposited material during the FFF printing process is proposed. The approach is based on the analysis of the printing process thermal cycle and the real-time nozzle temperature control. The mathematical model of the FFF printing process has been developed with the use of real-time control in the algorithm of numerical implementation. The successful solution of the thermal conductivity problem made it possible to determine segment-wise heating settings for use during the printing process, resulting in a high and stable quality of welding. Comparison of the results of modeling with other well-known mathematical models of the FFF printing process and experimental results showed the adequacy of the proposed model. A maximum deviation of 17.7% between the simulation results and the thermography data was observed. The proposed model was verified using rectangular 3D polylactide shapes printed with and without regulation of the power of the heat source according to the previously estimated settings. The overall quality of regulation, stability of the system, and the PI coefficients of the controller were evaluated using a simulated model of the control system. The results of the experiment fully correspond with the modeling results.

A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition

Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).