3D bioprinted scaffolds for diabetic wound-healing applications

The treatment strategy required for the effective healing of diabetic foot ulcer (DFU) is a complex process that is requiring several combined therapeutic approaches. As a result, there is a significant clinical and economic burden associated in treating DFU. Furthermore, these treatments are often unsuccessful, commonly resulting in lower-limb amputation. The use of drug-loaded scaffolds to treat DFU has previously been investigated using electrospinning and fused deposition modelling (FDM) 3D printing techniques; however, the rapidly evolving field of bioprinting is creating new opportunities for innovation within this research area. In this study, 3D-bioprinted scaffolds with different designs have been fabricated for the delivery of an antibiotic (levoflocixin) to DFU. The scaffolds were fully characterised by a variety of techniques (e.g. SEM, DSC/TGA, FTIR, and mechanical characterisation), demonstrating excellent mechanical properties and providing sustained drug release for 4 weeks. This proof of concept study demonstrates the innovative potential of bioprinting technologies in fabrication of antibiotic scaffolds for the treatment of DFU. Graphical abstract

A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

Coupling of Fused Deposition Modeling and Inkjet Printing to Produce Drug Loaded 3D Printed Tablets

In the current study, we have coupled Fused Deposition Modelling (FDM) for the fabrication of plain polyvinyl alcohol (PVA) tablets followed by dispensing of minoxidil ethanolic solutions using inkjet printing. The use of a drop-on-solid printing approach facilitates an accurate and reproducible process while it controls the deposition of the drug amounts. For the purpose of the study, the effect of the solvent was investigated and minoxidil ink solutions of ethanol 70% v/v (P70) or absolute ethanol (P100) were applied on the plain PVA tablets. Physicochemical characterization showed that solvent miscibility with the polymer substrate plays a key role and can lead to the formation of drug crystals on the surface or drug absorption in the polymer matrix. The produced minoxidil tablets showed sustained release profiles or initial bursts strongly affected by the solvent grade used for dispensing the required dose on drug loaded 3D printed tablets. This paradigm demonstrates that the coupling of FDM and inkjet printing technologies could be used for rapid development of personalized dosage forms.

A computational fluid dynamics assessment of 3D printed ventilator splitters and restrictors for differential multi-patient ventilation

Background The global pandemic of novel coronavirus (SARS-CoV-2) has led to global shortages of ventilators and accessories. One solution to this problem is to split ventilators between multiple patients, which poses the difficulty of treating two patients with dissimilar ventilation needs. A proposed solution to this problem is the use of 3D-printed flow splitters and restrictors. There is little data available on the reliability of such devices and how the use of different 3D printing methods might affect their performance. Methods We performed flow resistance measurements on 30 different 3D-printed restrictor designs produced using a range of fused deposition modelling and stereolithography printers and materials, from consumer grade printers using polylactic acid filament to professional printers using surgical resin. We compared their performance to novel computational fluid dynamics models driven by empirical ventilator flow rate data. This indicates the ideal performance of a part that matches the computer model. Results The 3D-printed restrictors varied considerably between printers and materials to a sufficient degree that would make them unsafe for clinical use without individual testing. This occurs because the interior surface of the restrictor is rough and has a reduced nominal average diameter when compared to the computer model. However, we have also shown that with careful calibration it is possible to tune the end-inspiratory (tidal) volume by titrating the inspiratory time on the ventilator. Conclusions Computer simulations of differential multi patient ventilation indicate that the use of 3D-printed flow splitters is viable. However, in situ testing indicates that using 3D printers to produce flow restricting orifices is not recommended, as the flow resistance can deviate significantly from expected values depending on the type of printer used. Trial registration Not applicable.

Accuracy investigation of Fused Deposition Modelling (FDM) processed ABS and ULTRAT parts.

This paper aims to assess the dimensional deviation of Fused Deposition Modeling (FDM) processed ABS and ULTRAT parts using a new geometrical model which can evaluate three types of dimensional deviation: along the z-axis, along external and internal dimensions, and through diameters. The methodology involves a step-by-step procedure wherein after establishing the experimental plan and manufacturing the specimens, the measurements taken are analyzed via Grey Relational Analysis (GRA) to find out the optimal combination of parameters leading to the minimum deviation in all dimensions of parts for both materials. Statistical techniques such as Analysis of Variance (ANOVA) and Signal to Noise (S/N) ratio were also used. Subsequently, a confirmation test was carried out to validate the results obtained. The findings of the ANOVA and the S/N ratio were in good concordance with those of GRA.

Online Detection and Prediction of Fused Deposition Modelled Parts Using Artificial Intelligence

Fused deposition modelling (FDM) is a technology used for filament deposition of heated plastic filaments by a given pattern by the melted extrusion process. Delamination is a critical issue of FDM's incredibly complex parts. In this chapter, the artificial intelligence (machine learning) model is used for online detections and prediction of FDM parts. The proposed machine learning and convolutional neural network model is capable of online detect delamination of FDM parts. The proposed model can also be applied for different types of additive manufacturing materials with less human interaction.

Pengaruh Setting Parameter pada Slicing Software terhadap Surface Roughness Objek 3D Printing menggunakan Metode Taguchi

<p>Teknologi FDM (<em>Fused Deposition Modelling</em>) merupakan salah satu teknologi yang digunakan untuk membuat objek 3D. FDM sering disebut sebagai teknologi yang sudah mampu mengubah dunia manufaktur dewasa ini. Namun teknologi FDM memiliki kelemahan karena teknologi ini menggunakan proses <em>building per layer </em>membuat permukaan yang dihasilkan terlihat memiliki garis yang menunjukan batas antar <em>layer </em>sehingga mempengaruhi kekasaran pada permukaan produk cetak. Penelitian ini menggunakan filamen <em>Super Tough</em> PLA (ST.PLA). Tujuan penelitian ini adalah untuk mengetahui pengaruh parameter proses terhadap kekasaran permukaan objek cetak dan untuk mengetahui seting parameter proses yang menghasilkan kekasaran permukaan terbaik dari parameter proses yang digunakan. Penelitian ini menggunakan metode Taguchi dengan matriks ortogonal L₂₅(5⁶). Parameter proses yang akan dipilih dan dianalisis dalam penelitian ini adalah<em> layer thickness, printing speed, nozzle temperature, orientation, flowrate</em>, <em>cooling speed </em>dan respon yang diamati adalah kekasaran permukaan objek cetak. Untuk mengatasi permasalahan <em>noise</em> (gangguan) maka dicetak masing-masing tiga kali replikasi Selanjutnya parameter proses tersebut akan dianalisis menggunakan Analisis Varian (ANOVA). Berdasarkan data hasil pengukuran kekasaran permukaaan objek cetak, maka diperoleh parameter proses yang memberikan pengaruh paling besar terhadap kekasaran permukaan objek cetak dengan menggunakan filamen ST-PLA adalah <em>layer thickness</em> dengan nilai F hitung sebesar 129,96, <em>flowrate</em> dengan nilai F hitung sebesar 6 dan <em>orientation</em> dengan nilai F hitung sebesar 3,03. Seting parameter proses yang menghasilkan nilai kekasaran permukaan terbaik objek cetak adalah 0,10 mm yaitu pada eksperimen nomor lima (Exp. No. 5) dengan rata-rata 12,61 µm, dengan pengaturan <em>layer thickness</em>, 45 mm/s pada pengaturan <em>printing speed</em>, 210˚C pada <em>nozzle temperature</em>, 0˚ pada <em>orientation</em>, 110% pada pengaturan <em>flowrate</em> dan 40% pada pengaturan <em>cooling speed</em>. Seluruh parameter proses tersebut disetting pada <em>slicing software</em> ideamaker 3.6.1. dalam menghasilkan G-Code objek cetak.</p>

EKSTRUKSI DAN KARAKTERISASI FILAMEN KOMPOSIT POLYLACTID ACID (PLA) / CARBON NANO TUBE (CNT)

Dewasa ini teknologi additive manufacturing (AM) mengalami perkembangan yang pesat. Salah satu metode additive manufacturing yang saat ini populer di berbagai belahan dunia adalah metode fused deposition modelling. Prinsip kerja metode ini adalah dengan cara mengekstrusi material filamen polimer yang meleleh dan sudah melewati temperatur rekristalisasinya melalui sebuah nozzle, kemudian produk akan terbentuk secara lapis demi lapis yang dibentuk melalui gerakan relatif dari meja mesin. Dalam pengaplikasiannya, material filamen dapat dibuat dalam bentuk komposit sehingga diperoleh sifat-sifat unik tertentu sehingga dapat digunakan pada bidang-bidang tertentu. Salah satunya adalah pada bidang kesehatan, sebagai pembuatan implan atau jaringan tubuh dengan memiliki kekuatan yang tinggi dan bersifat biodegradable. Pada penelitian ini, dilakukan pembuatan filamen komposit PLA-CNT dengan metode ekstrusi. Proses ekstrusi dilakukan dengan variasi temperatur 143, 145, dan 147ºC dengan memberi tambahan polyethylene glycol (PEG) sebagai plasticizer untuk meningkatkan fleksibilitas dan workability. Hasil pengamatan dengan scanning electron microscope (SEM) mengindikasikan bahwa proses ekstrusi berjalan kurang stabil karena terdapat tekstur permukaan yang bergelombang. Dari pengujian karakterisasi differential scanning calorimetry (DSC), derajat kristalinitas pada variasi ekstrusi filamen PLA-CNT mengalami peningkatan seiring meningkatnya temperatur ekstrusi. Pada pengujian karakterisasi fourier transform infrared spectroscopy (FTIR) terdapat perubahan komposisi kimia pada filamen yang signifikan, yang juga sejalan dengan degradasi sifat mekanis. Variasi filamen dengan temperatur ekstrusi 147ºC memiliki nilai kekerasan paling tinggi yaitu sebesar 40,50 MPa.

Mechanical and Physical Properties of Recycled-Carbon-Fiber-Reinforced Polylactide Fused Deposition Modelling Filament

Carbon-fiber-reinforced plastic materials have attracted several applications, including the fused deposition modelling (FDM) process. As a cheaper and more environmentally friendly alternative to its virgin counterpart, the use of milled recycled carbon fiber (rCF) has received much attention. The quality of the feed filament is important to avoid filament breakage and clogged nozzles during the FDM printing process. However, information about the effect of material parameters on the mechanical and physical properties of short rCF-reinforced FDM filament is still limited. This paper presents the effect of fiber loading (10 wt%, 20 wt%, and 30 wt%) and fiber size (63 µm, 75 µm, and 150 µm) on the filament’s tensile properties, surface roughness, microstructure, porosity level, density, and water absorptivity. The results show that the addition of 63 µm fibers at 10 wt% loading can enhance filament tensile properties with minimal surface roughness and porosity level. The addition of rCF increased the density and reduced the material’s water intake. This study also indicates a clear trade-off between the optimized properties. Hence, it is recommended that the optimization of rCF should consider the final application of the product. The findings of this study provide a new manufacturing strategy in utilizing milled rCF in potential 3D printing-based applications.