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Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed. 

The food industry is one of the most regulated businesses in the world and follows strict internal and regulated requirements to ensure product reliability and safety. In particular, the industry must ensure that biological, chemical, and physical hazards are controlled from the production and distribution of raw materials to the consumption of the finished product. In the United States, the FDA regulates the efficacy and safety of food ingredients and packaging. Traditional packaging materials such as paper, aluminum, plastic, and biodegradable compostable materials have gradually evolved. Coatings made with nanotechnology promise to radically improve the performance of food packaging materials, as their excellent properties improve the appearance, taste, texture, and shelf life of food. This review article highlights the role of nanomaterials in designing and manufacturing anti-fouling and antimicrobial coatings for the food packaging industry. The use of nanotechnology coatings as protective films and sensors to indicate food quality levels is discussed. In addition, their assessment of regulatory and environmental sustainability is developed. This review provides a comprehensive perspective on nanotechnology coatings that can ensure high-quality nutrition at all stages of the food chain, including food packaging systems for humanitarian purposes. 

Microneedle (MN) technology is an optimal choice for the delivery of drugs via the transdermal route, with a minimally invasive procedure. MN applications are varied from drug delivery, cosmetics, tissue engineering, vaccine delivery, and disease diagnostics. The MN is a biomedical device that offers many advantages including but not limited to a painless experience, being time-effective, and real-time sensing. This research implements additive manufacturing (AM) technology to fabricate MN arrays for advanced therapeutic applications. Stereolithography (SLA) was used to fabricate six MN designs with three aspect ratios. The MN array included conical-shaped 100 needles (10 × 10 needle) in each array. The microneedles were characterized using optical and scanning electron microscopy to evaluate the dimensional accuracy. Further, mechanical and insertion tests were performed to analyze the mechanical strength and skin penetration capabilities of the polymeric MN. MNs with higher aspect ratios had higher deformation characteristics suitable for penetration to deeper levels beyond the stratum corneum. MNs with both 0.3 mm and 0.4 mm base diameters displayed consistent force–displacement behavior during a skin-equivalent penetration test. This research establishes guidelines for fabricating polymeric MN for high-accuracy and low-cost 3D printing. 

Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain. 

Three-dimensional (3D) printing was utilized for the fabrication of a composite scaffold of poly(ε-caprolactone) (PCL) and calcium magnesium phosphate (CMP) bioceramics for bone tissue engineering application. Four groups of scaffolds, that is, PMC-0, PMC-5, PMC-10, and PMC-15, were fabricated using a custom 3D printer. Rheology analysis, surface morphology, and wettability of the scaffolds were characterized. The PMC-0 scaffolds displayed a smoother surface texture and an increase in the ceramic content of the composite scaffolds exhibited a rougher structure. The hydrophilicity of the composite scaffold was significantly enhanced compared to the control PMC-0. The effect of ceramic content on the bioactivity of fibroblast NIH/3T3 cells in the composite scaffold was investigated. Cell viability and toxicity studies were evaluated by comparing results from lactate dehydrogenase (LDH) and Alamar Blue (AB) colorimetric assays, respectively. The live-dead cell assay illustrated the biocompatibility of the tested samples with more than 100% of live cells on day 3 compared to the control one. The LDH release indicated that the composite scaffolds improved cell attachment and proliferation. In this research, the fabrication of a customized composite 3D scaffold not only mimics the rough textured architecture, porosity, and chemical composition of natural bone tissue matrices but also serves as a source for soluble ions of calcium and magnesium that are favorable for bone cells to grow. These 3D-printed scaffolds thus provide a desirable microenvironment to facilitate biomineralization and could be a new effective approach for preparing constructs suitable for bone tissue engineering. 

In Machine learning (ML) and deep learning (DL), hyperparameter tuning is the process of selecting the combination of optimal hyperparameters that give the best performance. Thus, the behavior of some machine learning (ML) and deep learning (DL) algorithms largely depend on their hyperparameters. While there has been a rapid growth in the application of machine learning (ML) and deep learning (DL) algorithms to Additive manufacturing (AM) techniques, little to no attention has been paid to carefully selecting and optimizing the hyperparameters of these algorithms in order to investigate their influence and achieve the best possible model performance. In this work, we demonstrate the effect of a grid search hyperparameter tuning technique on a Multilayer perceptron (MLP) model using datasets obtained from a Fused Filament Fabrication (FFF) AM process. The FFF dataset was extracted from the MakerBot MethodX 3D printer using internet of things (IoT) sensors. Three (3) hyperparameters were considered – the number of neurons in the hidden layer, learning rate, and the number of epochs. In addition, two different train-to-test ratios were considered to investigate their effects on the AM process data. The dataset consisted of five (5) dominant input parameters which include layer thickness, build orientation, extrusion temperature, building temperature, and print speed and three (3) output parameters: dimension accuracy, porosity, and tensile strength. RMSE, and the computational time, CT, were both selected as the hyperparameter performance metrics. The experimental results reveal the optimal configuration of hyperparameters that contributed to the best performance of the MLP model. 

Three-dimensional (3D) printing is implemented for surface modification of titanium alloy substrates with multilayered biofunctional polymeric coatings. Poly(lactic-co- glycolic) acid (PLGA) and polycaprolactone (PCL) polymers were embedded with amorphous calcium phosphate (ACP) and vancomycin (VA) therapeutic agents to promote osseointegration and antibacterial activity, respectively. PCL coatings revealed a uniform deposition pattern of the ACP-laden formulation and enhanced cell adhesion on the titanium alloy substrates as compared to the PLGA coatings. Scanning electron microscopy and Fourier-transform infrared spectroscopy confirmed a nanocomposite structure of ACP particles showing strong binding with the polymers. Cell viability data showed comparable MC3T3 osteoblast proliferation on polymeric coatings as equivalent to positive controls. In vitro live/dead assessment indicated higher cell attachments for 10 layers (burst release of ACP) as compared to 20 layers (steady release) for PCL coatings. The PCL coatings loaded with the antibacterial drug VA displayed a tunable release kinetics profile based on the multilayered design and drug content of the coatings. Moreover, the concentration of active VA released from the coatings was above the minimum inhibitory concentration and minimum bactericidal concentration, demonstrating its effectiveness against Staphylococcus aureus bacterial strain. This research provides a basis for developing antibacterial biocompatible coatings to promote osseointegration of orthopedic implants. 

Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin.