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The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

Additive manufacturing (AM), also known as three-dimensional (3D) printing technology, has been used in the health-care industry for over two decades. It is in high demand in the health-care industry due to its strength to manufacture custom-designed and personalized 3D constructs. Recently, AM technologies are being explored to develop personalized drug delivery systems, such as personalized oral dosages, implants and others due to their potential to design and develop systems with complex geometry and programmed controlled release profile. Furthermore, in 2015, the US Food and Drug Administration approved the first AM medication, Spritam® (Apprecia Pharmaceuticals) which has led to tremendous interest in exploring this technology as a bespoke solution for patient-specific drug delivery systems. The purpose of this study is to provide a comprehensive overview of AM technologies applied to the development of personalized drug delivery systems, including an analysis of the commercial status of AM based drugs and delivery devices.

This review paper provides a detailed understanding of how AM technologies are used to develop personalized drug delivery systems. Different AM technologies and how these technologies can be chosen for a specific drug delivery system are discussed. Different types of materials used to manufacture personalized drug delivery systems are also discussed here. Furthermore, recent preclinical and clinical trials are discussed. The challenges and future perceptions of personalized medicine and the clinical use of these systems are also discussed.

Substantial works are ongoing to develop personalized medicine using AM technologies. Understanding the regulatory requirements is needed to establish this area as a point-of-care solution for patients. Furthermore, scientists, engineers and regulatory agencies need to work closely to successfully translate the research efforts to clinics.

This review paper highlights the recent efforts of AM-based technologies in the field of personalized drug delivery systems with an insight into the possible future direction.

The purpose of this study is to increasing the gauge factor, reducing the hysteresis error and improving the stability over cyclic deformations of a conductive polylactic acid (CPLA)-based 3D-printed strain sensor by modifying the sensing element geometry.

Five different configurations, namely, linear, serpentine, square, triangular and trapezoidal, of CPLA sensing elements are printed on the thermoplastic polyurethane substrate material individually. The resistance change ratio of the printed sensors, when loaded to a predefined percentage of the maximum strain values over multiple cycles, is recorded. Finally, the thickness of substrate and CPLA and the included angle of the triangular strain sensor are evaluated for their influences on the sensitivity.

The triangular configuration yields the least hysteresis error with high accuracy over repeated loading conditions, because of its uniform stress distribution, whereas the conventional linear configuration produces the maximum sensitivity with low accuracy. The thickness of the substrate and sensing element has more influence over the included angle, in enhancing the sensitivity of the triangular configuration. The sensitivity of the triangular configuration exceeds the linear configuration when printed at ideal sensor dimensional values.

The 3D printing parameters are kept constant for all the configurations; rather it can be varied for improving the performance of the sensor. Furthermore, the influences of stretching rate and nozzle temperature of the sensing material are not considered in this work.

The sensitivity and accuracy of CPLA-based strain sensor are evaluated for modification in its geometry, and the performance metrics are enhanced using the regression modelling.

This study aims to evaluate in situ oxidative polymerization of aniline (Ani) as a post-processing method to promote extrusion-based 3D printed parts, made from insulating polymers, to components with functional properties, including electrical conductivity and chemical sensitivity.

Extrusion-based 3D printed parts of polyethylene terephthalate modified with glycol (PETG) and polypropylene (PP) were coated in an aqueous acid solution via in situ oxidative polymerization of Ani. First, the feedstocks were characterized. Densely printed samples were then used to assess the adhesion of polyaniline (PAni) and electrical conductivity on printed parts. The best feedstock candidate for PAni coating was selected for further analysis. Last, a Taguchi methodology was used to evaluate the influence of printing parameters on the coating of porous samples. Analysis of variance and Tukey post hoc test were used to identify the best levels for each parameter.

Colorimetry measurements showed significant color shifts in PP samples and no shifts in PETG samples upon pullout testing. The incorporation of PAni content and electrical conductivity were, respectively, 41% and 571% higher for PETG in comparison to PP. Upon coating, the surface energy of both materials decreased. Additionally, the dynamic mechanical analysis test showed minimal influence of PAni over the dynamic mechanical properties of PETG. The parametric study indicated that only layer thickness and infill pattern had a significant influence on PAni incorporation and electrical conductivity of coated porous samples.

Current literature reports difficulties in incorporating PAni without affecting dimensional precision and feedstock stability. In situ, oxidative polymerization of Ani could overcome these limitations. However, its use as a functional post-processing of extrusion-based printed parts is a novelty.

The purpose of this study is to modify the FDM 3D printer to print with polystyrene (PS) microspheres as the printing material, thus enabling bottom-up structural color printing and evaluating structural color printing.

This study chose a range of different heated bed temperatures to determine a suitable temperature for accelerating the self-assembly of photonic crystals and printing structural colors on various substrates. In addition, this study enhanced the structural color by doping PS microspheres with different contents of Acid Black 210 dye and evaluated the color-enhanced structural color by eye and spectrophotometer under different light sources.

The results show that the modified 3D printer can be used for structural color printing, and 50°C is determined as the heated bed temperature. There are significant differences in structural colors when printing under different color backgrounds and material substrates, and corresponding suitable substrates should be selected according to the application. The doping of PS microspheres with varying contents of dye results in different color levels of structural color. As with pigment colors, the visual perception of structural color varies when viewed under different light sources.

This paper proposes to print structural colors low-costly, analyze structural colors under substrate and light source conditions, and expand the structural color gamut by enhancing structural colors, which has positive implications for further research on structural colors as printing colors.

The purpose of this study is to determine the material extrusion (MEX) printability envelope of a new kind of low-viscosity powder-binder feedstocks using rheological properties.

Formulation of 13 feedstocks (variation of solid loading 60–67 Vol.% and thickening agent proportion 3–15 Vol.%) that were characterized and printed at different temperatures.

Three rheological models were successfully used to define the viscosity envelope, producing stable and defect-free printing. At a shear deformation rate experienced by the feedstock in the nozzle ranging from 100 to 300 s^–1, it was confirmed that metal injection molding (MIM) feedstocks exhibiting a low viscosity between 100 and 150 Pa s could be printed using an extrusion temperature as low as 85 °C.

MEX can be used in synergy with MIM to accelerate mold development for a new injected part or simply as a replacement for MIM when the cost of the mold becomes too high for very small production volumes.

Correlation between the rheological properties of this new generation of low-viscosity feedstocks and MEX printability has been demonstrated for the first time.

This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.

The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.

To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.

The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.

This study aims to evaluate the potential of using the in-nozzle impregnation approach to reuse recycled PET (RPET) to develop continuous banana fiber (CBF) reinforced bio-composites. The mechanical properties and fracture morphology behavior are evaluated to establish the relationships between layer spacing–microstructural characteristics–mechanical properties of CBF/RPET composite.

This study uses RPET filament developed from post-consumer PET bottles and CBF extracted from agricultural waste banana sap. RPET serves as the matrix material, while CBF acts as the reinforcement. The test specimens were fabricated using a customized fused deposition modeling 3D printer. In this process, customized 3D printer heads were used, which have a unique capability to extrude and deposit print fibers consisting of a CBF core coated with an RPET matrix. The tensile and flexural samples were 3D printed at varying layer spacing.

The Young’s modulus (E), yield strength (sy) and ultimate tensile strength of the CBF/RPET sample fabricated with 0.7 mm layer spacing are 1.9 times, 1.25 times and 1.8 times greater than neat RPET, respectively. Similarly, the flexural test results showed that the flexural strength of the CBF/RPET sample fabricated at 0.6 mm layer spacing was 47.52 ± 2.00 MPa, which was far greater than the flexural strength of the neat RPET sample (25.12 ± 1.94 MPa).

This study holds significant social implications highlighting the growing environmental sustainability and plastic waste recycling concerns. The use of recycled PET material to develop 3D-printed sustainable structures may reduce resource consumption and encourages responsible production practices.

The key innovation lies in the concept of in-nozzle impregnation approach, where RPET is reinforced with CBF to develop a sustainable composite structure. CBF reinforcement has made RPET a superior, sustainable, environmentally friendly material that can reduce the reliance on virgin plastic material for 3D printing.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.