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Bioelectronics presents a promising future in the field of embedded and implantable electronics, providing a range of functional applications, from personal health monitoring to bioactuators. However, due to the intrinsic difficulties present in producing and optimizing bioelectronics, recent research has focused on utilizing machine learning (ML) to reliably mitigate such issues and aid in process development. This review focuses on the recent developments of integrating ML into bioelectronics, aiding in a multitude of areas, such as material development, fabrication process optimization, and system integration. First, discussing how ML has aided in the material development by identifying complex relationships between process input parameters and desired outputs, such as product design. Second, examine the advancements in ML to accurately optimize fabrication precision and stability for various 3D printing technologies. Third, provide an overview of how ML can greatly assist in the analysis of complex, nonlinear relationships in data obtained from bioelectronics. Lastly, a summary of the challenges present with utilizing ML with bioelectronics and any other developments in this field. Such advancements in the field of bioelectronics and ML could hopefully build a strong foundation for this research field, promoting smart optimization together with effective use of ML to further enhance the effectiveness of such applications.

Drug delivery devices which can control the release of drugs on demand allow for improved treatment to a patient. These smart drug delivery devices allow for the release of drugs to be turned on and off as needed, thereby increasing the control over the drug concentration within the patient. The addition of electronics to the smart drug delivery devices increases the functionality and applications of these devices. Through the use of 3D printing and 3D-printed electronics, the customizability and functions of such devices can also be greatly increased. With the development in such technologies, the applications of the devices will be improved. In this review paper, the application of 3D-printed electronics and 3D printing in smart drug delivery devices with electronics as well as the future trends of such applications are covered.

The first years of an infant’s life represent a sensitive period for neurodevelopment and see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed instrumented toys as a new task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a Barometer and Inertial Measurement Unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provides a rich dataset describing the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition may be inferred. Such a tool potentially provides an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

Material jetting bioprinting is a highly promising three-dimensional (3D) bioprinting technique that facilitates drop-on-demand (DOD) deposition of biomaterials and cells at pre-defined positions with high precision and resolution. A major challenge that hinders the prevalent use of the material jetting bioprinting technique is due to its limited range of printable hydrogel-based bio-inks. As a proof-of-concept, further modifications were made to gelatin methacrylate (GelMA), a gold-standard bio-ink, to improve its printability in a thermal inkjet bioprinter (HP Inc. D300e Digital Dispenser). A two-step modification process comprising saponification and heat treatment was performed; the GelMA bio-ink was first modified

Additive manufacturing (AM) methods have grown and evolved rapidly in recent years [...]

The process-structure-property (PSP) relationship of continuous carbon fiber reinforced thermoplastics manufactured through fused filament fabrication was investigated for the first time, specifically on mode I Interlaminar Fracture toughness (ILFT). A standard double cantilever beam test is used to evaluate the mode I ILFT. The PSP relationship is established using micro-computed tomography scan and laser optical microscope. This study demonstrates that higher nozzle, bed temperatures, and lower print speed enhances the mode I ILFT. The mode I ILFT is found to be greatly affected by porosity and bond formation between the layers, which are dependent on the process parameters. National Research Foundation (NRF) This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Minister’s Ofce, Singapore under its Medium-Sized Centre funding scheme.

While significant progress has been made in laser powder bed fusion (L-PBF) for metal additive manufacturing (AM), there is still limited large scale adoption of this advanced manufacturing technique by the industry. This paper covers the recent developments in L-PBF with discussions from the materials and process perspectives. High entropy alloys and high strength aluminium alloys have been identified as key materials development for L-PBF. Then, scanning strategies and multi-lasers applications for the process are also discussed. Other research trends and topics such as powder recycling, shape memory alloys and magnetic alloys are illustrated. The final part of this paper provides an outlook on the recent advancements while suggesting potential research topics for L-PBF. National Research Foundation (NRF) Accepted version The authors acknowledge the support from the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.

PurposeSoft robotics is currently a rapidly growing new field of robotics whereby the robots are fundamentally soft and elastically deformable. Fabrication of soft robots is currently challenging and highly time- and labor-intensive. Recent advancements in three-dimensional (3D) printing of soft materials and multi-materials have become the key to enable direct manufacturing of soft robots with sophisticated designs and functions. Hence, this paper aims to review the current 3D printing processes and materials for soft robotics applications, as well as the potentials of 3D printing technologies on 3D printed soft robotics.Design/methodology/approachThe paper reviews the polymer 3D printing techniques and materials that have been used for the development of soft robotics. Current challenges to adopting 3D printing for soft robotics are also discussed. Next, the potentials of 3D printing technologies and the future outlooks of 3D printed soft robotics are presented.FindingsThis paper reviews five different 3D printing techniques and commonly used materials. The advantages and disadvantages of each technique for the soft robotic application are evaluated. The typical designs and geometries used by each technique are also summarized. There is an increasing trend of printing shape memory polymers, as well as multiple materials simultaneously using direct ink writing and material jetting techniques to produce robotics with varying stiffness values that range from intrinsically soft and highly compliant to rigid polymers. Although the recent work is done is still limited to experimentation and prototyping of 3D printed soft robotics, additive manufacturing could ultimately be used for the end-use and production of soft robotics.Originality/valueThe paper provides the current trend of how 3D printing techniques and materials are used particularly in the soft robotics application. The potentials of 3D printing technology on the soft robotic applications and the future outlooks of 3D printed soft robotics are also presented.

Copyright © 2020 The Author(s) and Korean Association of ANATOMISTS. Learning anatomy is commonly facilitated by use of cadavers, plastic models and more recently three-dimensional printed (3DP) anatomical models as they allow students to physically touch and hold the body segments. However, most existing models are limited to surface features of the specimen, with little opportunity to manipulate the structures. There is much interest in developing better 3DP models suitable for anatomy education. This study aims to determine the feasibility of developing a multi-material 3DP heart model, and to evaluate students' perceptions of the model. Semi-automated segmentation was performed on computed tomgoraphy plastinated heart images to develop its 3D digital heart model. Material jetting was used as part of the 3D printing process so that various colors and textures could be assigned to the individual segments of the model. Morphometric analysis was conducted to quantify the differences between the printed model and the plastinated heart. Medical students' opinions were sought using a 5-point Likert scale. The 3DP full heart was anatomically accurate, pliable and compressible to touch. The major vessels of the heart were color-coded for easy recognition. Morphometric analysis of the printed model was comparable with the plastinated heart. Students were positive about the quality of the model and the majority of them reported that the model was useful for their learning and that they would recommend their use for anatomical education. The successful feasibility study and students' positive views suggest that the development of multi-material 3DP models is promising for medical education.

Grippers with shape conformance and tunable rigidity are often achieved using a combination of soft and rigid structures made of different materials. These grippers are often known as soft grippers. In this review article, various gripper designs with shape conforming ability and stiffness tunability are discussed. In particular, the discussion mainly focuses on the advantages and limitations of each gripper design in terms of the shape conformance and the ease of manufacturability. Then, various 3D printing technologies that are capable of doing multimaterial printing have been introduced. The potential of multimaterial 3D printing of soft and smart grippers has been discussed. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) Submitted/Accepted version This work is partly supported by the Schaeffler Hub for Advanced Research at NTU, under the ASTAR IAF-ICP Programme ICP1900093.

The ability to design and fabricate lightweight structure is one of the most important aspects for building a drone system. Often, connecting wires are used on the drone system for powering and signal transmission at the expense of the drone’s weight. In this paper, we explore the design and fabrication of a safety cage for drone using design optimization and 3D printing. A comparison between fused deposition modelling (FDM) and selective laser sintering (SLS) 3D printing techniques for the fabrication of thin structures was made, and it was found that SLS is more superior in this aspect. A hybrid 3D printing process combining SLS and aerosol jet printing is proposed for the fabrication of lightweight multifunctional structure with printed circuit for a drone safety cage. The safety cage is designed in such a way that maximizes the production efficiency of SLS printing process. In addition, aerosol jet printing is used to fabricate conformal circuit onto the 3D-printed safety cage structure to replace the conventional connecting wires for weight saving purpose. Lastly, an electrical characterization is conducted to investigate the functionality of the printed conductive traces on the safety cage. Nevertheless, this work demonstrates the streamlining of various 3D printing approaches for the fabrication of multifunctional structures with conformal circuits. National Research Foundation (NRF) Submitted/Accepted version This research is supported by NAMIC Singapore and funded by the National Research Foundation Singapore under its Innovation Cluster Programme.

The use of 3D printing to produce acoustic panels with good mechanical and acoustic properties was investigated in this paper. Various fiber layups of the fiberglass face sheet and core designs were fabricated and tested for their indentation resistance and acoustic absorption performance. It was found that the bidirectional face sheet layup exhibited the best indentation energy absorption recording 4.2 J, which is 37% more than the 45-degree layout and 66% more than the quasi-isotropic layup. The specific energy absorption of the hybrid honeycomb core is the best among the three core designs recording 404 J/kg, which is 56% higher than the corrugated triangle with horizontal beam core (359 J/kg) and 20% higher than double ellipse core (335 J/kg). Computed-Tomography (CT) scan was used to study the fracture behavior of the sandwich structures. It was found that the bidirectional layup exhibited a different failure mode as compared to the 45-degree and quasi-isotropic layup. In terms of the acoustic properties, the face sheets with various layup patterns have a low acoustic absorption coefficient with minimal differences from each other at low frequencies (500 Hz–3000 Hz) and have higher absorption coefficients with greater differences from each other at frequencies between 3000 Hz–6500 Hz. The absorption curve was significantly affected by the design of the core. The orientation of the core also comes into play if the core is asymmetrical. The hybrid honeycomb sandwich structure was the optimal structure among the three designs for balanced indentation resistance and acoustic insulation. National Research Foundation (NRF) This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Medium-Sized Centre funding scheme.

Material jetting bioprinting is a highly promising three-dimensional (3D) bioprinting technique that facilitates drop-on-demand (DOD) deposition of biomaterials and cells at pre-defined positions with high precision and resolution. A major challenge that hinders the prevalent use of the material jetting bioprinting technique is due to its limited range of printable hydrogel-based bio-inks. As a proof-of-concept, further modifications were made to gelatin methacrylate (GelMA), a gold-standard bio-ink, to improve its printability in a thermal inkjet bioprinter (HP Inc. D300e Digital Dispenser). A two-step modification process comprising saponification and heat treatment was performed; the GelMA bio-ink was first modified via a saponification process under highly alkali conditions to obtain saponified GelMA (SP-GelMA), followed by heat treatment via an autoclaving process to obtain heat-treated SP-GelMA (HSP-GelMA). The bio-ink modification process was optimized by evaluating the material properties of the GelMA bio-inks via rheological characterization, the bio-ink crosslinking test, nuclear magnetic resonance (NMR) spectroscopy and the material swelling ratio after different numbers of heat treatment cycles (0, 1, 2 and 3 cycles). Lastly, size-exclusion chromatography with multi-angle light scattering (SEC-MALS) was performed to determine the effect of heat treatment on the molecular weight of the bio-inks. In this work, the 4% H2SP-GelMA bio-inks (after 2 heat treatment cycles) demonstrated good printability and biocompatibility (in terms of cell viability and proliferation profile). Furthermore, thermal inkjet bioprinting of the modified hydrogel-based bio-ink (a two-step modification process comprising saponification and heat treatment) via direct/indirect cell patterning is a facile approach for potential fundamental cell-cell and cell-material interaction studies. Submitted/Accepted version This study was supported by the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab.

A database linking process parameters and material properties for additive manufacturing enables the performance of the material to be determined based on the process parameters, which are useful in the design and fabrication stage of a product. The data, however, are often incomplete as each individual research work focused on certain process parameters and material properties due to the wide range of variables available. Imputation of missing data is thus required to complete the material library. In this work, we attempt to collate the data of Ti6Al4V, a popular alloy used in aerospace and biomedical industries, fabricated using powder bed fusion, or commonly known as selective laser melting (SLM). Various imputation techniques of missing data of the SLM Ti6Al4V dataset, such as the k-nearest neighbor (kNN), multivariate imputation by chained equations, and graph imputation neural network (GINN) are investigated in this article. It was observed that kNN performed better in imputing variables related to process parameters, whereas GINN performed better in variables related to material properties. To further improve the quality of imputation, a strategy to use the median of the imputed values obtained from the three models has resulted in significant improvement in terms of the relative mean square error. Self-organizing map was used to visualize the relationship among the process parameters and the material properties.

Additive manufacturing has proven to be a viable alternative to conventional manufacturing methodologies for metallic implants due to its capability to customize and fabricate novel and complex geometries. Specific to its use in dental applications, various groups have reported successful outcomes for customized root-analog dental implants in preclinical and clinical studies. However, geometrical accuracy of the fabricated samples has never been analyzed. In this article, we studied the geometric accuracy of a 3D printed titanium dental implant design against the tooth root of the monkey maxilla incisor. Monkey maxillas were scanned using cone-beam computed tomography, then segmentation of the incisor tooth roots was performed before the fabrication of titanium dental implants using a laser powder bed fusion (PBF) process. Our results showed 68.70% ± 5.63 accuracy of the 3D printed dental implant compared to the actual tooth (n = 8), where main regions of inaccuracies were found at the tooth apex. The laser PBF fabrication process of the dental implants showed a relatively high level of accuracy of 90.59% ± 4.75 accuracy (n = 8). Our eventual goal is to develop an accurate workflow methodology to support the fabrication of patient-specific 3D-printed titanium dental implants that mimic patients’ tooth anatomy and fit precisely within the socket upon tooth extraction. This is essential for promoting primary stability and osseointegration of dental implants in the longer term National Research Foundation (NRF) Published version This work is funded by the National Health Innovation Centre Singapore (NHIC-I2D-1712189) and National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme.

Polyjet, a material jetting 3D printing technique, has been widely used for the fabrication of patient-specific anatomical models owing to the toolless fabrication technique and its ability to print multiple materials in a single part. Although the fabrication of anatomical models with high dimensional accuracy has been demonstrated, 3D printed anatomical models with tissue-mimicking properties have not been realized. In this study, a composite layering design was used to tune the shore hardness and compressive modulus of the Polyjet-printed parts in an attempt to mimic the properties of human tissues. 216 specimens (with 72 combinations of design parameters) were printed and tested to develop the material library for the anatomical models. An analytical model was developed to estimate the effective compressive modulus and shore hardness of the composite laminate. A neural network was used to learn the multi-dimensional relationship between the design parameters and mechanical properties. The 5-33-2 network size is found to be the optimum neural network structure with a mean square error of 0.98% for the compressive modulus, lower than the traditional response surface method model. A genetic algorithm was used to search the design space for the most optimum design parameters for the targeted effective shore hardness. Nanyang Technological University National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme and the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore Start-Up Grant.

Additive manufacturing (AM) has grown and evolved rapidly in recent years [...]

Electric vehicles (EVs) are in the incipient stage today and have the capacity to lead the automobile sector in the future. Battery packs of an electric vehicle are held by a large battery housing located at the bottom of the car body. The battery pack contributes significantly to the vehicle’s overall weight. Reduction of the overall weight of the future cars is a designer’s priority today. The aim to reduce weight can be achieved using topological optimisation. The optimised design is complex and therefore requires freeform fabrication. Additive manufacturing (AM) or 3D printing allows the fabrication of complex structures, hence, enables the fabrication of topological optimised parts without compromising on the part performance. In this paper, the topological optimisation of an electric vehicle battery housing is carried out to reduce the weight of the housing. Certain parts of the battery housing are removed and modified to get the final design. The physical, geometric, and performance aspects of the re-designed and original battery housing are compared. Additionally, the feasibility of the fabrication of the re-designed battery housing is discussed through support structures generation and feasibility index. Different AM methods such as powder bed fusion (PBF) and directed energy deposition (DED) are analysed on the basis of advantages and limitations. Finally, a suitable AM technique, selective laser melting (SLM), is chosen to fabricate the topological optimised battery housing.

Three-dimensional (3D) bioprinting systems serve as advanced manufacturing platform for the precise deposition of cells and biomaterials at pre-defined positions. Among the various bioprinting techniques, the drop-on-demand jetting approach facilitates deposition of pico/nanoliter droplets of cells and materials for study of cell-cell and cell-matrix interactions. Despite advances in the bioprinting systems, there is a poor understanding of how the viability of primary human cells within sub-nanoliter droplets is affected during the printing process. In this work, a thermal inkjet system is utilized to dispense sub-nanoliter cell-laden droplets, and two key factors - droplet impact velocity and droplet volume - are identified to have significant effect on the viability and proliferation of printed cells. An increase in the cell concentration results in slower impact velocity, which leads to higher viability of the printed cells and improves the printing outcome by mitigating droplet splashing. Furthermore, a minimum droplet volume of 20 nL per spot helps to mitigate evaporation-induced cell damage and maintain high viability of the printed cells within a printing duration of 2 min. Hence, controlling the droplet impact velocity and droplet volume in sub-nanoliter bioprinting is critical for viability and proliferation of printed human primary cells. Published version This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAFICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab.

Ti-Nb based alloys have the potential to be used as structural implant materials due to their excellent bio-compatibility and ability to reduce stress shielding. The idea to additively manufacture Ti-Nb based alloys using selective laser melting (SLM) technology can further improve the resultant implant quality. However, the lack of economically sound and readily available pre-alloyed powder has pushed for the usage of composite powder as a means to hasten research pace in fabricating new alloy systems via SLM. The usage of Ti-Nb composite powder can lead to several problems, particularly the issue of macro-segregation. Hence, this paper presents the potential of laser re-melting scanning strategy to address macro-segregation without sacrificing (or even improving) density of parts fabricated by SLM.