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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.

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.

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.

Unlike traditional hard grippers, soft robotic grippers are commonly made of soft materials so that the soft grippers can produce motion via elastic deformations of their compliant components. The advantages of compliance allow soft grippers to effectively eliminate shocks caused by hard contact, which usually occurs when a hard robotic gripper manipulates a hard object. Until now, the soft robotic grippers are able to operate numerous objects with irregular geometries and different textures. Besides, with the help of embedded sensors, soft robotic grippers have facilitated the growing automation of many tasks, which are thought to be far too delicate for robotic manipulation. This paper reviews the advancement in soft robotic grippers. The paper first introduces the actuation technologies followed by the design and fabrication techniques. The use of 3D printing techniques in the fabrication of the soft gripper is also discussed. The Review then highlights the challenges and future outlook in the fabrication of soft grippers and sensors. Nanyang Technological University Submitted/Accepted version This work was partly supported by the Schaeffler Hub for Advanced Research at NTU, under the ASTAR IAF-ICP Programme ICP1900093.

While significant progress has been made in understanding laser powder bed fusion (L-PBF) as well as the fabrication of various materials using this technology, there is still limited adoption in the industry. One of the key obstacles identified is the lack of materials that can truly manufacture functional parts directly with L-PBF. This paper covers the emerging research on in-situ alloying and multi-metal processing. A comprehensive overview of the underlying scientific topics behind them is presented. The current state of research and progress from different perspectives (the materials and L-PBF processing parameters) are reviewed in order to provide a basis for follow-up research and development of these approaches. Defects, especially those associated with these two material processing routes, are also elucidated by discussing the mechanisms of their formation, including the main influencing factors, and the tendency for them to occur. Future research trends and potential topics are illustrated. The final part of this paper summarizes findings from this review and outlines the possibility of in-situ alloying and multi-metal processing using L-PBF. National Research Foundation (NRF) Accepted version

Additive manufacturing (AM) technique can help reduce time, cost, and complexity of the manufactured parts apart from adding functionality. This work explores AM techniques, namely fused deposition modeling (FDM) and PolyJet printing to fabricate a free-form structure with embedded electrical components. Both additive manufacturing technologies are discussed, analyzed, and compared. Another additive manufacturing method is then employed to print the electronic circuitry inside to connect the components. High-resolution X-ray computed tomography (CT) is employed to investigate the dimensional accuracy of the printed parts. This work demonstrates an innovative approach to construct arbitrary 3D objects with fully functional electronic circuits. NRF (Natl Research Foundation, S’pore)

A novel custom-made 3D silicone printer and two-part Ecoflex silicone resins were used to 3D-print standard-shaped silicone coupon and irregular-shaped meniscus structures via a heat-cured extrusion-based method. This article is segmented into three parts: (1) study on the effect of 3D printing parameters on dimensional accuracy and mechanical properties of 3D-printed silicone, (2) reliability and failure analysis of 3D-printed silicone according to ASTM D575 standards under monotonic and cyclic compressive loading, and (3) cytotoxicity of 3D-printed silicone by extraction method according to ISO 10993-12 for different extraction time and extract volume/surface area ratios. Based on analysis using regression method and analysis of variance, we found that the dimensional accuracy of lengths and widths is sensitive to both nozzle diameters and bed temperatures (BTs), while the height is only sensitive to BTs. Failure results were analyzed using the two-parameter Weibull probability distribution model and Weibull regression analysis and revealed that the Weibull modulus had a value greater than 1 in all groups, indicating an increasing failure rate with time for Ecoflex 30 and 50 meniscus implants. Results from quantitative cell proliferative assay exhibit statistically insignificant differences for all samples, pointing to the low cytotoxicity and excellent biocompatibility of printed silicone. Accepted version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its Medium-Sized Centre funding scheme and by the NTU StartUp Grant.

The complete mechanical properties (tensile, compressive, and shear properties) of the additively manufactured (AM) continuous carbon fiber-reinforced thermoplastic (CFRTP) fabricated using extrusion-based AM technique were investigated and reported. The fracture modes of the AM CFRTP in various mechanical tests were studied and reported. Anisotropic mechanical properties were observed in all mechanical tests, with the fiber direction having the highest strengths and stiffnesses and the across-the-layer direction having the lowest strengths and stiffnesses. A proof of concept topology optimized unmanned aerial vehicle (UAV) landing gear was designed and fabricated using the mechanical properties obtained experimentally. Finite element analysis and compressive tests conducted show that the UAV landing gear structure fabricated using the AM CFRTP was able to survive the most extreme condition during operation. National Research Foundation (NRF) Accepted version The authors would like to express their appreciation to Nanyang Technological University (NTU) High Performance Computing Centre (HPCC) for providing all the necessary supercomputing resources and support. This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Medium-Sized Centre funding scheme.

Additive manufacturing (AM) has brought about a revolution in the way we can manufacture complex products with customized features. AM has paved its way in the application areas ranging from aerospace, automotive, consumer to biomedical. AM of composites has attracted special attention due to its promise in improving, modifying, and diversifying the properties of generic materials through introducing reinforcements. This review provides a detailed landscape of fiber‐reinforced composites processed via AM techniques. Different AM processes, various material formulations, and strengths and drawbacks of AM methods are discussed. Emphasis is paid to AM techniques focusing on continuous fibers, as they hold the promise of becoming the next‐generation composite fabrication methodology. The article also tries to identify the potential of AM technology for fiber‐reinforced composites and delves into challenges facing the area. NRF (Natl Research Foundation, S’pore) Accepted version