https://cronfa.swan.ac.uk/Record/cronfa58325/Download/58325__21192__21346aaca01443e3a1601827f9c23cb0.pdf

The shape – morphing performance of magnetoactive soft materials

The idea of four-dimensional (4D) printing is the formation of intricate stimuli-responsive 3D architectures that transform into different forms and shapes upon exposure to environmental stimuli. 4D printing (4DP) of smart/intelligent materials is a promising and novel approach to generate intricate structures for biomedical, food, electronics, textile, and agricultural fields. Nowadays, soft robotics is a growing research field focusing on developing micro/nanoscale 4D-printed robots using intelligent materials. Herein, recent advancements in 4DP of soft robotics, actuators, and grippers are summarized. This review also highlights some recent developments in novel robotics technologies and materials including multi-material printing, electro-, and magneto-active soft materials (MASMs), and metamaterials. It also sheds light on different modeling mechanisms including numerical models and machine learning (ML) models for fabricating highly precise and efficient micro/macro-scaled robots. The applications of shape-memory polymers (SMPs), hydrogels, and liquid crystal elastomers (LCEs)-based 4D-printed soft and intelligent robots in different engineering fields are highlighted. Lastly, this review incorporates current challenges which are hindering the actual utilization of 4D-printed soft robotics and their possible remedies. 

4D printing: Technological developments in robotics applications

4D printing: Pragmatic progression in biofabrication

Bone tissue engineering uses various methods and materials to find suitable scaffolds that regenerate lost bone due to disease or injury. Poly(ε-caprolactone) (PCL) can be used in 3D printing for producing biodegradable scaffolds by fused deposition modeling (FDM). However, the hydrophobic surfaces of PCL and its non-osteogenic nature reduces adhesion and cell bioactivity at the time of implantation. This work aims to enhance bone formation, osteogenic differentiation, and in vitro biocompatibility via PCL scaffolds modification with Hydroxyapatite (HA) and Collagen type I (COL). This study evaluated the osteosupportive capacity, biological behavior, and physicochemical properties of 3D-printed PCL, PCL/HA, PCL/COL, and PCL/HA/COL scaffolds. Biocompatibility and cells proliferation were investigated by seeding human adipose tissue-derived mesenchymal stem cells (hADSCs) onto the scaffolds, which were analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay, and 6-diamidino-2-phenylindole (DAPI) staining. In addition, the bone differentiation potential of the hADSCs was assessed using calcium deposition, alkaline phosphatase (ALP) activity, and bone-related protein and genes. Although all constructed scaffolds support hADSCs proliferation and differentiation, the results showed that scaffold coating with HA and COL can boost these capacities in a synergistic manner. According to the findings, the tricomponent 3D-printed scaffold can be considered as a promising choice for bone tissue regeneration and rebuilding. 

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Enhanced osteogenic differentiation of stem cells by 3D printed PCL scaffolds coated with collagen and hydroxyapatite

ABSTRACT Inspired by the vast amounts of investigations carried out on three-dimensional (3D) printed structures and their recent accelerated developments, the present review paper comprehensively describes the current trends as well as promising findings of 3D printed energy absorbing structures (EAS) for crashworthiness application. Particular attention is paid to the mechanical behaviour and crushing performance of 3D printed EAS. The main 3D printing technological processes, their material feedstocks choices and unique structural designs, investigated recently, are discussed in detail. Deformation modes obtained by 3D printed EAS under different loading conditions are identified. Additionally, salient suggestions with future realisation of complex 3D printed EAS are provided. This review will serve as a springboard to propel the technological advancement of additively manufactured EAS incorporated into moving vehicles and utilised as protective devices. Hence, setting the goals to encourage novel research that guarantees the efficient protection of lives and valuables during mild and catastrophic impacts.

https://www.tandfonline.com/doi/pdf/10.1080/17452759.2022.2074698?needAccess=true

Current trends in additively manufactured (3D printed) energy absorbing structures for crashworthiness application – a review

Additive manufacturing (AM) or 3D printing is a digital manufacturing process and offers virtually limitless opportunities to develop structures/objects by tailoring material composition, processing conditions, and geometry technically at every point in an object. In this review, we present three different early adopted, however, widely used, polymer-based 3D printing processes; fused deposition modelling (FDM), selective laser sintering (SLS), and stereolithography (SLA) to create polymeric parts. The main aim of this review is to offer a comparative overview by correlating polymer material-process-properties for three different 3D printing techniques. Moreover, the advanced material-process requirements towards 4D printing via these print methods taking an example of magneto-active polymers is covered. Overall, this review highlights different aspects of these printing methods and serves as a guide to select a suitable print material and 3D print technique for the targeted polymeric material-based applications and also discusses the implementation practices towards 4D printing of polymer-based systems with a current state-of-the-art approach.

3D/4D Printing of Polymers: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA).

Convolutional Neural Networks are deep learning algorithms that are typically used for the classification of images. A black and white image can be represented as the number of rows and columns along with values of intensity for each of components of these rows and columns. Vibration measured with the help of accelerometer can be used to create an image like data. These data can be used for machine learning applications with the help of deep learning. In deep learning techniques, features do not have to be extracted from the data set. The CNN network can generate a feature-like set which can be further classified with a fully connected neural network to give out the probability over the predefined class. The neural networks model is trained with a data set created in laboratory conditions to monitor the unfractured and single blade fracture condition operation state. The paper deals with showing the proof of concept that CNN algorithms can be applied for conditional monitoring of runner blades. The paper mainly focuses on demonstrating that deep learning algorithms can be used as analysis tools for hydropower runners by applying these techniques in scaled versions of 3D printed runners.

Vibration Analysis of 3D printed runner with CNN for using deep learning in hydropower for condition monitoring

Human Gait Analysis is crucial in diagnosis, monitoring, treatment, and rehabilitation. It requires the measurement of gait parameters like joint angles, angular motion, ground reaction force (GRF), momentum, step width, step length, velocity, etc. This study introduces a markerless, easy-to-use approach for measuring one of the gait parameters i.e., knee flexion/extension angle, and verifies it with an existing standard marker-based approach. Knee flexion/extension angle is calculated via Machine Learning (ML) pose estimation model “MediaPipe Pose” and Computer vision (CV) without the use of markers. For the verification of accuracy, the obtained values are compared with the data on each video frame obtained from Kinovea® which is a marker-based motion analysis software. A correlation of 0.941 was observed between the results from Kinovea® and MediaPipe Pose. Similarly, the mean absolute error of knee angles was 5.88 degrees. The research shows that knee flexion/extension angle can be accurately measured using ML solution for high-fidelity body pose tracking. Professionals involved in the field of biomechanics, sports medicine, physiotherapy as well as other medical fields can use this method as an alternative markerless approach for knee flexion/extension angle measurement for gait analysis.

Knee Flexion/Extension Angle Measurement for Gait Analysis Using Machine Learning Solution “MediaPipe Pose” and Its Comparison with Kinovea®

Entangled by a web-like pattern of rivers and rivulets, Nepal’s energy sector currently is dominated by hydroelectricity which sustains a major portion of the country’s economy. But still, after more than a hundred years of its operation, the sector’s dependence on imports is uncanny. This not only upsizes the project cost but also the time and human resources. The research, therefore, aims to aid the cause by examining the prospect of manufacturing runners using local industrial expertise in synchronization with available data and research. Here, the paper focuses on the sand casting of a 750KW Francis runner and the design of its parameters namely gating and feeding system to ensure directional solidification. Apart from the conventional procedure, the casting of the runner was proceeded as three separate parts: hub, shroud, and blade which were later welded together. The gate and riser placement for each of the parts was designed independently and analysed for any shrinkage porosities and possible voids using computer simulation with several iterations each. Specifically, temperature variations during three major stages of solidification assigned as 0, 50, and 100 were tested for a total of 195 nodes. The design was then transferred into the mould and casting was performed. The procedure resulted in a cast with a clean surface finish and minimal visible defects. The study, thus, suggests the possibility of manufacturing runners through metal casting and its substantial implications in the improvement of the hydropower industry in Nepal.

Determination of Casting Parameters for Affirmative Directional Solidification in 750 KW Francis Runner

Turbine having complex profiles has always been a challenge to the manufacturers. With possible errors while manufacturing and surface finishing, it is nearly impossible attaining the same profile of blades or buckets as suggested by the hydraulic design. Specifically in the case of Nepal the manufacturing of Francis turbine runners has been a challenge due to its complex profile and lack of state-of-the-art manufacturing facilities in the Nepalese metal manufacturing industries. Many attempts have been made in the past to manufacture these runners with the available resources. However, the developed turbines have been of smaller size and dimensions and the process used for fabrication have been limited to these small runners. So, for fabrication of the Francis turbine runners a new process has been developed. The process makes use of a rapid prototyping technique called additive manufacturing. The developed method has made the manufacturers easier to manufacture the runners of complex profiles and larger size. The steps from modifications of the cad model to 3D printing and workshop fabrication has been discussed in this paper.

Raman Silwal

Papers

3D/4D Printing of Polymers: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA).

Vibration Analysis of 3D printed runner with CNN for using deep learning in hydropower for condition monitoring

Knee Flexion/Extension Angle Measurement for Gait Analysis Using Machine Learning Solution “MediaPipe Pose” and Its Comparison with Kinovea®

Determination of Casting Parameters for Affirmative Directional Solidification in 750 KW Francis Runner

Use of 3D Printing technology for developing novel procedure to manufacture runner of Francis Turbine