This article introduces an experimental emulation of a novel chunk-based flexible multi-DoF aerial 3D printing framework. The experimental demonstration of the overall autonomy focuses on precise motion planning and task allocation for a UAV, traversing through a series of planned space-filling paths involved in the aerial 3D printing process without physically depositing the overlaying material. The flexible multi-DoF aerial 3D printing is a newly developed framework and has the potential to strategically distribute the envisioned 3D model to be printed into small, manageable chunks suitable for distributed 3D printing. Moreover, by harnessing the dexterous flexibility due to the 6 DoF motion of UAV, the framework enables the provision of integrating the overall autonomy stack, potentially opening up an entirely new frontier in additive manufacturing. However, it's essential to note that the feasibility of this pioneering concept is still in its very early stage of development, which yet needs to be experimentally verified. Towards this direction, experimental emulation serves as the crucial stepping stone, providing a pseudo mockup scenario by virtual material deposition, helping to identify technological gaps from simulation to reality. Experimental emulation results, supported by critical analysis and discussion, lay the foundation for addressing the technological and research challenges to significantly push the boundaries of the state-of-the-art 3D printing mechanism.

Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structure can help increase the spatial resolution of MagicTac to perceive external interaction contacts. Moreover, the sensor is produced using the multi-material additive manufacturing technique, which simplifies the manufacturing process while ensuring repeatability of production. Compared to traditional vision-based tactile sensors, it offers the advantages of i) high spatial resolution, ii) significant affordability, and iii) fabrication-friendly construction that requires minimal assembly skills. We evaluated the proposed MagicTac in the tactile reconstruction task using the deformation field and optical flow. Results indicated that MagicTac could capture fine textures and is sensitive to dynamic contact information. Through the grid-based multi-material additive manufacturing technique, the affordability and productivity of MagicTac can be enhanced with a minimum manufacturing cost of 4.76 GBP and a minimum manufacturing time of 24.6 minutes.

Fluidic logic circuitry analogous to its electric counterpart could potentially provide soft robots with machine intelligence due to its supreme adaptability, dexterity, and seamless compatibility using state-of-the-art additive manufacturing processes. However, conventional microfluidic channel based circuitry suffers from limited driving force, while macroscopic pneumatic logic lacks timely responsivity and desirable accuracy. Producing heavy duty, highly responsive and integrated fluidic soft robotic circuitry for control and actuation purposes for biomedical applications has yet to be accomplished in a hydraulic manner. Here, we present a 3D printed hydraulic fluidic half-adder system, composing of three basic hydraulic fluidic logic building blocks: AND, OR, and NOT gates. Furthermore, a hydraulic soft robotic half-adder system is implemented using an XOR operation and modified dual NOT gate system based on an electrical oscillator structure. This half-adder system possesses binary arithmetic capability as a key component of arithmetic logic unit in modern computers. With slight modifications, it can realize the control over three different directions of deformation of a three degree-offreedom soft actuation mechanism solely by changing the states of the two fluidic inputs. This hydraulic fluidic system utilizing a small number of inputs to control multiple distinct outputs, can alter the internal state of the circuit solely based on external inputs, holding significant promises for the development of microfluidics, fluidic logic, and intricate internal systems of untethered soft robots with machine intelligence. Introduction Soft robotics has emerged as a promising avenue for the development of adaptable, bioinspired robotic systems. At the heart of this emerging technology lies the ingenious concept of fluidic logic, which draws inspiration from the complex, yet highly efficient, hydraulic systems found in nature, such as the muscular hydrostats of cephalopods [7, 8] and the hydrostatic skeletons of worms for propulsion [9, 10]. One of the groundbreaking innovations in soft robotics is the integration of fluidic logic, a control mechanism that harnesses the flow of fluids, typically air or liquids, to control the movement and deformation of soft robotic structures in analogous to electric circuits.

Robotic hands are an important tool for replacing humans in handling toxic or radioactive materials. However, these are usually highly expensive, and in many cases, once they are contaminated, they cannot be re-used. Some solutions cope with this challenge by 3D printing parts of a tendon-based hand. However, fabrication requires additional assembly steps. Therefore, a novice user may have difficulties fabricating a hand upon contamination of the previous one. We propose the Print-N-Grip (PNG) hand which is a tendon-based underactuated mechanism able to adapt to the shape of objects. The hand is fabricated through one-shot 3D printing with no additional engineering effort, and can accommodate a number of fingers as desired by the practitioner. Due to its low cost, the PNG hand can easily be detached from a universal base for disposing upon contamination, and replaced by a newly printed one. In addition, the PNG hand is scalable such that one can effortlessly resize the computerized model and print. We present the design of the PNG hand along with experiments to show the capabilities and high durability of the hand.

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. Finally, the research proposes a DDIA localization method based on spatio-temporal graph neural networks. The accuracy and effectiveness of the DDIA model are rigorously demonstrated through comprehensive analytical cases.

Progress has been made to innovate printing materials and printing processes, in terms of building blocks, joining mechanisms, forms of control, and transformation methods. Typically, material forms for 3D printing include solid filaments, wires, liquid resins, powders, and sheets (1). These feedstocks are transformed into discrete building units (such as droplets and lines) and placed, deposited, or solidified at designated locations for layer-by-layer manufacturing. However, 3D printing of continuous and flexible tape (with the geometric form in between filaments and sheets) without breaking or transformation remains underexplored and challenging. In the composite manufacturing industry, carbon fiber prepreg tapes are widely used for placement, which is called automated tape placement/laying (ATP/ATL) (3). Such ATP systems generally use heat and pressure to consolidate the composite materials (4, 5). However, ATP/ATL systems are typically mounted with large-scale gantry systems or robotic arms (4, 6-8). Such approaches require high capital investment and complex heavy equipment, which is not easily accessible to general researchers and difficult to integrate with desktop-scale 3D printing technologies.

ABSTRACT Considering data insufficiency in metal additive manufacturing (AM), transfer learning (TL) has been adopted to extract knowledge from source domains (e.g., completed printings) to improve the modeling performance in target domains (e.g., new printings). Current applications use all accessible source data directly in TL with no regard to the similarity between source and target data. This paper proposes a systematic method to find appropriate subsets of source data based on similarities between the source and target datasets for a given set of limited target domain data. Such similarity is characterized by the spatial and model distance metrics. A Pareto frontier-based source data selection method is developed, where the source data located on the Pareto frontier defined by two similarity distance metrics are selected iteratively. The method is integrated into an instance-based TL method (decision tree regression model) and a model-based TL method (fine-tuned artificial neural network). Both models are then tested on several regression tasks in metal AM. Comparison results demonstrate that 1) the source data selection method is general and supports integration with various TL methods and distance metrics, 2) compared with using all source data, the proposed method can find a small subset of source data from the same domain with better TL performance in metal AM regression tasks involving different processes and machines, and 3) when multiple source domains exist, the source data selection method could find the subset from one source domain to obtain comparable or better TL performance than the model constructed using data from all source domains. Keywords: metal additive manufacturing, transfer learning, source data selection, Pareto frontier 1 Introduction Metal additive manufacturing (AM) fabricates parts by depositing metal materials layer by layer with various heat sources, e.g., the laser beam and electric arc. Although metal AM has been adopted in electronics (Pang et al. 2020), automotive (Vasco 2021), aerospace (Blakey-Milner et al. 2021), and other industries, low productivity and unstable quality are two drawbacks that restrict the applications of metal AM. To alleviate the two drawbacks, constructing data-driven models to reveal correlations among processes, structures, and properties has attracted attention in both industry and academia. These models are built based on collected data from experiments or simulations and adopted for process optimization, control, or monitoring to improve the quality of printed parts.

This paper proposes a framework that optimizes the linkage mechanism of the quasi-serial manipulator for target tasks. This process is explained through a case study of 2-degree-of-freedom linkage mechanisms, which significantly affect the workspace of the quasi-serial manipulator. First, a vast quasi-serial mechanism is generated with a workspace satisfying a target task and it converts it into a 3D CAD model. Then, the workspace and required torque performance of each mechanism are evaluated through kinematic and dynamic analysis. A deep learning-based surrogate model is leveraged to efficiently predict mechanisms and performance during the optimization process. After model training, a multi-objective optimization problem is formulated under the mechanical and dynamic conditions of the manipulator. The design goal of the manipulator is to recommend quasi-serial mechanisms with optimized kinematic (workspace) and dynamic (joint torque) performance that satisfies the target task. To investigate the underlying physics from the obtained Pareto solutions, various data mining techniques are performed to extract design rules that can provide practical design guidance. Finally, the manipulator was designed in detail for realization with 3D printed parts, including topology optimization. Also, the task-based optimized manipulator is verified through a payload test. Based on these results, the proposed framework has the potential for other real applications as realized cases and provides a reasonable design plan through the design rule extraction.

Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer precision, they are often time-consuming and unsuitable for real-time predictions and online control in iterative design scenarios. Conversely, machine learning models rely heavily on high-quality datasets, which can be costly and challenging to obtain within the metal AM domain. Our work addresses this by introducing a physics-informed neural network framework specifically designed for temperature field prediction in metal AM. This framework incorporates a physics-informed input, physics-informed loss function, and a Convolutional Long Short-Term Memory (ConvLSTM) architecture. Utilizing real-time temperature data from the process, our model predicts 2D temperature fields for future timestamps across diverse geometries, deposition patterns, and process parameters. We validate the proposed framework in two scenarios: full-field temperature prediction for a thin wall and 2D temperature field prediction for cylinder and cubic parts, demonstrating errors below 3% and 1%, respectively. Our proposed framework exhibits the flexibility to be applied across diverse scenarios with varying process parameters, geometries, and deposition patterns.

Soft Robots distinguish themselves from traditional robots by embracing flexible kinematics. Because of their recent emergence, there exist numerous uncharted territories, including novel actuators, manufacturing processes, and advanced control methods. This research is centred on the design, fabrication, and control of a pneumatic soft robot. The principal objective is to develop a modular soft robot featuring with multiple segments, each one of three degrees of freedom. This yields to tubular structure with five independent degrees of freedom, enabling motion across three spatial dimensions. Physical construction leverages tin-cured silicone and a wax casting method, refined through iterative processes. 3D-printed PLA moulds, filled with silicone, yield the desired model, while bladder-like structures, are formed within using solidified paraffin wax positive moulds. For control, an empirically fine-tuned open-loop system is adopted. The project culminates in rigorous testing bending ability and weight carrying capacity and possible applications are discussed.