We address the problem of object arrangement and scheduling for sequential 3D printing. Unlike the standard 3D printing, where all objects are printed slice by slice at once, in sequential 3D printing, objects are completed one after other. In the sequential case, it is necessary to ensure that the moving parts of the printer do not collide with previously printed objects. We look at the sequential printing problem from the perspective of combinatorial optimization. We propose to express the problem as a linear arithmetic formula, which is then solved using a solver for satisfiability modulo theories (SMT). However, we do not solve the formula expressing the problem of object arrangement and scheduling directly, but we have proposed a technique inspired by counterexample guided abstraction refinement (CEGAR), which turned out to be a key innovation to efficiency.

-- Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly di fficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers. I ntroduction Robots are being used to automate tasks previously performed by humans, with robot hands playing a particularly important role. In a social implementation, changing hands according to the task is problematic in terms of implementation cost. However, a robot hand that can perform many tasks with a single hand has advantages such as greatly reducing the cost of introduction and contributing greatly to the realization of an automated society. Most tools in society are made to fit human hands, so the human mimetic robot hand can be implemented in society without the use of special tools.

The integration of deep generative networks into generating Computer-Aided Design (CAD) models has garnered increasing attention over recent years. Traditional methods often rely on discrete sequences of parametric line/curve segments to represent sketches. Differently, we introduce RECAD, a novel framework that generates Raster sketches and 3D E xtrusions for CAD models. Representing sketches as raster images offers several advantages over discrete sequences: 1) it breaks the limitations on the types and numbers of lines/curves, providing enhanced geometric representation capabilities; 2) it enables interpolation within a continuous latent space; and 3) it allows for more intuitive user control over the output. Technically, RECAD employs two diffusion networks: the first network generates extrusion boxes conditioned on the number and types of extrusions, while the second network produces sketch images conditioned on these extrusion boxes. By combining these two networks, RECAD effectively generates sketch-and-extrude CAD models, offering a more robust and intuitive approach to CAD model generation. Experimental results indicate that RECAD achieves strong performance in unconditional generation, while also demonstrating effectiveness in conditional generation and output editing. Introduction The digital genesis of modern artifacts, from everyday consumer products to complex industrial machinery, is now deeply intertwined with Computer-Aided Design (CAD) systems. Central to many CAD workflows is sketch-based modeling, where 2D sketches imbued with geometric constraints and design intent are transformed into intricate 3D models through a series of feature-based modeling operations, ultimately giving rise to complex assemblies. Among these feature-based modeling operations, extrusion is the most prevalent, allowing designers to generate 3D shapes by extending 2D sketches along a defined path.

Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

A novel autonomous chunk-based aerial additive manufacturing framework is presented, supported with experimental demonstration advancing aerial 3D printing. An optimization-based decomposition algorithm transforms structures into sub-components, or chunks, treated as individual tasks coordinated via a dependency graph, ensuring sequential assignment to UA Vs considering inter-dependencies and printability constraints for seamless execution. A specially designed hexacopter equipped with a pressurized canister for lightweight expandable foam extrusion is utilized to deposit the material in a controlled manner. To further enhance precise execution of the printing, an offset-free Model Predictive Control mechanism is considered compensating reactively for disturbances and ground effect during execution. Additionally, an interlocking mechanism is introduced in the chunking process to enhance structural cohesion and improve layer adhesion. Extensive experiments demonstrate the framework's effectiveness in constructing precise structures of various shapes, while seamlessly adapting to practical challenges, proving its potential for a transformative leap in aerial robotic capability for autonomous construction. A video with the overall demonstration can be found here: https://youtu.be/WC1rLMLKEg4. Preprint submitted to Journal of Automation In Construction February 27, 2025 1. Introduction In recent times, ground breaking advancement in additive manufacturing, seamlessly integrated with autonomous robotics, are unlocking an exciting frontier in next generation construction and manufacturing process. Additive manufacturing has demonstrated a paradigm shift impact, addressing complex manufacturing processes with unprecedented precision and efficiency. Its transformative potential is becoming increasingly evident as it evolves and finds applications across a wide range of industries [1, 2, 3], while simultaneously paving the way for further innovations in the future. An intriguing development is its recent integration into the construction industry, capitalizing on its ability to automate construction processes, provide extensive design flexibility, and construct intricate structures designed using Computer-Aided Design (CAD) software [4, 5]. Numerous studies have demonstrated the design and deployment of large-scale robotic arms and gantry systems for printing building components and even entire houses using a variety of base materials [6]. A key advantage of such methods is their ability to adapt with high level of automation throughout the construction process, making them particularly well-suited for deployment in remote, inaccessible, and harsh environments[7, 8]. Notable examples include disaster-stricken areas, such as regions impacted by fires and earthquakes, where the rapid construction of shelters and basic infrastructure is imperative.

As robotics advances toward integrating soft structures, anthropomorphic shapes, and complex tasks, soft and highly stretchable mechanotransducers are becoming essential. To reliably measure tactile and proprioceptive data while ensuring shape conformability, stretchability, and adaptability, researchers have explored diverse transduction principles alongside scalable and versatile manufacturing techniques. Nonetheless, many current methods for stretchable sensors are designed to produce a single sensor configuration, thereby limiting design flexibility. Here, we present an accessible, flexible, printing-based fabrication approach for customizable, stretchable sensors. Our method employs a custom-built printhead integrated with a commercial 3D printer to enable direct ink writing (DIW) of conductive ink onto cured silicone substrates. A layer-wise fabrication process, facilitated by stackable trays, allows for the deposition of multiple liquid conductive ink layers within a silicone matrix. To demonstrate the method's capacity for high design flexibility, we fabricate and evaluate both capacitive and resistive strain sensor morphologies. Experimental characterization showed that the capacitive strain sensor possesses high linearity (R^2 = 0.99), high sensitivity near the 1.0 theoretical limit (GF = 0.95), minimal hysteresis (DH = 1.36%), and large stretchability (550%), comparable to state-of-the-art stretchable strain sensors reported in the literature.

Robot-assisted feeding systems enhance the independence of individuals with motor impairments and alleviate caregiver burden. While existing systems predominantly rely on software-based safety features to mitigate risks during unforeseen collisions, this study explores the use of a mechanical fail-safe to improve safety. We designed a breakaway utensil attachment that decouples forces exerted by the robot on the user when excessive forces occur. Finite element analysis (FEA) simulations were performed to predict failure points under various loading conditions, followed by experimental validation using 3D-printed attachments with variations in slot depth and wall loops. To facilitate testing, a drop test rig was developed and validated. Our results demonstrated a consistent failure point at the slot of the attachment, with a slot depth of 1 mm and three wall loops achieving failure at the target force of 65 N. Additionally, the parameters can be tailored to customize the breakaway force based on user-specific factors, such as comfort and pain tolerance. CAD files and utensil assembly instructions can be found here: https://tinyurl.com/rfa-utensil-attachment

This paper introduces a data-driven algorithm for modeling and compensating shape deviations in additive manufacturing (AM), addressing challenges in geometric accuracy and batch production. While traditional methods, such as analytical models and metrology, laid the groundwork for geometric precision, they are often impractical for large-scale production. Recent advancements in machine learning (ML) have improved compensation precision, but issues remain in generalizing across complex geometries and adapting to position-dependent variations. We present a novel approach for powder bed fusion (PBF) processes, using GraphCompNet, which is a computational framework combining graph-based neural networks with a generative adversarial network (GAN)-inspired training process. By leveraging point cloud data and dynamic graph convolutional neural networks (DGCNNs), GraphCompNet models complex shapes and incorporates position-specific thermal and mechanical factors. A two-stage adversarial training procedure iteratively refines compensated designs via a compensator-predictor architecture, offering real-time feedback and optimization. Experimental validation across diverse shapes and positions shows the framework significantly improves compensation accuracy (35 to 65 percent) across the entire print space, adapting to position-dependent variations. This work advances the development of Digital Twin technology for AM, enabling scalable, real-time monitoring and compensation, and addressing critical gaps in AM process control. The proposed method supports high-precision, automated industrial-scale design and manufacturing systems.

Conventional predictive modeling of parametric relationships in manufacturing processes is limited by the subjectivity of human expertise and intuition on the one hand and by the cost and time of experimental data generation on the other hand. This work addresses this issue by establishing a new Large Language Model (LLM) framework. The novelty lies in combining automatic extraction of process-relevant knowledge embedded in the literature with iterative model refinement based on a small amount of experimental data. This approach is evaluated on three distinct manufacturing processes that are based on machining, deformation, and additive principles. The results show that for the same small experimental data budget the models derived by our framework have unexpectedly high extrapolative performance, often surpassing the capabilities of conventional Machine Learning. Further, our approach eliminates manual generation of initial models or expertise-dependent interpretation of the literature. The results also reveal the importance of the nature of the knowledge extracted from the literature and the significance of both the knowledge extraction and model refinement components.

This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based additive manufacturing promises flexible and autonomous material deposition in large-scale or hazardous environments. However, achieving robust real-time control of a multi-rotor aerial robot under varying payloads and potential disturbances remains challenging. Traditional controllers like PID often require frequent parameter re-tuning, limiting their applicability in dynamic scenarios. We propose a DRL framework that learns adaptable control policies for multi-rotor drones performing waypoint navigation in AM tasks. We compare Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) within a curriculum learning scheme designed to handle increasing complexity. Our experiments show TD3 consistently balances training stability, accuracy, and success, particularly when mass variability is introduced. These findings provide a scalable path toward robust, autonomous drone control in additive manufacturing.