Human fingers achieve exceptional dexterity and adaptability by combining structures with varying stiffness levels, from soft tissues (low) to tendons and cartilage (medium) to bones (high). This paper explores developing a robotic finger with similar multi-stiffness characteristics. Specifically, we propose using a lattice configuration, parameterized by voxel size and unit cell geometry, to optimize and achieve fine-tuned stiffness properties with high granularity. A significant advantage of this approach is the feasibility of 3D printing the designs in a single process, eliminating the need for manual assembly of elements with differing stiffness. Based on this method, we present a novel, human-like finger, and a soft gripper. We integrate the latter with a rigid manipulator and demonstrate the effectiveness in pick and place tasks.

Lattice structures have great potential for several application fields ranging from medical and tissue engineering to aeronautical one. Their development is further speeded up by the continuing advances in additive manufacturing technologies that allow to overcome issues typical of standard processes and to propose tailored designs. However, the design of lattice structures is still challenging since their properties are considerably affected by numerous factors. The present paper aims to propose, discuss, and compare various modeling approaches to describe, understand, and predict the correlations between the mechanical properties and the void volume fraction of different types of lattice structures fabricated by fused deposition modeling 3D printing. Particularly, four approaches are proposed: (i) a simplified analytical model; (ii) a semi-empirical model combining analytical equations with experimental correction factors; (iii) an artificial neural network trained on experimental data; (iv) numerical simulations by finite element analyses. The comparison among the various approaches, and with experimental data, allows to identify the performances, advantages, and disadvantages of each approach, thus giving important guidelines for choosing the right design methodology based on the needs and available data.

We present a method to fabricate double shell structures printed in trans-versal directions using multi-axis fused-deposition-modeling (FDM) robot-ic 3D printing. Shell structures, characterized by lightweight, thin walls, fast buildup, and minimal material usage, find diverse applications in pro-totyping and architecture for uses such as fa\c{c}ade panels, molds for concrete casting, or full-scale pavilions. We leverage an underlying representation of transversal strip networks generated using existing methods and propose a methodology for converting them into printable partitions. Each partition is printed separately and assembled into a double-shell structure. We out-line the specifications and workflow that make the printing of each piece and the subsequent assembly process feasible. The versatility and robust-ness of our method are demonstrated with both digital and fabricated re-sults on surfaces of different scales and geometric complexity.

Aerial Manipulators (AMs) provide a versatile platform for various applications, including 3D printing, architecture, and aerial grasping missions. However, their operational speed is often sacrificed to uphold precision. Existing control strategies for AMs often regard the manipulator as a disturbance and employ robust control methods to mitigate its influence. This research focuses on elevating the precision of the end-effector and enhancing the agility of aerial manipulator movements. We present a composite control scheme to address these challenges. Initially, a Nonlinear Disturbance Observer (NDOB) is utilized to compensate for internal coupling effects and external disturbances. Subsequently, manipulator dynamics are processed through a high pass filter to facilitate agile movements. By integrating the proposed control method into a fully autonomous delta-arm-based AM system, we substantiate the controller's efficacy through extensive real-world experiments. The outcomes illustrate that the end-effector can achieve accuracy at the millimeter level.

Miniature underwater robots play a crucial role in the exploration and development of marine resources, particularly in confined spaces and high-pressure deep-sea environments. This study presents the design, optimization, and performance of a miniature robotic fish, powered by the oscillation of bio-inspired fins. These fins feature a rigid-flexible hybrid structure and use an eccentric rotating mass (ERM) vibration motor as the excitation source to generate high-frequency unidirectional oscillations that induce acoustic streaming for propulsion. The drive mechanism, powered by miniature ERM vibration motors, eliminates the need for complex mechanical drive systems, enabling complete isolation of the entire drive system from the external environment and facilitating the miniaturization of the robotic fish. A compact, untethered robotic fish, measuring 85*60*45 mm^3, is equipped with three bio-inspired fins located at the pectoral and caudal positions. Experimental results demonstrate that the robotic fish achieves a maximum forward swimming speed of 1.36 body lengths (BL) per second powered by all fins and minimum turning radius of 0.6 BL when powered by a single fin. These results underscore the significance of employing the ERM vibration motor in advancing the development of highly maneuverable, miniature untethered underwater robots for various marine exploration tasks.

Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analytical properties of the closed form model, in particular sensitivity analysis, to quantify the response of the model predictions to variations in parameters. Techniques developed to analyze data, specifically manifold learning methods, have the potential to complement, and even extend the scope of the traditional analytical approaches. We report on a study comparing and contrasting analytical and data-driven approaches to quantify parameter identifiability and, importantly, perform parameter reduction tasks. We use the infinite bus synchronous generator model, a well-understood model from the power systems domain, as our benchmark problem. Our traditional analysis methods use the Fisher Information Matrix to quantify parameter identifiability analysis, and the Manifold Boundary Approximation Method to perform parameter reduction. We compare these results to those arrived at through data-driven manifold learning schemes: Output - Diffusion Maps and Geometric Harmonics. For our test case, we find that the two suites of tools (analytical when a model is explicitly available, as well as data-driven when the model is lacking and only measurement data are available) give (correct) comparable results; these results are also in agreement with traditional analysis based on singular perturbation theory. We then discuss the prospects of using data-driven methods for such model analysis.

In this paper, we present a concurrent and scalable trajectory optimization method to improve the quality of robot-assisted manufacturing. Our method simultaneously optimizes tool orientations, kinematic redundancy, and waypoint timing on input toolpaths with large numbers of waypoints to improve kinematic smoothness while incorporating manufacturing constraints. Differently, existing methods always determine them in a decoupled manner. To deal with the large number of waypoints on a toolpath, we propose a decomposition-based numerical scheme to optimize the trajectory in an out-of-core manner, which can also run in parallel to improve the efficiency. Simulations and physical experiments have been conducted to demonstrate the performance of our method in examples of robot-assisted additive manufacturing.

Industrial X-ray computed tomography (XCT) is a powerful tool for non-destructive characterization of materials and manufactured components. XCT commonly accompanied by advanced image analysis and computer vision algorithms to extract relevant information from the images. Traditional computer vision models often struggle due to noise, resolution variability, and complex internal structures, particularly in scientific imaging applications. State-of-the-art foundational models, like the Segment Anything Model (SAM)-designed for general-purpose image segmentation-have revolutionized image segmentation across various domains, yet their application in specialized fields like materials science remains under-explored. In this work, we explore the application and limitations of SAM for industrial X-ray CT inspection of additive manufacturing components. We demonstrate that while SAM shows promise, it struggles with out-of-distribution data, multiclass segmentation, and computational efficiency during fine-tuning. To address these issues, we propose a fine-tuning strategy utilizing parameter-efficient techniques, specifically Conv-LoRa, to adapt SAM for material-specific datasets. Additionally, we leverage generative adversarial network (GAN)-generated data to enhance the training process and improve the model's segmentation performance on complex X-ray CT data. Our experimental results highlight the importance of tailored segmentation models for accurate inspection, showing that fine-tuning SAM on domain-specific scientific imaging data significantly improves performance. However, despite improvements, the model's ability to generalize across diverse datasets remains limited, highlighting the need for further research into robust, scalable solutions for domain-specific segmentation tasks.

The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their high bandwidth and capability to detect subtle anomalies that other sensors might have less sensitivity. In this study, we investigate malfunctioning industrial machines to evaluate and compare anomaly detection performance across different machine types and fault conditions. Log-Mel spectrograms of machinery sound are used as input, and the performance is evaluated using the area under the curve (AUC) score for two different methods: baseline dense autoencoder (AE) and one-class deep Support Vector Data Description (deep SVDD) with different subspace dimensions. Our results over the MIMII sound dataset demonstrate that the deep SVDD method with a subspace dimension of 2 provides superior anomaly detection performance, achieving average AUC scores of 0.84, 0.80, and 0.69 for 6 dB, 0 dB, and -6 dB signal-to-noise ratios (SNRs), respectively, compared to 0.82, 0.72, and 0.64 for the baseline model. Moreover, deep SVDD requires 7.4 times fewer trainable parameters than the baseline dense AE, emphasizing its advantage in both effectiveness and computational efficiency.

Fused Deposition Modeling (FDM) is a widely used additive manufacturing (AM) technique valued for its flexibility and cost-efficiency, with applications in a variety of industries including healthcare and aerospace. Recent developments have made affordable FDM machines accessible and encouraged adoption among diverse users. However, the design, planning, and production process in FDM require specialized interdisciplinary knowledge. Managing the complex parameters and resolving print defects in FDM remain challenging. These technical complexities form the most critical barrier preventing individuals without technical backgrounds and even professional engineers without training in other domains from participating in AM design and manufacturing. Large Language Models (LLMs), with their advanced capabilities in text and code processing, offer the potential for addressing these challenges in FDM. However, existing research on LLM applications in this field is limited, typically focusing on specific use cases without providing comprehensive evaluations across multiple models and tasks. To this end, we introduce FDM-Bench, a benchmark dataset designed to evaluate LLMs on FDM-specific tasks. FDM-Bench enables a thorough assessment by including user queries across various experience levels and G-code samples that represent a range of anomalies. We evaluate two closed-source models (GPT-4o and Claude 3.5 Sonnet) and two open-source models (Llama-3.1-70B and Llama-3.1-405B) on FDM-Bench. A panel of FDM experts assess the models' responses to user queries in detail. Results indicate that closed-source models generally outperform open-source models in G-code anomaly detection, whereas Llama-3.1-405B demonstrates a slight advantage over other models in responding to user queries. These findings underscore FDM-Bench's potential as a foundational tool for advancing research on LLM capabilities in FDM.