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Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler–Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities. [ABSTRACT FROM AUTHOR]

Forte is an open-source library specialized in multireference electronic structure theories for molecular systems and the rapid prototyping of new methods. This paper gives an overview of the capabilities of Forte, its software architecture, and examples of applications enabled by the methods it implements. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) emerged as an integral part of machine learning, accelerating model development by enabling gradient-based optimization without explicit analytical derivatives. Recently, the benefits of AD and computing arbitrary-order derivatives with respect to any variable were also recognized in the field of quantum chemistry. In this work, we present dxtb—an open-source, fully differentiable framework for semiempirical extended tight-binding (xTB) methods. Developed entirely in Python and leveraging PyTorch for array operations, dxtb facilitates extensibility and rapid prototyping while maintaining computational efficiency. Through comprehensive code vectorization and optimization, we essentially reach the speed of compiled xTB programs for high-throughput calculations of small molecules. The excellent performance also scales to large systems, and batch operability yields additional benefits for execution on parallel hardware. In particular, energy evaluations are on par with existing programs, whereas the speed of automatically differentiated nuclear derivatives is only 2 to 5 times slower compared to their analytical counterparts. We showcase the utility of AD in dxtb by calculating various molecular and spectroscopic properties, highlighting its capacity to enhance and simplify such evaluations. Furthermore, the framework streamlines optimization tasks and offers seamless integration of semiempirical quantum chemistry in machine learning, paving the way for physics-inspired end-to-end differentiable models. Ultimately, dxtb aims to further advance the capabilities of semiempirical methods, providing an extensible foundation for future developments and hybrid machine learning applications. The framework is accessible at https://github.com/grimme-lab/dxtb. [ABSTRACT FROM AUTHOR]

Laser forming is a highly flexible and iterative contactless thermomechanical forming process that utilises a defocused laser beam to induce material shortening and bending. There is a high potential for the use of laser forming in rapid prototyping, small and unique batch production; however, to achieve that potential a certain level of automation is required. The presented work seeks to address this need of automation by proposing an end-to-end sheet-metal manufacturing system utilising laser forming and cutting, where the input for the system is a 3D CAD model and the output is a manufactured product. The contribution of the presented work is interconnected laser forming and cutting framework. The presented framework consists of subsystems for automatic extraction of geometrical information from a CAD model, process planning and generation of manufacturing information, scheduling of tasks and a feedback control loop for laser forming. The framework is experimentally validated and has shown consistent forming and cutting behaviour on two types of parts with variation in cutting features, bend angle and lengths of the bend's free edge. [ABSTRACT FROM AUTHOR]

The emerging paradigm of personalised bone repair embodies a transformative triad comprising bio-inspired design, digital fabrication, and the exploration of innovative materials. The increasing average age of the population, alongside the rising incidence of fractures associated with age-related conditions such as osteoporosis, necessitates the development of customised, efficient, and minimally invasive treatment modalities as alternatives to conventional methods (e.g., autografts, allografts, Ilizarov distraction, and bone fixators) typically employed to promote bone regeneration. A promising innovative technique involves the use of cellularised scaffolds incorporating mesenchymal stem cells (MSCs). The selection of materials—ranging from metals and ceramics to synthetic or natural bio-derived polymers—combined with a design inspired by natural sources (including bone, corals, algae, shells, silk, and plants) facilitates the replication of geometries, architectures, porosities, biodegradation capabilities, and mechanical properties conducive to physiological bone regeneration. To mimic internal structures and geometries for construct customisation, scaffolds can be designed using Computer-aided Design (CAD) and fabricated via 3D-printing techniques. This approach not only enables precise control over external shapes and internal architectures but also accommodates the use of diverse materials that improve biological performance and provide economic advantages. Finally, advanced numerical models are employed to simulate, analyse, and optimise the complex processes involved in personalised bone regeneration, with computational predictions validated against experimental data and in vivo studies to ascertain the model's ability to predict the recovery of bone shape and function. [ABSTRACT FROM AUTHOR]

In addition to anatomical loss, removal of the full or portion of the orbit results in a facial deformity and psychological distress for the patient. This article details a practical case of prosthetic rehabilitation using digital workflow for an orbital deformity caused by post-COVID-19 Mucor mycosis. The main goal of this case study was to create a maxillofacial orbital prosthesis that is well-retained, simple to use, and accurate in terms of appearance. The study addresses the problems involved in fabricating the orbital prosthesis, particularly the unique dimensions and form of the defect, replicating the natural skin tone, and accomplishing retention by the most prudent and patient-friendly approach. Through this article, a digitised algorithm, using photogrammetry technique for facial scan, is suggested for fabricating the prosthesis. [ABSTRACT FROM AUTHOR]

Investigations into the various properties of granular matter composed of particles with defined shapes have gained increasing attention. Additive manufacturing, with its freedom of shape and rapid prototyping capabilities, has significantly contributed to these studies. However, this technique may introduce defects in the manufactured particles, which can significantly affect the properties of granular materials. The extent of these defects on particles of different shapes is investigated here. Particles of various shapes (cube, octahedron, quatropod, stellated octahedron, tetrahedron, and tetrapod) were manufactured and subsequently imaged using micro-Computed Tomography. The surface roughness, solidity, and convexity of the particles were quantified. Discrete element simulations of granular bed porosity, utilizing both idealized and real particle shapes, were conducted with different surface mesh resolutions and frictional parameters. A clear influence of the manufacturing process on the packing properties of 3D printed particles was identified. This influence is not uniform across all shapes and is directly correlated with the particle convexity. For numerical simulations, a shape-dependent correction of particle density and surface characteristics are imperative for each shape under consideration, despite the fact that the particles were manufactured using the same technique and material. [ABSTRACT FROM AUTHOR]

Purpose: Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process. Design/methodology/approach: PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles. Findings: PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one. Originality/value: The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding. [ABSTRACT FROM AUTHOR]

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method's output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times. [ABSTRACT FROM AUTHOR]

To what extent does a firm's digital readiness influence its competence in implementing digital initiatives? This study employs a deep-learning-based dual-stage approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) to demonstrate and quantify this relationship. Data were sourced from a questionnaire survey involving 428 architecture, engineering and construction (AEC) firms in New Zealand. The PLS-SEM analysis confirmed the positive correlation between the digital readiness of an organization and its competence towards seven types of DT, including immersive technologies, sensing technology, robotics, 3D printing, digital fabrication, artificial intelligence and big data. The ANN analysis further quantified the importance of the investigated readiness indicators in influencing digital competence. The results highlighted four most significant readiness attributes influencing the digital competence of AEC firms: (1) organizational culture, (2) perception of the leadership team, (3) hardware & software systems and (4) strategy plans. The findings can serve as a baseline for developing effective change management strategies and contribute to reducing the digital divide within AEC organizations, facilitating the effectiveness of organizational digital transformation. [ABSTRACT FROM AUTHOR]

Objective: This study is aimed at detailing a technique for digitally fabricated fixed restoration for both immediate replacement after extraction and splinting of the adjacent mobile teeth. Clinical Considerations: Demand for a fixed restoration of the missing teeth in the mandibular anterior region is very common but sometimes problematic for dentists. It often happens that there is significant loosening of the remaining teeth adjacent to the missing tooth, which should be splinted. This case report describes a digitally fabricated fixed restoration for both replacing lost teeth and splinting periodontally compromised mobile teeth. With the CAD‐CAM technique, patients can receive the restoration immediately after the extraction. The digital design and fabrication of the restoration were elaborated using the restorative process of a patient with a mandibular anterior tooth to be extracted and multiple loose lower anterior teeth. The immediate restoration process and the pontic modification when the extraction socket had healed were also described. Conclusions: The digital process avoids the nudging of the loose tooth by the traditional impression technique, improves the accuracy of the restoration, and avoids undue stress on the loose tooth when the restoration is in place. In addition, this method allows the patient to obtain a fixed restoration immediately after extraction, and if damage occurs after the restoration has been cemented, such as fracture or debonding, a new restoration can be prepared before the patient's return visit. Clinical Significance: This minimally invasive restorative method achieved the fixation of loose teeth while restoring the missing teeth, but the success of the treatment needed long‐time observation. [ABSTRACT FROM AUTHOR]

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher‐order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non‐printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D‐printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible‐elastomer as the lattice and a stiff‐epoxy as the infill material is illustrated. [ABSTRACT FROM AUTHOR]

The constant improvement of stereolithography (SL) in terms of achievable resolution and printing time has sparked high expectations that SL will enable the rapid prototyping of truly microfluidic chips with features below 100 µm. However, most commercial high‐resolution stereolithography devices are based on Digital Light Processing (DLP) and thus sacrifice lateral printing size for resolution. Consequently, even 10 years after the advent of microstereolithography there is no commercialized 3D printing system that can effectively fulfill all the demands to replace soft lithography for microfluidic prototyping. In this work, for the first time, This study demonstrates that a commercial laser‐based stereolithography device is capable of manufacturing microfluidic chips with embedded channels smaller than 100 µm with a footprint of 7.24 × 0.3 cm2. A chip fabricated in poly(ethylene glycol) diacrylate (PEGDA) that can readily be used for fluid mixing, is presented in this study. This research shows that the accessibility of high‐resolution chips with footprints of several cm2, using laser‐based stereolithography, enables the manufacturing of truly microfluidic systems with high impact on prototyping and manufacturing. [ABSTRACT FROM AUTHOR]

Laser powder bed fusion (LPBF) is a rapid prototyping technology with high forming accuracy, which can print complex metallic components applied in the aerospace field. The aluminum alloy parts manufactured by LPBF can better achieve lightweight structures than other metallic materials due to the combination of materials and structure. However, many metallurgical defects such as spherification, porosity, coarse grain, and crack are more likely formed inside the printed components during the LPBF process, resulting in the decline of properties of the aluminum alloy. In order to solve the above-mentioned problems, researchers have conducted in-depth exploration to improve the mechanical properties of LPBF fabricated Al parts. In this paper, the methods to improve the forming quality of aluminum alloy are explored from the direction of regulation strategies for microstructure and properties. The current research status of LPBF-processed aluminum alloy is discussed from the aspects of process parameter optimization, alloying element modification, reinforcing phase addition, and post-treatment. Furthermore, the microstructure and properties of LPBF-processed aluminum alloy are systematically reviewed. [ABSTRACT FROM AUTHOR]

With the development of 3D metal printers for rapid prototyping and industrial component production, heightened attention was directed towards post-processing operations for achieving precise surface quality and geometrical tolerances for these components. This paper investigated the orthogonal cutting of multi-material 3D printed workpieces using a coated cutting tool through finite element simulation. The workpieces featured different horizontal and vertical arrangements of layers composed of aluminum 7075-T6 alloy (Al), stainless steel 316 low alloy (SS), and Ti6Al4V alloy (Ti). The study explored the impacts of multi-material composition, coating thickness, and the rake angle of the cutting tool on machining forces, stress distribution, temperature distribution, and chip formation geometry. The results revealed a bimodal chip morphology in the machining process of horizontally arranged SS layers combined with other alloys. The SS layer resulted in a relatively uniform chip formation, while layers with two other materials exhibited a serrated chip formation. In contrast, a discontinuous chip formed when combining Al and Ti materials, as well as in the horizontally arranged layers made of Al, SS, and Ti alloys. The cutting force increased by 2.26 times when cutting workpieces with the horizontal arrangement of SS and Al layers compared to those with a single Al material. For the horizontal and vertical arrangement of layers made of Al and SS, von Mises stress values over the edge of the coated cutting tool significantly increased where the tool contacted the SS layer. Additionally, the horizontal arrangement of layers made of Al and SS materials caused the coated cutting tool to exhibit an extensive temperature distribution, with the maximum recorded temperature reaching 1448 °K. Increasing coating thickness led to a decrease in maximum principal stress at the surface of the tool and a rise in temperature at the cutting edge of the insert. [ABSTRACT FROM AUTHOR]

This article presents a 3D model of a yellow hydrogen generation system that uses the electricity produced by a photovoltaic carport. The 3D models of all key system components were collected, and their characteristics were described. Based on the design of the 3D model of the photovoltaic carport, the amount of energy produced monthly was determined. These quantities were then applied to determine the production of low-emission hydrogen. In order to increase the amount of low-emission hydrogen produced, the usage of a stationary energy storage facility was proposed. The Metalog family of probability distributions was adopted to develop a strategic model for low-emission hydrogen production. The hydrogen economy of a company that uses small amounts of hydrogen can be based on such a model. The 3D modeling and calculations show that it is possible to design a compact low-emission hydrogen generation system using rapid prototyping tools, including the photovoltaic carport with an electrolyzer placed in the container and an energy storage facility. This is an effective solution for the climate and energy transition of companies with low hydrogen demand. In the analytical part, the Metalog probability distribution family was employed to determine the amount of monthly energy produced by 6.3 kWp photovoltaic systems located in two European countries: Poland and Italy. Calculating the probability of producing specific amounts of hydrogen in two European countries is an answer to a frequently asked question: In which European countries will the production of low-emission hydrogen from photovoltaic systems be the most profitable? As a result of the calculations, for the analyzed year 2023 in Poland and Italy, specific answers were obtained regarding the probability of monthly energy generation and monthly hydrogen production. Many companies from Poland and Italy are taking part in the European competition to create hydrogen banks. Only those that offer low-emission hydrogen at the lowest prices will receive EU funding. [ABSTRACT FROM AUTHOR]

Background: Inferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals. Methods: All IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved. Results: The usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively. Conclusion: 3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time. [ABSTRACT FROM AUTHOR]

The demand for stretchable strain sensors with customizable sensitivities has increased across a spectrum of applications, spanning from human motion detection to plant growth monitoring. Nevertheless, a major challenge remains in the digital fabrication of scalable and cost‐efficient strain sensors with tailored sensitivity to diverse demands. Currently, there is a lack of simple digital fabrication approaches capable of adjusting strain sensitivity in a controlled way with no changes to the material and without affecting the linearity. In this study, parallel microgates‐based strain sensors whose strain sensitivity can be adjusted systematically throughout an all‐laser‐based fabrication process without any material replacement are presented. The technique employs a two‐step direct laser writing method that combines the well‐established capabilities of laser ablation and laser marking, boasting a varying gauge factor of up to 433% (GF = 168), while paving the way for the mass production of nanocomposite strain sensors. Parallel microgates‐based strain sensors exhibit a remarkable signal‐to‐noise ratio at ultralow strains (ɛ = 0.001), rendering them ideal for monitoring the gradual growth of plants. As an application demonstration, the proposed sensors are deployed on tomato plants to capture their growth under varying planting conditions including hydroponic and soil mediums, as well as diverse irrigation regimens. [ABSTRACT FROM AUTHOR]

Traditional handcrafting techniques used in the art of sculpting usually produce three-dimensional works of art, while modern reproducing techniques specific to copying and printing machines usually produce two-dimensional results. Mixing these techniques has had a major role in the development of recently emergent 3D digital fabrication techniques. Through a personal case study, this article discusses the similarities and differences between specific traditional additive manual processes used in the art of sculpting and additive three-dimensional modeling and 3D printing techniques. The case study showcases the hybridization of digital modeling and 3D printing processes with traditional physical handcrafting techniques to chart and inform a mixed media approach, with an array of positive outcomes in the art of sculpting and architecture. [ABSTRACT FROM AUTHOR]

This study explores the development of biofoundries, emphasizing the integration of synthetic biology with artificial intelligence (AI) and robotics. It outlines critical challenges such as the necessity for interdisciplinary collaboration and the development of hardware, software, and AI specific to biofoundry operations. To address these challenges, we recommend strategies like rapid prototyping, soft integration, and the strategic implementation of AI. We underscore the vital role of synthetic biology researchers in advancing biofoundry capabilities and advocate for collaborative, multidisciplinary efforts to optimize the development and functionality of biofoundries. [ABSTRACT FROM AUTHOR]

The possibility of obtaining a combined titanium-steel construction by directed energy deposition using vanadium or vanadium/nickel interlayers has been investigated. Vanadium wire VnPr-1, nickel wire NP1 grade, and welding wires Sv-08G2S, 316L, and MSG NiFe-1 grades were used for deposition on the end of the titanium plate VT1-0. The use of vanadium as an interlayer deposited on titanium makes it possible to provide a joining with a stable structure, steady hardness distribution, and acceptable manufacturability. The content of dissolved titanium in the vanadium layer has a negative effect on the subsequent deposition of steel, nickel, or iron-nickel alloy. Therefore, the titanium content was reduced to a level of less than 0.5% by the deposition of at least 4 vanadium beads. The results of tensile tests demonstrated that all specimens failed in the elastic section mostly along the interlayers between vanadium and subsequent layers of low-carbon steel, nickel, and iron-nickel alloys deposited on it. The greatest ultimate tensile strength is achieved in the specimen, which is a compound of titanium and steel through interlayers of vanadium and iron-nickel alloy, and constitutes 251 MPa. The only specimen was destroyed along the titanium-vanadium interlayer probably due to brittle ω–phase formation. [ABSTRACT FROM AUTHOR]

Microfluidic lab‐on‐a‐chip (LOC) devices have become essential tools for multitudes of applications in various research fields. 3D printing of microfluidic LOC devices offers many advantages over more traditional manufacturing processes, including rapid prototyping and single‐step fabrication of complex 3D structures. In this work, 3D‐printed microfluidic devices are designed and fabricated for optical chromatography and sorting. Optical chromatography is performed by inserting a single‐mode optical fiber into the device creating a counter‐propagating laser beam to the fluid flow. Particles are separated depending on refractive index and size. To demonstrate optical sorting, a cross‐type sorter 3D‐printed microfluidic device is fabricated that directs the laser beam perpendicular to the flow direction. Design features such as a sloping channel and a channel configuration for 3D hydrodynamic focusing (to aid in controlled sample flow and particle position) help to optimize sorting performance. Stable optofluidic trapping and sorting are successfully achieved using the fabricated microfluidic devices. These results highlight the tremendous potential of 3D printing of microfluidic LOC devices for applications aimed at the optofluidic manipulation of micron‐sized particles. [ABSTRACT FROM AUTHOR]

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

3D printing has revolutionized the fabrication and rapid prototyping of microfluidic devices. However, it remains challenging to achieve optically transparent microfluidic chips with 3D printing, which limits their applicability in high-resolution imaging applications. We present a paradigm-shifting, low-cost, and generalizable post-processing method to address this challenge. Our method is centered around the implementation of a novel post-processing equipment "Spinning Desicurer" which integrates solvent-free resin removal, surface smoothing, and efficient surface curing. Our method produces microfluidic devices with high optical transparency and can be readily used to image microscopic objects such as cells and microparticles with fine details. [ABSTRACT FROM AUTHOR]

The present work aims at investigating the impact of heat treatments on the mechanical, environmental and economic performances of components in carbon fibre polyetheretherketone (PEEK) composite produced using the Fused Deposition Modelling technique. The mechanical properties of PEEK can be strongly improved by performing heat treatments to maximize the degree of crystallinity in PEEK. To this purpose, the typical annealing heat treatment in a furnace, that is energy and time intensive, was compared to an innovative in situ annealing process named Direct Annealing System (DAS), which is performed during the 3D printing process. In order to evaluate the effect of heat treatment on mechanical properties of 3D printed parts, tensile tests were carried out on samples treated both using the annealing in a furnace and DAS processes. Similarly, the environmental and economic impacts of the different heat treatments were analysed by means of Life Cycle Assessment and Life Cycle Costing methodologies. The results demonstrated that the DAS system allows the improvement of the mechanical properties of carbon fibre PEEK composite, even though the highest performances can be obtained using a heat treatment in furnace. On the other hand, the DAS system is characterized by lower environmental and economic impacts than the annealing in furnace, denoting its more sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Light‐curing rapid prototyping (SLA) has become an emerging technology in the manufacturing industry because of its high precision, rapid prototyping, and the ability to mold complex parts. To enhance the mechanical properties and thermal stability of its raw material photosensitive resin (PR), carbon fiber (CF) was surface modified by chemical oxidation and grafting of amino silane (KH550) to obtain KH550‐modified carbon fiber (ACF). Then, ACF was composited with photosensitive resin to obtain modified carbon fiber/photosensitive resin (ACF/PR) composites. The viscosities of ACF/PR composites, including the viscosity, curing shrinkage, mechanical properties, and thermal stability of the ACF/PR composites, were characterized. The results showed that KH550 was successfully grafted onto CF. When the addition of ACF in the composites was 0.6%, the tensile strength, elongation at break, and impact strength of ACF/PR reached 39.48 MPa, 20.32%, and 13.62 kJ/m2, which were 120%, 27.15%, and 154% higher than that of the pure resin; the thermal decomposition temperatures and the maximum thermal decomposition temperatures at 50% mass loss of ACF/PR increased to 457.66°C and 442.44°C at 50% mass loss, which is 3.95% and 3.63% higher than that of the pure resin. Currently, the composites have excellent strength, toughness, and thermal stability. This paper gives a cost‐efficient method for improving the functioning of PR. Highlights: Mixed acid oxidation and amino silane modification of CFs.Preparation of modified CF/photosensitive resin composites.Composites with excellent mechanical properties and thermal stability. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

3D printing has matured into a versatile technique that offers researchers many different printing methods and materials with varying properties. Nowadays, 3D printing is deployed within a myriad of different applications, ranging from chemistry to biotechnology –including bioanalytics, biocatalysis or biosensing. Due to its inherent design flexibility (which enables rapid prototyping) and ease of use, 3D printing facilitates the relatively quick and easy creation of new devices with unprecedented functions.. This review article describes how 3D printing can be employed for research in the fields of biochemistry and biotechnology, and specifically for biocatalysis and biosensor applications. We survey different relevant 3D printing techniques, as well as the surface activation and functionalization of 3D‐printed materials. Finally, we show how 3D printing is used for the fabrication of reaction ware and enzymatic assays in biocatalysis research, as well as for the generation of biosensors using aptamers, antibodies, and enzymes as recognition elements. [ABSTRACT FROM AUTHOR]

Genetic modification of plants fundamentally relies upon customized vector designs. The ever-increasing complexity of transgenic constructs has led to increased adoption of modular cloning systems for their ease of use, cost effectiveness, and rapid prototyping. GreenGate is a modular cloning system catered specifically to designing bespoke, single transcriptional unit vectors for plant transformation—which is also its greatest flaw. MultiGreen seeks to address GreenGate's limitations while maintaining the syntax of the original GreenGate kit. The primary limitations MultiGreen addresses are 1) multiplexing in series, 2) multiplexing in parallel, and 3) repeated cycling of transcriptional unit assembly through binary intermediates. MultiGreen efficiently concatenates bespoke transcriptional units using an additional suite of level 1acceptor vectors which serve as an assembly point for individual transcriptional units prior to final, level 2, condensation of multiple transcriptional units. Assembly with MultiGreen level 1 vectors scales at a maximal rate of 2*⌈log6n⌉+3 days per assembly, where n represents the number of transcriptional units. Further, MultiGreen level 1 acceptor vectors are binary vectors and can be used directly for plant transformation to further maximize prototyping speed. MultiGreen is a 1:1 expansion of the original GreenGate architecture's grammar and has been demonstrated to efficiently assemble plasmids with multiple transcriptional units. MultiGreen has been validated by using a truncated violacein operon from Chromobacterium violaceum in bacteria and by deconstructing the RUBY reporter for in planta functional validation. MultiGreen currently supports many of our in-house multi transcriptional unit assemblies and will be a valuable strategy for more complex cloning projects. [ABSTRACT FROM AUTHOR]

Three-dimensional printing technology is a rapid prototyping technology that has been widely used in manufacturing. However, the printing parameters in the 3D printing process have an important impact on the printing effect, so these parameters need to be optimized to obtain the best printing effect. In order to further understand the impact of 3D printing parameters on the printing effect, make theoretical explanations from the dimensions of mathematical models, and clarify the rationality of certain important parameters in previous experience, the purpose of this study is to predict the impact of 3D printing parameters on the printing effect by using machine learning methods. Specifically, we used four machine learning algorithms: SVR (support vector regression): A regression method that uses the principle of structural risk minimization to find a hyperplane in a high-dimensional space that best fits the data, with the goal of minimizing the generalization error bound. Random forest: An ensemble learning method that constructs a multitude of decision trees and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. GBDT (gradient boosting decision tree): An iterative ensemble technique that combines multiple weak prediction models (decision trees) into a strong one by sequentially minimizing the loss function. Each subsequent tree is built to correct the errors of the previous tree. XGB (extreme gradient boosting): An optimized and efficient implementation of gradient boosting that incorporates various techniques to improve the performance of gradient boosting frameworks, such as regularization and sparsity-aware splitting algorithms. The influence of the print parameters on the results under the feature importance and SHAP (Shapley additive explanation) values is compared to determine which parameters have the greatest impact on the print effect. We also used feature importance and SHAP values to compare the importance impact of print parameters on results. In the experiment, we used a dataset with multiple parameters and divided it into a training set and a test set. Through Bayesian optimization and grid search, we determined the best hyperparameters for each algorithm and used the best model to make predictions for the test set. We compare the predictive performance of each model and confirm that the extrusion expansion ratio, elastic modulus, and elongation at break have the greatest influence on the printing effect, which is consistent with the experience. In future, we will continue to delve into methods for optimizing 3D printing parameters and explore how interpretive machine learning can be applied to the 3D printing process to achieve more efficient and reliable printing results. [ABSTRACT FROM AUTHOR]

Featured Application: Applications for this work include rapid prototyping of fan or compressor casings using additive manufacturing. Additive manufacturing (AM) presents significant cost savings and lead time reductions because of features inherent to the manufacturing process. The technology lends itself to rapid prototyping due to the streamlined workflow of quickly implementing design changes. Compared to traditional machining, AM techniques are simpler in execution for design engineers because they do not require detailed engineering drawings and they typically make use of the nominal geometry in computer models. A novel transonic fan casing assembly has been developed that makes use of AM inserts surrounding the rotor to provide an opportunity to cost-effectively change the corresponding flowpath. The rapid prototyping design philosophy developed from this work will allow for numerous experimental studies into the effects that different design parameters of casing geometries have on fan aerodynamic performance. A fan stage representative of a small turbofan engine was successfully tested with smooth-walled, additively manufactured inserts as a baseline case for future configurations. Before installing the 3D printed casing assembly, computational thermal stress analysis was performed to reduce the risk in implementation due to the demanding environment associated with the rotor. AM components and materials typically have nonlinear mechanical properties, adding to the complexity of the structural analysis. As part of the research, steady aerodynamic performance was measured over the entire relevant operating range of the fan. [ABSTRACT FROM AUTHOR]

Parts manufactured by additive manufacturing (AM) are criticized due to the high surface roughness. Roller burnishing is one of the post-processing processes which may be applied for reducing the roughness and asperities on surfaces of 3D printed parts. In this study, innovative zirconium oxide (ZrO2) reinforced acrylonitrile butadiene styrene (ABS) based composites have developed in filament form for 3D printing of roller burnishing rapid tools. The filaments of ABS-ZrO2 were extruded under the range of 230-240 °C of barrel temperature and 4-6 RPM of screw speed under heat treatment as per Taguchi L9 based experimentation. As per the mechanical properties concern of composite filament material, the combination of preheat treatment, 235 °C of barrel temperature, and 6 RPM of screw speed have been noted best setting (Young's modulus: 886.00 MPa). The filament preparation has been supported with such as x-ray diffraction (XRD), Fourier transforms infrared (FTIR), and microstructural analysis using metallurgical image analysis software (MIAS). A machine learning (ML) approach based on classification and regression trees (CART) has been utilized to predict the tensile peak and break strength. Finally, the 3D printed roller burnishing tool has significantly reduced the surface roughness after burnishing. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) represents a state‐of‐the‐art 3D printing technology that holds great promise in the field of electrochemistry as a fast, in‐house, and digital fabrication method of sustainable sensors. Despite the progress in FDM, existing practical 3D printed sensors still require post‐printing treatment to improve their operational features, either through (electro)chemical surface activation or surface modification with external functional materials. Herein, a novel lab‐made conductive biofilament with integrated two different types of metallic nanoparticles (NPs) for the 3D printing of ready‐to‐use and multiplexed electrochemical sensors is introduced. The filament is composed of biodegradable polylactic acid as a base, polyethylene glycol dimethyl ether as a plasticizer, carbon black as a conductive filler, and nanopowder oxides of bismuth and copper as integrated functional precursor materials. The as‐printed sensors serve as multifunctional transducers for the direct and sensitive determinations of several analytes, by exploiting the different properties of the two co‐existing NPs. Particularly, heavy metals (such as lead, and cadmium) are voltammetrically quantified by amalgamation with BiNPs electrogenerated from Bi2O3, while biomarkers (such as glucose, and uric acid) are determined in enzymatic‐free modes. Determination of glucose takes advantage of the electrocatalytic properties of CuO, while uric acid is directly oxidized at the sensor. [ABSTRACT FROM AUTHOR]

Polymerization in the solid state is generally infeasible due to restrictions on mobility. However, in this work, the solid‐state photopolymerization of crystalline dicyclopentadiene is demonstrated via photoinitiated ring‐opening metathesis polymerization. The source of mobility in the solid state is attributed to the plastic crystal nature of dicyclopentadiene, which yields local short‐range mobility due to orientational degrees of freedom. Polymerization in the solid state enables photopatterning, volumetric additive manufacturing of free‐standing structures, and fabrication with embedded components. Solid‐state photopolymerization of dicyclopentadiene offers a new paradigm for advanced and freeform fabrication of high‐performance thermosets. [ABSTRACT FROM AUTHOR]

3D printing, also known as additive manufacturing, is an innovative technology that allows for the construction of complex, three‐dimensional structures layer by layer using digital plans. This technology has transformed industries including as aerospace, automotive, healthcare, and consumer items by allowing for rapid prototyping, customization, and the manufacture of complex geometries. Graphene, a single layer of carbon atoms organized in a hexagonal lattice, is well‐known for its superior electrical and thermal conductivity, as well as its great tensile strength. When graphene is mixed with composite materials, it greatly improves their mechanical and functional properties, resulting in composites with higher strength, conductivity, lower weight, and greater durability. The combination of 3D printing and graphene‐reinforced composites creates new opportunities for the production of high‐performance, application‐specific structures. This review identifies key advancements in the synthesis, processing, and application of these composites, while also addressing critical challenges such as material dispersion, scalability, and the impact of graphene on the 3D printing process itself. A significant conclusion of this review is the recognition that overcoming these challenges is not only feasible but essential for harnessing the full potential of 3D‐printed graphene‐reinforced composites across diverse industrial sectors. The unique contribution of this work lies in providing a comprehensive roadmap for future research, guiding efforts to bridge current gaps and drive innovation in this emerging field. [ABSTRACT FROM AUTHOR]

Rapid prototyping and fabrication of microstructure have been revolutionized by 3D printing, especially stereolithography (SLA) based techniques due to the superior spatial resolution they offer. However, SLA-type 3D printing faces intrinsic challenges in multi-material integration and adaptive Z-layer slicing due to the use of a vat and a mechanically controlled Z-layer generation. In this paper, we present the conceptualization of a novel paradigm which uses dynamic and multi-phase laminar flow in a microfluidic channel to achieve fabrication of 3D objects. Our strategy, termed "in situ 3D polymerization," combines in situ polymerization and co-flow aqueous two-phase systems and achieves slicing, polymerization, and layer-by-layer printing of 3D structures in a microchannel. The printing layer could be predicted and controlled solely by programming the fluid input. Our strategy provides generalizability to fit with different light sources, pattern generators, and photopolymers. The integration of the microfluidic channel could enable high-degree multi-material integration without complicated modification of the 3D printer. [ABSTRACT FROM AUTHOR]

Photonic integrated circuits (PICs) are iterating on electronic ones to enable higher‐speed parallel computing while meeting the growing demands for energy efficiency in big‐data processing. However, manufacturing multi‐layered and 3D PICs remains challenging based on conventional planar lithography. Here, a rapid prototyping technique is delineated to configure large‐area and 3D PIC in glass based on laser‐direct written optical waveguides by employing femtosecond cylindrically polarized vortex laser beams. The vortex laser beam is mathematically rotationally invariant and capable of conformally transforming waveguide cross‐sections at arbitrary bending angles of 0–90° and across a large depth change. Based on this approach, a one‐layer 12‐core waveguide circuit with a variable cross‐section (diameter 5–11.5 µm) while maintaining a high mode ellipticity >0.974, as well as a more complex three‐layer 36‐core connector in which the waveguide dives continuously from 50 µm to 550 µm with a small loss difference of <0.1 dB is demonstrated. This work offers a pathway for 3D imprinting of large‐area photonic circuits in glass and other transparent materials, which has potential applications in high‐efficiency photonic computing, multidimensional quantum networks, and 3D optics topology. [ABSTRACT FROM AUTHOR]

The emergence of prosumers, who actively participate in designing and producing goods, has generated a growing interest in homemade products. Factors such as design methods, component reuse, or digital fabrication empower prosumer designers to realize their ideas. Although there are cases of bioinspired products manufactured by prosumers, the interactions between bioinspired design methods in the prosumer field have not been addressed from an academic point of view. This article presents a case that combines bioinspired design methods with prosumer characteristics from the perspective of a designer who uses biological research results whilst acting as a prosumer. The proposal is to see whether working on a small scale, without the need for biomimetics experts, and independently, as a prosumer, is feasible and valuable. As a result, a bicycle flashlight is designed with a microgenerator bioinspired by the geometry of samara seeds, and is tested in a wind tunnel. This case shows that the integration of a bioinspired design in prosumer contexts poses unique challenges and requires a multidisciplinary approach. Furthermore, the application of a bioinspired approach in this case has not only provided a certain level of novelty to the final product, but has also improved its efficiency and reduced its financial expenditure. [ABSTRACT FROM AUTHOR]

The morphology-performance-fitness paradigm has long been a guiding principle inspiring a great deal of laboratory and field studies fundamental to understanding functional-morphology relationships across the tree of life. Despite the power of experimental approaches, they also come with inherent limitations associated with equipment and animal costs, as well as ethical considerations for the types of manipulations that can be implemented. Modeling can provide an opportunity to surmount some of these challenges by offering greater flexibility in manipulating variables and exploring a wider parameter space than is tractable during animal experimentation. However, effective implementation of these tools requires careful consideration of the limitations and benefits they convey, requiring both greater interdisciplinary training from early stages of educational development and increased collaboration and synergies among scientists from traditionally separate disciplines. With institutions increasingly recognizing the need for and investing in providing universal access to computational and rapid prototyping resources, we believe that it is an opportune moment to prioritize greater synergy to accelerate discovery and innovation across fields. [ABSTRACT FROM AUTHOR]

Emulsification processes are often found in the process industry and their evaluation is crucial for product quality and safety. Numerous methods exist to analyze critical quality attributes (CQA) such as the droplet sizes and droplet size distribution (DSD) of an emulsification process. During the emulsification process, the optical process accessibility may be limited due to high disperse phase content of liquid-liquid systems. To overcome this challenge, a modular, optical measurement flow cell is presented to widen the application window of optical methods in emulsification processes. In this contribution, the channel geometry is subject of optimization to modify the flow characteristics and produce high optical quality. In terms of rapid prototyping, an iterative optimization procedure via SLA-3D printing was used to increase operability. The results demonstrated that the flow cell resulting from the optimization procedure provides a broad observation window for droplet detection. [ABSTRACT FROM AUTHOR]

Digital fabrication of concrete structures has gained substantial research traction over the last decade, enabling efficient material use and adding more architectural freedom. Current research focuses on chemical and mineral admixtures, as well as manipulating the cement hydration reaction to control the yield stress evolution with time in the cement paste. Instead of providing yield stress through the concrete fluid properties, a high yield stress can be provided by interparticle friction from the use of high aggregate volumes and large nominal maximum aggregate size, with flow enhanced for material extrusion by vibration. Granular physics was applied to concrete mixture design to develop concrete mixtures with excellent edge retention abilities that flow easily under vibration. A linear regression analysis of concrete yield stress after vibration revealed that water content is most important, followed by fineness modulus of the aggregate combination and density after vibration. A decrease in water content, increase in fineness modulus and increase in density after vibration were found to increase the static yield stress after vibration. [ABSTRACT FROM AUTHOR]

Concrete is a building material with excellent structural and architectural qualities that could be molded into any shape. However, it is responsible for approximately 8% of the world's total greenhouse gas emissions. Therefore, material consumption must be reduced during the design and construction of concrete structures. To achieve this, designers could employ approaches that allow achieving more with less, such as designing structural geometries that use material where it is needed and removing it from elsewhere. In addition, efficient concrete construction methods are a major focus of research in architecture, civil engineering, and material sciences. One area of investigation is the direct digital fabrication of concrete, or three-dimensional concrete printing (3DCP), and another is the indirect digital fabrication by three-dimensional printed formworks (3DPFs). Focusing on 3DPFs, many projects have investigated them using multiple approaches, such as stay-in-place, in situ, or disposable strategies. A number of these approaches are incompatible with economies of scale. Mass production and modular, reusable assemblies currently dominate the market to cut costs, with reusable 3DPFs largely underrepresented in current research. This research presents a case study for a new manufacturing workflow for the design and fabrication of precast facade panels. Structural topology optimization (TO) designs facade panels with optimized material distribution according to the panel's connection to a substructure. Then, flexible thermoplastic polyurethane (TPU) molds are additively manufactured and repeatedly cast. This research demonstrates a proof-of-concept for additively manufactured TPU molds for the repeatable casting of concrete when accommodating undercuts in complex cast geometries. In addition, it investigates a hybrid molding approach that uses a TPU–polylactic acid (PLA) two-part mold. This research is novel because the use of additively manufactured flexible molds made from TPU for the repeatable casting of geometries is in its early stages. In addition, to the best of the author's knowledge, no previous study has looked into creating hybrid molds with TPU and PLA filaments. This research is pioneering because it addresses material savings during the design phase with TO; and during the fabrication phase by creating reusable 3DPFs. [ABSTRACT FROM AUTHOR]

Rapid investment casting with additively produced molds can offer excellent surface finishes, tight dimensional tolerances, and complex geometries for high-performance metal parts in a rapid fashion. However, there is a long-standing challenge in the investment casting of high-strength aluminum alloy (AA) 7075 due to its hot cracking susceptibility and severe solidification shrinkage. Here, we show the unprecedented rapid investment casting of AA7075 by applying nano-treating technology, whereby a low-volume fraction of nanoparticles is dispersed into the metal to modify its solidification behavior and microstructure. TiC nanoparticles were able to effectively modify alloy's microstructure while suppressing its hot cracking susceptibility during solidification. Primary grain sizes were reduced by 95%, from 1000 µm to 50 µm. Secondary eutectic phases solidified as thin and dispersed fragments with disrupted lamellar morphologies. Nano-treated AA7075 parts produced by rapid investment casting exhibited exceptional tensile strength and ductility in both as-cast and heat-treated conditions. This study highlights the potential of investment casting high-performance alloys that were traditionally considered impossible to fabricate by this method. [ABSTRACT FROM AUTHOR]

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. In addition, many models report a low mean-square error (MSE) across the entire domain of a part. However, in each time-step, most areas of the domain do not experience significant changes in temperature, except for the regions near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This article presents a data-driven model that uses the Fourier neural operator to capture the local temperature evolution during the AM process. Besides MSE, the model is also evaluated using the R² metric, which places great weight on the regions where the temperature changes significantly than MSE. The model was trained and tested on numerical simulations based on the discontinuous Galerkin finite element method for the direct energy deposition AM process. The results shows that the model maintains 0.983 - 0.999 R² over geometries not included in the training data, which is higher than convolutional neural networks and graph convolutional neural networks we implemented, the two widely used architectures in data-driven predictive modeling. [ABSTRACT FROM AUTHOR]

Developing immersive learning systems is challenging due to their multidisciplinary nature, involving game design, pedagogical modelling, computer science, and the application domain. The diversity of technologies, practices, and interventions makes it hard to explore solutions systematically. A new methodology called Multimodal Immersive Learning Systems Design Methodology (MILSDeM) is introduced to address these challenges. It includes a unified taxonomy, key performance indicators, and an iterative development process to foster innovation and creativity while enabling reusability and organisational learning. This article further reports on applying design-based research to design and develop MILSDeM. It also discusses the application of MILSDeM through its implementation in a real-life project conducted by the research team, which included four initiatives and eight prototypes. Moreover, the article introduces a unified taxonomy and reports on the qualitative analysis conducted to assess its components by experts from different domains. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system. [ABSTRACT FROM AUTHOR]

This study explores the development and implementation of Shadow, an advanced mobile social robot designed to meet specific functional requirements. Shadow is intended to serve both as a versatile tool and a human companion, assisting in various tasks across different environments. The construction emphasizes cost efficiency and high agility, utilizing 3D printing technology exclusively. The robot features omnidirectional kinematics and a flexible power electronics system, accommodating diverse energy needs with lithium batteries that ensure at least seven hours of autonomous operation. An integrated sensor array continuously monitors the power system, tracks tilt and acceleration, and facilitates self-diagnostic functions. Rapid prototyping allows for swift iteration, testing, and refinement to align with project goals. This paper provides a comprehensive blueprint for designing cost-effective, highly agile robots using advanced manufacturing techniques. Extensive testing, including stability and sensory skills evaluations, demonstrates Shadow's adherence to its design objectives. Shadow has advanced from technology readiness level (TRL) 2 to TRL 7 within a year and is currently undergoing trials with advanced functionalities, offering significant insights into overcoming practical design challenges and optimizing robot functionality. [ABSTRACT FROM AUTHOR]