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The design of metallic glasses (MGs) with good properties is one of the long-standing bottlenecks in materials science and engineering, which has been relying mostly on far less efficient traditional trial-and-error methods. Even the currently popular machine learning-based forward designs, which use manual input to navigate high dimensional compositional space, often become inefficient with the increasing compositional complexity in MGs. Here, we developed an inverse design machine learning model, leveraging the variational autoencoder (VAE), to directly generate the MGs with good glass-forming ability (GFA). We demonstrate that our VAE with the property prediction model is not only an expressive generative model but also able to do accurate property prediction. Our model allows us to automatically generate novel MG compositions by performing simple operations in the latent space. After randomly generating 3000MG compositions using the model, a detailed analysis of four typical metallic alloys shows that unreported MG compositions with better glass-forming ability can be predicted. Moreover, our model facilitates the use of powerful optimization algorithms to efficiently guide the search for MGs with good GFA in the latent space. We believe that this is an efficient way to discover MGs with excellent properties. [ABSTRACT FROM AUTHOR]

Development of nanophotonics has made it possible to control the wavelength and direction of thermal radiation emission, but it is still limited by Kirchhoff's law. Magneto-optical materials or Weyl semimetals have been used in recent studies to break the time-reversal symmetry, resulting in a violation of Kirchhoff's law. Currently, most of the work relies on the traditional optical design basis and can only realize the nonreciprocal thermal radiation at a specific angle or wavelength. In this work, on the basis of material informatics, a design framework of a multilayer nonreciprocal thermal absorber with high absorptivity and low emissivity at any arbitrary wavelength and angle is proposed. Through a comprehensive investigation of the underlying mechanism, it has been discovered that the nonreciprocal thermal radiation effect is primarily attributed to excitation of the cavity mode at the interface between the metal and the multilayer structure. Moreover, the impact of factors, such as layer count, incidence angle, extinction coefficient, and applied magnetic field on nonreciprocal thermal radiation, is thoroughly explored, offering valuable insights to instruct the design process. Additionally, by expanding the optimization objective, it becomes feasible to design fixed dual-band or even multi-band nonreciprocal thermal absorbers. Consequently, this study offers essential guidelines for advancing the control of nonreciprocal thermal radiation. [ABSTRACT FROM AUTHOR]

A highly accurate iteration of the Liu & Zenner multiaxial fatigue criterion is presented and validated against the FatLim dataset. The proposed criterion modifies the mean stress parameters of the original criterion while keeping the amplitude parameters unchanged. The investigation of the best‐performing stress amplitude parameters revealed that existing multiaxial fatigue criteria that rely on dimensional weight parameters exhibit a notable prediction bias. This bias leads to inaccurate fatigue assessments. The paper sheds light on the extent of this bias and its implications for common engineering practices. By introducing the refined criterion, the study addresses these limitations and provides a more reliable framework for multiaxial fatigue analysis. The comprehensive validation against the FatLim dataset demonstrates the superiority of the proposed criterion in accurately predicting fatigue behavior under diverse loading conditions. The findings underscore the significance of considering both amplitude and mean stress parameters in multiaxial fatigue criteria, offering valuable insights for enhancing the accuracy of fatigue assessments in engineering applications. Highlights: A data‐driven validation process aids in selecting fatigue criteria for complex loading conditionsNew insights gained regarding the limitations of existing criteria under partial and overload casesNew integral fatigue criterion yields the highest result accuracy among studied criteriaThe proposed approach improves the accuracy of fatigue analysis and design of machine elements [ABSTRACT FROM AUTHOR]

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type‐1 (HIV‐1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one‐dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild‐type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔGbind) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model‐guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing. [ABSTRACT FROM AUTHOR]

Sparse General Matrix-Matrix Multiplication (SpGEMM) has played an important role in a number of applications. So far, many efficient algorithms have been proposed to improve the performance of SpGEMM on GPUs. However, the performance of each algorithm for matrices of different structures varies a lot. There is no algorithm that can achieve the optimal performance of SpGEMM computation on all matrices. In this article, we design a machine learning based approach for predicting the optimal SpGEMM algorithm on input matrices. By extracting features from input matrices, we utilize LightGBM and XGBoost to train different lightweight models. The models are capable of predicting the best performing algorithm with low inference overhead and high accuracy for the given input matrices. We also investigate the impact of tree depth on model accuracy and inference overhead. Our evaluation shows that an increase in tree depth leads to a corresponding increase in prediction accuracy, reaching a maximum of approximately 85%, while resulting in increased inference overhead of approximately 10 µs. Compared with the state-of-the-art algorithms on three GPU platforms, our method achieves better overall performance. [ABSTRACT FROM AUTHOR]

Trunk shakers perform a forced vibration that should be set according to tree species and architecture to efficiently detach fruits. However, manufacturing companies produce designs that are poorly customisable according to different trees. This work presents a prototype that is configurable in amplitude and frequency of vibration, which has been developed to generate the different vibration patterns required by various fruit trees. To study the feasibility of the developed technology, we built a test bench with a post, to which we fixed olive, almond and orange tree branches, and tested different configurations of the machine (frequencies from 12.25 to 20.75 Hz and eccentricities from 58 to 86 mm). Acceleration values were recorded at different points: the trunk shaker, the post in the grip, the top of the post, and in each branch. We recorded a large loss of acceleration transmissibility between the shaker and the post (20%–60%) due to existing frictions that dissipate energy through heat loss. There is a non-linear increase between vibration frequency and the resultant acceleration value recorded, as well as with increasing eccentricity. The shaker was able to provide a different vibration pattern in each configuration, affecting the transmission of vibration in each of the branches studied, for which mean acceleration transmissibility ranged between 82% (almond), 104% (olive) and 136% (orange). [ABSTRACT FROM AUTHOR]

This paper proposes a new double mover configuration of a linear switched reluctance motor (LSRM). The proposed design is established for optimization of the motional forces and to ensure a high-grade electromechanical energy conversion process. The major drawback of the traditional linear machine is that its force densities within and throughout its area is produced in its radial direction of the yoke and does not contribute to its motion or twisting force of the rotor. If these normal forces happen to be in the direction of motion, a larger motional force profile for SRM is yielded. Based on these guidelines, a new LSRM is developed. In order to compare the energy conversion efficiency of LSRM with that of the conventional SRM, a finite element model is constructed. The proposed system is simulated using the FEM software and tested under four conditions. Further, an experimental prototype of the proposed machine is also developed and tested in the laboratory. The results obtained from the prototype indicate that the proposed geometry offers superior performance in terms of high-power density and higher percentage of the national forces. [ABSTRACT FROM AUTHOR]

When designing structures that take part in rotating fluid flow machines, one important effect that may be taken into account is resonance, which is evaluated through the modal analysis. Under rotating conditions, the formulation and behavior of the structure changes, requiring a new formulation. Thus, this work formulates the fluid flow topology optimization for rotating resonance, while also considering a low mass density (compressible or incompressible) fluid flow. Therefore, three different effects are considered in the fluid flow-based design: from the modal analysis, from the solid structure, and from the low mass density fluid flow. The topology optimization problem is formulated for a simplified 2D model of a rotating fluid flow device, and a nodal design variable. The topology optimization is performed through an integer linear programming algorithm. Numerical examples are presented for the developed topology optimization formulation. • Topology optimization rotor design considering a resonance and fluid flow multi-objective function. • Rotor resonance considering spin-softening. • Fluid flow topology optimization considering compressible and incompressible flows. • Discrete variable topology optimization. [ABSTRACT FROM AUTHOR]

The optimization of scenarios and design of real-time-control in tokamaks, especially for machines still in design phase, requires a comprehensive exploration of solutions to the Grad–Shafranov (GS) equation over a high-dimensional space of plasma and coil parameters. Emulators can bypass the numerical issues in the GS equation, if a large enough library of equilibria is available. We train an ensemble of neural networks to emulate the typical shape-control targets (separatrix at midplane, X-points, divertor strike point, flux expansion, and poloidal beta) as a function of plasma parameters and active coil currents for the range of plasma configurations relevant to spherical tokamaks with a super-X divertor, with percent-level accuracy. This allows a quick calculation of the classical-control shape matrices, potentially allowing real-time calculation at any point in a shot with submillisecond latency. We devise a hyperparameter sampler to select the optimal network architectures and quantify uncertainties on the model predictions. To generate the relevant training set, we devise a Markov-chain Monte Carlo algorithm to produce large libraries of forward Grad–Shafranov solutions without the need for user intervention. The algorithm promotes equilibria with desirable properties, while avoiding parameter combinations resulting in problematic profiles or numerical issues in the integration of the GS equation. [ABSTRACT FROM AUTHOR]

Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering--nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods, including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several "interpretable" decisions which can be explained with physical insights. [ABSTRACT FROM AUTHOR]

The exploration and utilization of water resources on the Moon are of substantial global interest. To utilize lunar resources and construct bases, the construction machinery should travel over the lunar surface (which is mainly covered with powdery regolith) and excavate the regolith. However, various technical issues should be resolved to achieve this efficiently. In this study, a new platform rover was developed, and its motion behavior was analyzed to better understand the traveling and excavation behaviors of construction machinery on the Moon. The rover is a four-track vehicle equipped with a robotic arm consisting of a boom, arm, and bucket. To analyze the rover's motion behavior in sandy terrain, we first developed a simulator based on terramechanics and performed a numerical analysis. Subsequently, various experiments were conducted using the rover in the JAXA Space Exploration Field, which simulates the lunar environment. In the experiments, the rover traveled over level and sloped terrains and excavated the ground. The simulation and experimental results revealed similar trends in the traveling and excavation behaviors of the rover. These results can serve as basic guidelines for the design and operation of construction machinery on the Moon. [ABSTRACT FROM AUTHOR]

In any short-rotation coppice (SRC) operation, a certain percentage of harvestable material is unrecovered, which contributes to harvesting system losses. This material may be in the form of merchantable and non-merchantable components. These losses affect economics but also influence yield, nutrient cycling, and carbon sequestration. There are very few estimates for harvesting losses available in the literature, and they are limited by small sample sizes. The objective of this work was to provide a broad overview of harvesting losses in willow SRC over a wide range of standing biomass and harvesting conditions. The average total harvesting losses were between 3 and 4 Mg ha−1, which is between 6 and 7 percent of the standing biomass. Losses can spike to nearly 40% on less than 3% of the area. Harvesting losses are significantly, but weakly, correlated with increased standing biomass. These results highlight the complexity and variability in harvesting losses as well as which aspects of harvesting systems might be targeted to reduce or partition material losses. These results have implications for designing machinery and economic modeling of these systems. [ABSTRACT FROM AUTHOR]

This study introduces innovative designs for abrasive tools aimed at enhancing surface finishing processes. Prototypes consisting of non-continuous abrasive films with discontinuous surface carriers and abrasive layers were developed to improve the efficiency and effectiveness of the smoothing process. Four distinct abrasive films with varying nominal grain sizes were fabricated to explore the versatility and efficacy of the prototypes. The results indicate that the incorporation of carrier irregularities significantly influences surface finishing processes, leading to improvements in material removal efficiency and surface quality. Longitudinal discontinuities facilitate faster removal of irregularities from workpiece materials, reducing the risk of deep scratches on surfaces. Additionally, this study highlights the importance of tool motion patterns in optimizing material removal processes and ensuring surface quality. The integration of carrier irregularities with additional oscillatory tool motion shows promise for further improving surface quality. These findings advance our understanding of abrasive machining processes and provide valuable insights for optimizing abrasive tool designs and machining strategies for enhanced surface finishing. [ABSTRACT FROM AUTHOR]

The paper presents the construction and technological aspects of a device for pitting fruit, especially cherries, on an industrial scale. The presented work includes design solutions related to the transport of pits removed from pitted fruit, as well as a gravity chute, with an indication of potential operational problems related to the machine design. The authors proposed the use of a screw conveyor, especially in the case of longer drums, to optimize the process. Some analyses are also presented, including friction occurring during the transport of pits. Ultimately, a specific design concept is presented that integrates the screw conveyor with the pitting drum. The presented conclusions point to practical technological and engineering aspects related to fruit processing, suggesting the potential for innovative solutions in the design of pitters. [ABSTRACT FROM AUTHOR]

Deployable polyhedral grippers have attracted increasing attention for their priority in noncontact capturing missions. Enrichment of these grippers may benefit the conduction of various capturing tasks. In this paper, novel deployable polyhedral grippers are presented. A design flow is proposed for the structural designs of diverse grippers. The core problem during the construction is reducible to the structural designs and combination of multiple synchronously deployable modules. Each module, containing three faces connected by two revolute joints, can realize one-degree-of-freedom deployment. Type synthesis of synchronously deployable modules adopting different layouts of revolute joints is conducted. The mobility and kinematics of these modules are analyzed to verify the achieved motion. As examples, four deployable polyhedral grippers based on different polyhedrons and deployment diagrams are presented. The deployment performance of the prototype proves the validity of the proposed design method and exhibits the potential of these deployable polyhedral grippers for diverse capturing missions. [ABSTRACT FROM AUTHOR]

Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial‐to‐calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio‐derived features, potentially eliminating the need for ab initio calculations in descriptor‐based screening. The model of ASR enables temperature‐dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross‐validation reveals the model to be effective at discovering new promising materials prior to their initial discovery, demonstrating the model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, detailed discussion of different approaches of model featurization is provided for ML property prediction. Finally, the model is used to screen more than 19 million perovskites to develop a list of promising cheap, earth‐abundant, stable, and high performing materials, and find some top materials contain mixtures of less‐explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail. [ABSTRACT FROM AUTHOR]

Innovative technological solutions have become increasingly critical in addressing the transportation sector's environmental impact. Passenger vehicles present an opportunity to introduce novel drivetrain solutions that can quickly penetrate the electric vehicle market due to their shorter development time and lifetime compared to commercial vehicles. As environmental policy pressure increases and customers demand more sustainable products, shifting from a linear business approach to a circular economy model is in prospect. The new generation of economically competitive machines must be designed with a restorative intention, considering future reuse, refurbishment, remanufacture, and recycling possibilities. This review investigates the market penetration possibilities of permanent magnet-assisted synchronous reluctance machines for mini and small-segment electric vehicles, considering the urban environment and sustainability aspects of the circular economy model. When making changes to the materials used in an electric machine, it is crucial to evaluate their potential impact on efficiency while keeping the environmental impact of those materials in mind. The indirect ecological effect of the vehicle's use phase may outweigh the reduction in manufacturing and recycling at its end-of-life. Therefore, thoroughly analysing the materials used in the design process is necessary to ensure maximum efficiency while minimising the environmental impact. [ABSTRACT FROM AUTHOR]

The Hybrid Excitation Flux Switching Generator (HEFSG) has gained significant popularity in recent times owing to its relatively simple remarkably efficient topology. To optimize the performance of the generator, recent advancements and emerging patterns in mathematical modeling and software simulation, along with the utilization of optimization techniques, have facilitated the development of a novel methodology for electrical machine design. This study investigates the configuration and optimization of a Hybrid Excitation Flux Switching Generator, focusing on the rotor, armature coil, and field excitation. The optimization process involves multiple sequences for each component, employing the Local Optimization Method as an iterative approach to determine the optimal sequence that yields the highest output efficiency. Through the investigation of six rotor sequences, two armature coil sequences, and two field excitation coil sequences, a detailed optimization process was conducted. Consequently, the final output voltage of the HEFSG gains a 1.10% increment of voltage compared to the initial outcomes. Several sequences have influenced the output voltage performance of the generator during the optimization process. Therefore, modifications to the design of the arrangement contribute to the expansion of the operational range of the generator. [ABSTRACT FROM AUTHOR]

Piezoelectric actuators commonly used in microgrippers have a small stroke, and their accuracy is reduced by the transmission amplification unit, which leads to a contradiction between the clamping range and the clamping accuracy in existing piezoelectric-actuated microgrippers. This paper proposes a design scheme to divide the total clamping range of the microgripper into segments based on the compliant multistable mechanism (CMM). First, by using the stable equilibrium positions of the CMM, the total clamping range of the microgripper is divided into multiple smaller clamping sub-intervals to accommodate objects of different scales. Then, the theoretical models of the displacement amplification ratio of the microgripper amplification mechanism and the stiffness of the microgripper in different clamping sub-intervals are established, and the force-displacement characteristics of the CMM are analyzed. Next, through finite element simulation, the correctness of the theoretical analyses is verified, and it is shown that objects between 0 µm and 1.650 mm can be clamped using four clamping sub-intervals under a five times displacement amplification ratio. Finally, a microgripper of the CMM consisting of two three-segment fully compliant bistable mechanisms connected in series is designed and machined, and microgripper segmented clamping experiments are conducted. The experimental results demonstrate the feasibility of the design scheme proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method for analyzing the electromagnetic performance of a superconducting (SC) machine according to the 10 MW shielding configuration and the presence or absence of an armature core. To establish the SC machine design process, an analytical method considers shielding conditions and armature core type is included. The presented analytical method derives the governing equations and general solutions for each region using Maxwell's equations and electromagnetic field theory. Furthermore, it calculates the analytical solutions by applying appropriate boundary conditions. Additionally, the performance of actively and passively shielded, with the same size and shielding performance of SC machine is compared. The electromagnetic performance obtained through the analytical method is compared with finite element analysis to validate its accuracy. The accuracy of the presented analytical method can be utilized in various design analyzes, including initial design and optimal design. [ABSTRACT FROM AUTHOR]

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation. [ABSTRACT FROM AUTHOR]

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate's copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate's copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line's efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface's roughness. The device's structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine's gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool's overall dimensions, and simulation validated the fly-cutterring's constituent parts. Enhancing the machine tool's stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway's straightness. The result verified the machine tool's design index, with the Z- and Y-axes' straightness being better than 2.42 μ m / 800 mm and 2.32 μ m / 200 mm, respectively. Ultimately, the device's machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications. [ABSTRACT FROM AUTHOR]

3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs in tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common forward and inverse problems, relevant types of structures, and design space and responses in 3D/4D printing. Second, we review recent works that have employed a variety of ML approaches for the inverse design of different mechanical responses, ranging from structural properties to active shape changes. Finally, we briefly discuss the main challenges, summarize existing and potential ML approaches, and extend the discussion to broader design problems in the field of 3D/4D printing. This paper is expected to provide foundational guides and insights into the application of ML for 3D/4D printing design. [ABSTRACT FROM AUTHOR]

"The Book of Secrets in the Results of Ideas", usually called "The Book of Secrets" is a codex containing drawings and descriptions of thirty-one artifacts attributed to the engineer Alī Ibn Khalaf al-Murādī, who lived in Andalusia in southern Spain at the beginning of the 11th century. This manuscript is one of the first written testimonies that describe medieval mechanisms with complex precision. The aim of this work is to reconstruct and study from a historical and technological point of view two of the ancient artifacts contained in the "Book of Secrets", the "Fortress Demolisher" and the "Magic Well". The "Fortress Demolisher" is a war machine designed to demolish the upper part of the walls or towers and consists of a battering ram mounted on a platform that can be raised several meters above the ground using a scissor mechanism. The "Magic Well" is a mechanism with several pulleys whose purpose is to raise water several meters from the bottom of a well by collecting a small portion of rope. To reconstruct the two artifacts, a digital model and a detailed study of the geometry and operation of the mechanisms, were first carried out. 3D printing, using the Fused Deposition Modelling technology, made the physical reproduction. The digitization and physical reproduction by 3D printing of the mechanisms represent an update of the traditional ways of transmitting knowledge. The use of these new technologies makes the knowledge more accessible, understandable and attractive. [ABSTRACT FROM AUTHOR]

Appropriate design and machining of mechanical part are of great importance to guarantee the comprehensive quality of product. In this paper, a rational approach of design and machining of mechanical part with multiple objectives is formulated by combining the probability‐based method for multi‐objective optimization with sequential optimization. The probability‐based method for multi‐objective optimization is employed to transfer the problem of multiple objectives into a single objective one in the respect of probability theory, while the "sequential algorithm for optimization" is used to conduct successive deep optimization quantitatively. By performing the assessment of preferable probability of each scheme and the "sequential algorithm for optimization", the optimal design of materials processing is thus completed rationally. Furthermore, the approach is used to deal with thermal design of spindle system for computerized numerical control grinder, optimal designs of large floor‐type milling and boring machine spindle box and bending performance of B‐pillar under condition of side impact as examples, respectively. The results indicate the rationality of the approach. [ABSTRACT FROM AUTHOR]

Super-resolution (SR), a technique to increase the resolution of images, is a pre-processing step in the pipelines of applications of Earth observation (EO) data. The manual design and optimisation of SR models that are specific to every possible EO use case is a laborious process that creates a bottleneck for EO analysis. In this work, we develop an automated machine learning (AutoML) method to automate the creation of dataset-specific SR models. AutoML is the study of the automatic design of high-performance machine learning models. We present the following contributions. (i) We propose AutoSR4EO, an AutoML method for automatically constructing neural networks for SR. We design a search space based on state-of-the-art residual neural networks for SR and incorporate transfer learning. Our search space is extendable, making it possible to adapt AutoSR4EO to future developments in the field. (ii) We introduce a new real-world single-image SR (SISR) dataset, called SENT-NICFI. (iii) We evaluate the performance of AutoSR4EO on four different datasets against the performance of four state-of-the-art baselines and a vanilla AutoML SR method, with AutoSR4EO achieving the highest average ranking. Our results show that AutoSR4EO performs consistently well over all datasets, demonstrating that AutoML is a promising method for improving SR techniques for EO images. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods have gained widespread attention in optimizing nanostructures for target transport properties and discovering unexpected physics. Here, an ML method is developed to discover and experimentally confirm binary CeO2‐MgO aperiodic multilayers (AMLs) for thermal barrier coatings with significantly enhanced reflectance. The effect of varying AML design parameters like total thickness, average period, and randomness in layer thicknesses, on the spectral and total reflectance is demonstrated. Introducing aperiodicity in layer thicknesses is shown to lead to a broadband increase in spectral reflectance due to photon localization. Since the number of possible AML structures increases exponentially with total thickness, a Genetic Algorithm optimizer is developed to efficiently discover AMLs with enhanced reflectance, for total thicknesses of 5–50 µm. Surprisingly, all the optimized structures show an odd number of layers with a CeO2 layer at both ends, deviating from the traditional way of designing binary superlattices with paired layers. The optimized AML and a reference periodic superlattice of 5 µm thickness are fabricated by Pulsed Laser Deposition and characterized by optical reflectance measurements. The fabricated AML, despite considerable fabrication uncertainty, still enhances the reflectance to 48% from 40% of the reference superlattice, validating the effectiveness of our ML‐based optimization process. [ABSTRACT FROM AUTHOR]

An optimal electric machine design task can be posed as a constrained multi-objective optimization problem. While the objectives require time-consuming finite element analysis, constraints, such as geometric constraints, can often be based on mathematical expressions. This article investigates this mixed computationally expensive optimization problem and proposes a computationally efficient optimization method based on evolutionary algorithms. The proposed method always generates feasible solutions by using a generalizable repair operator and also addresses time-consuming objective functions by incorporating surrogate models for their prediction. The article successfully establishes the superiority of the proposed method over a conventional optimization approach. This study demonstrates how a complex engineering design task can be optimized efficiently for multiple objectives and constraints requiring heterogeneous evaluation times. It also shows how optimal solutions can be analysed to select a single preferred solution and harnessed to reveal vital design features common to optimal solutions as design principles. [ABSTRACT FROM AUTHOR]

In view of the problems that FMECA in the design stage of CNC machine tools does not take into account the complexity of the designed functional structure, does not compare the weight of risk factors, and is not easy to distinguish the harm degree of failure mode, CBWM, and DEA are applied to the study of FMECA in the design stage of CNC machine tools. Based on the complexity of functional structure in machine tool design, score the factors of each potential failure mode, form a decision unit, re‐formulate the scoring table, and subdivide the severity into machine tool hazard (M), personal hazard (P), environmental hazard (E), and buyer satisfaction (B). The factors influencing risk factors were defined as the probability of failure mode (O), machine tool hazard (M), personal hazard (P), environmental hazard (E), and buyer satisfaction (B). On the basis of the scoring of each factor, the functional structure complexity (C) is taken as the input index, and the risk factor is taken as the output index. The CBWM method is applied to determine the weight of each risk factor, and DEA method is used to calculate the efficiency of each decision making unit. Finally, a new method for calculating risk priority number RPN is proposed by integrating the score value of factors, weight value of factors, and efficiency value of fault mode, and the hazard degree of fault mode is ranked according to the risk priority coefficient. Taking a machining center spindle system as an example, the proposed method is applied to analyze the fault mode and its influence, and the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

The aim of the present study was to examine the sensitivity of several movement features during running to exhaustion in a subject-specific setup adopting a cross-sectional design and a machine learning approach. Thirteen recreational runners, that systematically trained and competed, performed an exhaustive running protocol on an instrumented treadmill. Respiratory data were collected to establish the second ventilatory threshold (VT2) in order to obtain a reference point regarding the gradual accumulation of fatigue. A machine learning approach was adopted to analyze kinetic and kinematic data recorded for each participant, using a random forest classifier for the region pre and post the second ventilatory threshold. SHapley Additive exPlanations (SHAP) analysis was used to explain the models' predictions and to provide insight about the most important variables. The classification accuracy value of the models adopted ranged from 0.853 to 0.962. The most important feature in six out of thirteen participants was the angular range in AP axis of upper trunk C7 (RTAPu) followed by maximum loading rate (RFDmaxD) and the angular range in the LT axis of the C7. SHAP dependence plots also showed an increased dispersion of predictions in stages around the second ventilatory threshold which is consistent with feature interactions. These results showed that each runner used the examined features differently to cope with the increase in fatigue and mitigate its effects in order to maintain a proper motor pattern. [ABSTRACT FROM AUTHOR]

THIS ARTICLE INVESTIGATES DESIGN OPTIMISATION in the automotive field using machine learning (ML). A thin-walled crash box under axial impact is studied and the design parameters are optimised for front-impact crash tests. This study is based on geometrically and physically nonlinear shell theory, finite element analysis (FEA), dynamic buckling analysis and design optimisation using ML. An artificial neural network framework consisting of various ML methods is developed. A generative adversarial network is established for data generation and reinforcement learning is implemented to automate exploration of the design parameter. This ML framework is proven to determine optimal parameters under predefined crashworthiness constraints. [ABSTRACT FROM AUTHOR]

This research aimed to address the issue of physical damage caused by long-term work in high-saline fields by electrifying sunflower growing machinery and designing control systems. Firstly, the method of establishing the equation of motion is used to obtain the parameters to carry out motorization modification, and then the control system is built using STM32 as the main controller, which has the functions of control, data acquisition, and alarm. The feasibility and stability of the study are verified by using a model vehicle to conduct tests of straight-line driving and steering in a simulated high-saline field. [ABSTRACT FROM AUTHOR]

When designing and manufacturing machines and installations for processing, transporting and storing grain and seeds, it is necessary to know some of their basic physical and mechanical characteristics. They are also necessary for the correct selection of the operating modes of the facilities in the grain warehouses. In this paper, an engineering and technical classification of the mass-grown cereals, legumes and oilseed crops in Bulgaria is presented. For the most used varieties and hybrids, density of grains (seeds), hectoliter mass, angle of natural slope, and angle of friction on smooth materials are determined. [ABSTRACT FROM AUTHOR]

The metering of industrial manufacturing liquids is constantly being updated. Using an automated system, metered dispense can now be more accurate to its theoretical value. This system can help reduce the difference between theoretical and metered volumes in manufacturing applications. This paper describes an automated liquid dispensing machine that uses an AT Mega 328 microcontroller to control the metered volume to the programme set point. The machine's design and implementation include hardware systems assembled from various modules with various functions, such as a microcomputer, power supply, liquid level detection, liquid discharge, conveyor and container detection, and LCD modules. The data collected by the liquid level detection module is analyzed when the system is started to determine the liquid level in the tank. A user loads empty containers onto the conveyor, which are then transferred to the discharge point. The machine calculates the equivalent time in seconds to open the discharge solenoid valve via its control relay and dispenses the desired volume based on the user set point. The experiment performed on the machine with water as the test liquid revealed that the automated dispensing machine's maximum time permissible is 35 s, which corresponds to an equivalent volume of approximately 700 mL of water. Experiments were conducted to compare the theoretical time required to obtain a specified volume to the time required for the machine to dispense the same volume. The results showed that the machine's accuracy is approximately 97.87 %. Therefore, the machine can be used in beverage manufacturing companies, pharmaceutical industries, and laboratories to dispense and fill specified fluids that meet the machine's specifications. [ABSTRACT FROM AUTHOR]

Developable mechanisms provide unparalleled compactness and deployability. This paper explores the kinematic behavior of developable mechanisms that conform to regular cylindrical surfaces. Design considerations that aid in the dimensional synthesis of these mechanisms are developed and demonstrated through case studies. The design implications, limitations, and opportunities associated with regular cylindrical developable mechanisms are discussed through the lens of both an analytical and graphical methods. [ABSTRACT FROM AUTHOR]

To achieve a fundamental reduction in the carbon emissions associated with grinding machines, it is imperative to systematically explore low-carbon considerations in the design phase. The spindle is a significant contributor to carbon emissions in grinding machines, and an effective approach for reducing carbon emissions is the structural optimization of the spindle. Most of the current optimization methods aim at improving processability without considering the reduction of carbon emissions. In this context, the present study addresses the issue of carbon emissions within the spindle design phase. Initially, the determination of the spindle's carbon emissions function and the selection of the optimization objective were undertaken. The structural factors that have a significant influence on the optimization objective were identified as optimization variables. Subsequently, the optimization objective function was established through the application of the fitting method. Finally, the proposed model was refined through the utilization of an enhanced whale algorithm. The findings indicate an 8.22% reduction in carbon emissions associated with the spindle, accompanied by marginal enhancements in both static and dynamic spindle performance. The concluding section of this paper deliberates on the impact of structural parameters on the specified objectives, thereby providing insights for the optimal design of the spindle. [ABSTRACT FROM AUTHOR]

This paper presents a method for workshop-suited identification of thermo-elastic errors of three-axis machine tools (MTs) using on-machine measurement. For this purpose, a new design for a three dimensional artifact is presented. Using the artifact, it is possible to identify all relevant geometric errors of three-axis portal milling machines in table design. The method presented is therefore suitable for the one-time identification of geometric errors caused by manufacturing inaccuracies, assembly-related deviations and wear, and for the identification of thermally induced geometry errors over a long-term period. Since the artifact is scalable in size, it can be easily adapted to different machine sizes. The presented method thus represents a universal and inexpensive possibility to measure geometric and especially thermo-elastic errors. Within the scope of this paper, an error model was established for the investigated machine type and an artifact design was derived on the basis of this model. The suitability of the artifact for identifying thermo-elastic errors has also been investigated experimentally. For this purpose, an artifact made of thermo-invariant material (Invar) was produced. This artifact was measured cyclically on a MT with three linear axes, which was exposed to a controlled thermal load in a temperature chamber. The positions of the geometric features were collected using a 3D touch trigger probe. In addition, the ambient temperatures were recorded. The results of this measurement are discussed in this paper. • A novel method for workshop-suited identification of thermo-elastic errors of three-axis machine tools is presented. • A kinematic error model is derived. • A procedure for errors identification using the artifact and a 3D touch trigger probe is developed. • Measurement uncertainty analysis. • Experimental investigation of the suitability of the method for identifying thermo-elastic errors. [ABSTRACT FROM AUTHOR]

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Mo del Ca libration, and Val idation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector's heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous. This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression. The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results. • Experimental investigation of a scroll compressor. • Model calibration and validation using full factorial design and optimal experimental design. • Comparison of automated model development by Optimal Experimental Design and Machine Learning. • Test time reduction up to 80 % while still keeping reasonable accuracy. [ABSTRACT FROM AUTHOR]