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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]

We consider the context of machining systems design where both equipment selection and line balancing decisions have to be taken in order to minimise the total cost of the system. The designed flow line employs multi-positional machines with rotary tables where vertical and horizontal machining modules can be used for the realisation of machining processes. For this challenging optimisation problem in production research, we develop an innovative mathematical model based on a mixed-integer linear programme and a heuristic algorithm for an approximate solution. An extensive numerical experiment is conducted in order to evaluate the performances of the proposed mathematical model and the developed heuristic. The obtained results show that the decision makers can use the elaborated methods for solving efficiently even large-scale industrial problems. [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]

Sun coral, identified as Tubastraea coccinea and Tubastraea tagusensis, are invasive marine species originally from the Pacific Ocean that have become a significant threat to native biodiversity and ecosystems in Brazil. The rapid growth and unique reproduction strategies of Tubastraea species have facilitated their success as invasive species. In the past two decades, Brazil has experienced a notable loss of biodiversity, partially attributed to invasive species, as reported by the Brazilian Platform on Biodiversity and Ecosystem Services. Current management efforts primarily involve manual removal, often employing sledgehammers and chisels, carried out by environmental government agencies, in partnership with NGOs and volunteers. However, the need for more efficient management strategies emphasizes the important role of engineering and technology in improving environmental management. To address this issue, our work focuses on the development and prototyping of two underwater tools: an impact hammer and a rotating brush. These tools are designed with ergonomic features, operating time, and size considerations suitable for use by SCUBA divers. Our prototypes have successfully met the required specifications and offer potential for large-scale manufacturing, promising more effective measures for controlling invasive sun coral in Brazil. [ABSTRACT FROM AUTHOR]

Cashew (Anacardium occidentale L.) plays an important role in global agriculture, particularly in commercializing the cashew kernel. Nevertheless, kernel extraction poses a challenge due to the nut variable shape and size. Characterizing the properties of the cashew nut becomes a pivotal input for the advancement of processes and machinery. This work presents a comprehensive characterization of raw and steamed cashew nuts from Puerto Carreño, Colombia, with focus on morphological, physical, and mechanical properties. Cashew nut samples from different farms in the region were collected. Morphological properties, including size and shape, were analyzed by measuring length, width, and thickness for 100 nuts from each supplier, both in their raw state and after steaming. Geometric diameter and sphericity were calculated, and qualitative shape characterization was performed. Physical properties, such as mass, bulk density, true density, and moisture content, were also measured. Angle of repose and coefficient of friction were determined to assess flow and frictional behavior. Additionally, compressive mechanical properties were evaluated through uniaxial compression testing along three orientations. The findings indicated significant differences in morphological and mechanical properties between raw and steamed cashew nuts. Conversely, the results demonstrated that cashew nuts from Vichada region exhibit a noteworthy degree of dispersion and larger dimensions than those from Asia and Africa, comparable to those from Brazil. The findings guide machinery design in the cashew industry and offer insights into the physical, morphological, and mechanical properties of cashew nuts in Puerto Carreño, contributing to understanding and enabling quality assessment, processing optimization, and product development. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra‐low scale junctionless gate‐all‐around (JLGAA) field‐effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm‐scale JLGAA FET devices. The influence of design parameter variations and high‐k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures‐of‐merit (FoMs), the subthreshold swing (SS) factor and ION/IOFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting ION/IOFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both ION/IOFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for ION/IOFF and 92.6% for SS, showcasing the model's robustness in these classification tasks. [ABSTRACT FROM AUTHOR]

The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin state, thus influencing interactions with polysulfides and resulting in diverse Li-S bond breaking and lithium migration barriers. Using a carbon-coated Fe/Co electrocatalyst (synthesized using recycled Li-ion battery electrodes as raw materials) at the positive electrode of a Li | |S pouch cell with high sulfur loading and lean electrolyte conditions, we report an initial specific energy of 436 Wh kg−1 (whole mass of the cell) at 67 mA and 25 °C. The atomic-level interactions among electrocatalytic sites in Li | |S batteries remain unclear. Here, authors propose a multiview machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, thus enhancing the understanding of electrocatalytic sites. [ABSTRACT FROM AUTHOR]

Addressing the challenges of limited passability and adaptability encountered by existing pipe robots in navigating through tee and four-way junction pipes, This article designs a pipe robot based on the principle of metamorphism. This robot is designed with a variable number of linkages, constituting the core of its system structure. Through this innovative approach, it achieves movement in diverse types of pipelines by altering its own structural configuration. Kinematic analysis is conducted using both the Denavit-Hartenberg (D-H) method and screw theory, providing expressions for the forward kinematics in terms of the robot's positional and attitudinal states, as well as geometric solutions for the inverse kinematics. Simulations conducted in MATLAB and ADAMS software demonstrate consistent postural changes of the robot, validating its design and capabilities. Prototype experiments in a 500 mm diameter pipeline further confirm the robot's structural design rationality and its ability to navigate through tee and four-way junction pipes by altering its posture. The results indicate that this pipe robot effectively navigates through straight, vertical tee, and horizontal four-way junction pipes, offering a viable solution for efficiently maneuvering through complex pipeline junctions. [ABSTRACT FROM AUTHOR]

Observational data with massive sample sizes are often distributed on many local machines. From an experimental design perspective, investigators often desire to identify the effect of new treatments (even ML algorithms) on many blocks of experimental data. With time requirements or budget constraints, assigning all treatments to each block is not always feasible. This creates incomplete responses with respect to a randomized complete block design (RCBD). These incomplete responses are missing by design. However, whether they can be estimated with missing imputation methods is not well understood. Thus, it is challenging to correctly identify the treatment effects with missing data. To this end, this paper provides a method for imputation and analysis of the responses with missing data. The proposed method consists of three steps: Reconstruction, Imputation, and 'Complete'-data Analysis (RICA). The incomplete responses are imputed with the expectation-maximization (EM) algorithm. The RCBD model is then fitted by the resulting dataset. The identifiability result suggests that the missing may be nonignorable for each block, but the whole data of an incomplete design are missing by design when the design is balanced. Theoretical results on relative efficiency also inform us when the missingness should be imputed for incomplete designs with the role of balanced variance. Applications on real-world data verify the efficacy of this method. [ABSTRACT FROM AUTHOR]

The existing I2C interfaces require significant CPU intervention for data communication. In SOC systems, when using internally integrated I2C modules as masters, software control of I/O ports is necessary to emulate the I2C protocol for data transmission. This paper proposes a Mini I2C bus interface circuit design scheme that supports both master and slave modes. The I2C interface features minimal CPU intervention during data transmission, ease of use, small circuit area, and low power consumption. Additionally, the internal state machine design employs independent finite state machines (FSMs) for master and slave modes, enabling flexible configuration of the I2C module to operate in either mode. In comparison to reference (ShenZhen in Microelectronics Technology CO.BJ8M306A, Datasheet.2019.12.2., 2019), the proposed I2C solution reduces CPU instructions by 50% during data transmission, and by 33% compared to reference (GigaDevice Semiconductor Inc. GD32F1x0, Datasheet, 2022). After DC synthesis, the proposed design occupies only 14% of the area and consumes only 3.6% of the power of the Open Source I2C Design (Forencich in verilog-i2c. GitHub repository. Retrieved from https://github.com/alexforencich/verilog-i2c, n.d.). Therefore, this design scheme is better suited for low-power systems. The proposed design was validated through simulation using Xilinx ISE 14.7 with a SPARTAN 3 FPGA model xc3s500e-5pq208, and finally implemented using Huahong 95 nm CMOS technology, demonstrating high integration and low power consumption. [ABSTRACT FROM AUTHOR]

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study leverages big data and machine learning to identify key parameters influencing student dropout, develop a predictive model, and enable real-time monitoring and timely interventions by educational authorities. Two preliminary trials refined machine learning models, established evaluation standards, and optimized hyperparameters. These trials facilitated the systematic exploration of model performance and data quality assessment. Achieving close to 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having a significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates. [ABSTRACT FROM AUTHOR]

The current investigation examines the influence of footing shape, the base area of footing (A), the mass of footing-machine assembly (m), and eccentric force settings (mee) on the dynamic response and performance of machine foundation systems. Five different footing configurations are employed to perform field block vibration tests involving three square, one circular and one rectangular footing. The experiments are performed at the geotechnical field laboratory of IIT Kanpur, India (N26°30′59.0892″, E80°13′51.6888″). The accuracy and reliability of the experimental results are endorsed by the results obtained from the mass-spring-dashpot (MSD) analysis. In addition, an artificial neural network (ANN) model is created to anticipate the dynamic behaviour of the soil-foundation system. A thorough parametric study demonstrates the efficacy of the developed ANN model. It is revealed from the investigation that the stiffness (k) and the damping ratio (D) of the soil for square foundations increase by 7% and 3%, respectively, with a 40% increase in A. Similarly, the circular foundation exhibits 7 and 3% higher k and 4 and 3% higher D than those obtained for square and rectangular foundations, respectively. For square foundations, a 24% enhancement in m leads to a 42 and 4% increase in k and D, respectively. In contrast, for circular and rectangular foundations, a 13% increase in m results in a 27 and 19% increase in k and D, respectively. In this study, experimental testing, analytical validation, and ANN modelling provide insight into the response of machine foundations under various operating conditions. The results of this study can be utilized to optimize the design of machine foundations. [ABSTRACT FROM AUTHOR]

Bean damage is one of the primary concerns in the pod-breaking process. Studies for pod-breaking machines are ongoing to ensure that the products made from these machines are of good quality. The objective of the study is to determine the physical and mechanical characteristics of the UF-18 pod. The Arduino-based load compressor testing machine was designed and developed to characterize the UF-18 pod. It was found that the average geometric mean diameter, surface area, and sphericity index of 115.37 mm, 41,899.48 mm2, and 0.6372, respectively, and with a variation of ±27.17, ±14538133.04, and ±0.00038 respectively. Furthermore, the cacao pod samples had an average dimension of 181.29 mm, 94.26 mm, 90.01 mm, and 17.44 mm measured for the length, equatorial diameter, intermediate diameter and external thickness, respectively. Different pod sizes and thicknesses require various forces ranging from 36.94 to 92.42 kg (362.38 N to 906.64 N) and time ranging from 6-11 seconds to be able to break the pods. Determining the physical and mechanical properties of cacao pods enables fabricators to design efficient machines, which lessens the force to break and the damage to the beans, thus producing quality beans. [ABSTRACT FROM AUTHOR]

This paper presents a method aimed at improving comprehension of AC electric machine principles by facilitating the learning of the Rotating Magnetic Field (RMF) through visualization tools provided by Finite Element Method (FEM) software. First, traditional methods used in textbooks to explain RMF in electric machines are reviewed, with an analysis of various instructional strategies. Acknowledging the limitations of these conventional approaches and the inherent complexity of RMF comprehension, a novel visualization method is proposed. Understanding RMFs in electric machines is fundamental for electrical engineers due to their crucial role in electric machine design and optimization. While textbooks typically rely on mathematical explanations and simple sketches, advancements in computer and software technology offer opportunities to utilize finite element tools for enhanced comprehension. Through dynamic animations and interactive simulations, emphasis is placed on prioritizing conceptual understanding over mathematical descriptions. Furthermore, this paper explores the development of pre‐simulation models in FEM tools to facilitate RMF learning in AC electric machines, and the process of creating a model to teach rotating magnetic fields in electric machines is carefully outlined. This approach holds promise for engineers seeking a deeper understanding of electric machines and can also be utilized by educators to enhance their teaching methodologies. [ABSTRACT FROM AUTHOR]

In the future ten years, the mining depths of more than 30% metal mines will exceed 1000 m. Deep metal mining is confronted with complex geological conditions such as high in-situ stress, high ground temperature, highly-changeable grounds and strong mining disturbance, which bring severe challenges to roadway construction. Due to the shortcomings of noncontinuous operation, low construction rate and frequent disaster occurrence, traditional drilling-and-blasting method cannot meet the construction requirement, therefore the use of tunnel boring machine (TBM)for continuous mechanical tunnelling becomes the future development direction of deep metal mine roadway construction. However, due to the distinctive engineering geological features and roadway construction requirements, the application of TBM tunnelling technology in deep metal mine roadway construction still suffers from some technological problems and challenges. First, this paper summarizes the three main application difficulties for TBM tunnelling technology in deep metal mine roadway construction; 1) the lack of adaptative TBM type selection and machine design theory; 2) the lack of high efficient TBM tunnelling technology; 3) the lack of prewarning, prevention and control technology for engineering disasters. Then, this paper introduces the current research progresses for TBM tunnelling technology in deep metal mine roadway construction from six aspects, i. e., efficient rock breakage theory for cutterhead and cutters, adaptative TBM type selection and machine design technology, accurate tunnelling performance prediction and evaluation methodology, tunnelling technology for small turning radius or highly-inclined roadways, intelligent prewarning, prevention and control technology for engineering disasters, intelligent decision-making and assisted-driving technology. The above research progresses primarily indicate the development direction for the extensive application of TBM tunnelling technology for rapid roadway construction in deep metal mines. [ABSTRACT FROM AUTHOR]

The industry sector has long been seeking methods to enhance its manufacturing system control, production, and monitoring, while maintaining the quality of its products and reducing costs and time. One method for achieving a more comprehensive understanding of the manufacturing processes is the identification of a corresponding process window (PW). However, there is no globally accepted definition of process window, which is principally used in different manufacturing processes. In some cases, it is combined with operating window or process map concepts. In light of the aforementioned consideration, this article puts forth a definition of process window, drawing upon the various notions and aspects related to the optimal process parameters selection as discussed in the literature. Furthermore, the identification of key controllable process parameters and the criteria to delimit boundaries of desired parts are described as aspects to identify the corresponding process window. Moreover, this paper provides an overview of the techniques used to establish a process window, principally through the application of machine learning techniques (ML) and design of experiments (DOE). Furthermore, the uncertainty intrinsic to the manufacturing process and the methodologies employed to identify the process window are examined. In conclusion, this article emphasises the necessity of transferring these models to new materials or machines, which will require further investigation in future research. [ABSTRACT FROM AUTHOR]

The present study explores the engineering considerations for designing processing machinery for Asparagus racemosus roots, important medicinal plant known for its therapeutic potential thus aligning with achieving the sustainable development goal (SDG) 3 of the United Nations (good health and well-being). Physical, frictional and textural characteristics essential for the design and operation of processing machinery were determined for technological advancements in processing and value addition. The length, width and thickness of whole roots were determined to be 351 ± 56, 5.14 ± 3.95 and 4.93 ± 3.76 respectively, and 253.94 ± 117.30, 6.893 ± 3.705 and 6.39 ± 3.62 mm for trimmed roots. The aspect ratio and sphericity were found to be 35.51 ± 1.12 and 0.093 ± 0.003 respectively, describing an oblong shape. The determined weight variability (8.613-17.432 g), surface area (1529.08-2140.28 mm2) and projected area (890-3340 mm2) of the roots influence the ease of peeling and drying process. Bulk density and true density were estimated to be 298.64 and 604.48 kg/m3 respectively, with porosity of 50.50%. The angle of repose and internal friction coefficient were 33.25 ± 0.303° and 1.03 respectively, whereas external friction against mild steel (0.92) and stainless steel (0.61) helped enhance the understanding of handling characteristics of the roots. The texture of this tuber was found to be softer compared to other tubers, with firmness ranging from 22.43 to 39.34 N and cutting force from 36.18 to 54.79 N. These findings aid in designing efficient processing machinery in alignment with achieving the various SDGs to promote sustainable development and well-being. [ABSTRACT FROM AUTHOR]

The contact stiffness of the mechanical joint usually becomes the weakest part of the stiffness for the whole machinery equipment, which is one of the important parameters affecting the dynamic characteristics of the engineering machinery. Based on the three-dimensional Weierstrass–Mandelbrot (WM) function, the novel normal contact stiffness model of the joint with the bi-fractal surface is proposed, which comprehensively considers the effects of elastoplastic deformation of asperity and friction factor. The effect of various parameters (fractal dimension, scaling parameter, material parameter, friction factor) on the normal contact stiffness of the joint is analyzed by numerical simulation. The normal contact stiffness of the joint increases with an increase in the fractal dimension, normal load, and material properties and decreases with an increase in the scaling parameter. Meanwhile, the fractal parameters of the equivalent rough surface of the joint are calculated by the structural function method. The experimental results show that when the load is between 14 and 38 N∙m, the error of the model is within 20%. The normal contact stiffness model of the bi-fractal surface joint can provide a theoretical basis for the analysis of the dynamic characteristics of the whole machine at the design stage. [ABSTRACT FROM AUTHOR]

This article describes the most important stages of testing the levels of innovative readiness of a new machine and material construction solutions for closed loops of polymer powders in 3D additive manufacturing. The aim of this study was to indicate the state and directions of development of the technology of geometric recycling of polymer powders in 3D additive manufacturing, the need to control the geometric quality of the powders, and the quality of the design of machines for their processing in a closed circuit. The method was aimed at creating a strategy and verifying the stages of development of a new idea for stabilizing the geometric and dynamic design features of polymer powders for additive manufacturing (3D printing). Connections and relationships of the variable design features of powders, machines and devices with variable postulated states of products and processes allowed for the analysis, knowledge, description and development of variables for stabilizing geometric features of polymer powders in recycling. The results show the possibilities of supporting innovative creativity according to the adopted method, allow for determining the level of compliance of the quality of the products of the technical system, the effectiveness of preparatory processes for recycling and the non-toxicity of the products and processes of the new technology and their closed loop. The goal of developing useful design values, according to an integrated method of analysis, assessment and environmental development, was achieved, including the proposal of design features of razor blade shredders, supported by genetic algorithms, and devices for stabilizing polymer powders in additive manufacturing with recycling. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of a double turntable five-axis machine tool during milling are affected by milling loads and machining position, and differ from those at rest. In this paper, the joint stiffness model of the machine tool under milling loads was established. This model was integrated with dynamic modeling of machine tools. The research derived the dynamic parameters of the machine tool under milling loads, and validated the model by the dynamic characteristic test method of the machine tool under milling condition. The prediction of the dynamic characteristics of the machine under milling loads reduces the error of the results by a maximum of 11.5 % compared to the results at standstill. An orthogonal experiment was designed and a k-fold cross-validation method was applied to extend the testing dataset. By integrating the Nadam algorithm, a predictive model for the dynamic characteristics of the double turntable five-axis machine tool was established. This study indicated the influence of milling load and machining position on the machine tool dynamics. This study offered theoretical guidelines for optimal machine tool design and machining process planning. [ABSTRACT FROM AUTHOR]

Rock abrasivity plays an important role in the machine design, construction scheduling, and budgeting of TBM projects. Establishing several faster and simpler estimation equations for the Cerchar Abrasivity Index (CAI) of rocks is, therefore, very important. This study investigated the correlation between the CAI and mechanical properties of rock, rock mass classification parameters, and machine performance. A TBM construction database including 159 tunnel sections is established. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis (0.66 < R2 < 0.76). In this process, a normalized specific energy is proposed to evaluate the machine performance. The results show that the rock compressive strength and brittleness index are the most dependent parameters to explain CAI, and the estimated rock mass strength also indicates a close correlation. In addition, the contribution of a rock mass classification system and machine performance index for estimating CAI cannot be ignored. Finally, the estimation performance of the developed equations is compared and evaluated, and a new method for selecting an optimal model based on ranking is proposed. Since the input parameters of the proposed equations can be readily available at the project planning stage, they are very practical for TBM designers, tunnel designers, and contractors. Highlights: The correlations between the CAI and rock mechanical properties, rock mass classification parameters, and machine performance are investigated. A normalized specific energy index is proposed for assessing the performance of rocks excavation by TBMs. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis. A new method for selecting optimal estimation model based on ranking is presented. [ABSTRACT FROM AUTHOR]

In this study, a novel magnetic gear design is introduced. Unlike conventional magnetic gears that can only achieve a single gear ratio using permanent magnetic poles, the proposed design incorporates electromagnetic coils that can adapt to various control strategies, resulting in a multiple gear ratio for the same machine design. We selected a gear system with five gear ratios to validate the new design. The performance of the proposed design was compared with that of the conventional magnetic gear. While permanent magnet poles offer high torque transmission with a small volume, they cannot provide different gear ratios for the same configuration. Therefore, this work suggests using a single-gear machine based on a fixed number of electromagnetic coils to achieve different gear ratios. This research outlines the design steps, simulation process, and detailed analysis. The results demonstrate the effectiveness of the proposed design strategy, which can be potentially applied to wind turbines, transportation, and other scenarios with comparable success. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model's accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications. [ABSTRACT FROM AUTHOR]

Annatto (Bixa orellana L.), a perennial plant primarily cultivated for its seeds. The pigments extracted from its seeds are used as colourants worldwide. The manual separation of seeds from the annatto pods is a tedious and time-consuming process that requires high manpower and results in low productivity and a poor quality product. To overcome human drudgery and increase productivity, a mechanical decorticator was developed. For designing the decorticator, engineering properties like axial dimensions, geometric mean diameter, sphericity, bulk density, porosity, angle of repose, coefficient of static friction, hardness, and terminal velocity of annatto pods at moisture content of 6 and 9% (w.b.) and annatto seeds at moisture content of 8 and 11% (w.b.) were studied. The optimization was performed by analyzing the effect of machine parameters such as shaft speed (250±1, 270±1 and 290±1 rpm), air velocity in blower (2.3±1, 2.5±1 and 2.8±1 m/s) and feed rate (75±1, 100±1 and 125±1 kg/h) on extraction and cleaning efficiencies and percentage of seed damage using Response Surface Methodology (RSM). The optimized machine parameters of feed rate (125 kg/h), shaft speed (250 rpm) and air velocity in the blower (2.3 m/s) resulted in maximum extraction and cleaning efficiencies of 98.1 and 99.8 wt.%, respectively, with a minimum seed damage of 3.9 wt.% and a desirability value of 0.91. [ABSTRACT FROM AUTHOR]

Wind energy systems are based on synchronous machines, which can support high-speed rotation cases due to possible high-speed values of incoming winds. The machines used are affiliated with permanent magnet machines, whereas speed detection can become difficult. This complication appears when high incoming wind speed values increase the motor temperature, which in turn can influence motor parameters, and especially the stator resistance. This can impact the proposed speed software estimator robustness. The proposed high-speed estimator algorithm is based on the Model Reference Adaptive System (MRAS) estimation method, which can be used for motor speed estimation. This concept includes the reactive power model, which ensures the robustness of the estimator when facing any possible stator resistance variation, even at very high speeds. [ABSTRACT FROM AUTHOR]

Millets are small-seeded grains rich in nutrients, making them an essential part of a healthy diet in many cultures. Millets, being highly nutritious grains, are gaining popularity globally, necessitating efficient processing equipment. However, flaking millets is labour-intensive and time-consuming, often discouraging households from incorporating millets into their regular meals. The machine's design incorporates key features such as ease of use and cost-effectiveness, making it suitable for small-scale millet processing at home. Key features of the millet flaking machine include an efficient flaking mechanism, adjustable flaking settings, easy maintenance and energy efficiency. This machine flakes millets uniformly for versatile culinary uses. Users can adjust the thickness settings and the design facilitates easy cleaning. It is energy-efficient and has a long lifespan. Overall, the millet flaking machine presented in this paper offers a practical solution for households looking to incorporate nutritious millet flakes into their diet without the hassle of traditional flaking methods. Its user-friendly design, efficient performance and affordability make it a valuable addition to household kitchen appliances. [ABSTRACT FROM AUTHOR]

Based on the cultivation environment and material parameters of Pleurotus pulmonarius sticks, a P. pulmonarius stick cutting machine is designed to solve the problem of directly realizing the cutting of P. pulmonarius sticks for a net-shaped mushroom stick cultivation layer frame. The machine consists of three parts: a traveling trolley, an XYZ three-axis moving mechanism, and a clamping and cutting mechanism. Based on the force analysis of the clamping and cutting mechanism and the P. pulmonarius sticks, the key components were designed and selected. A prototype was constructed, and several cutting tests were conducted, with the cutting speed, number of cutting circles, and depth of the cut as the main factors. The success rate of cutting, the roundness of the cut, and the degree of loss in terms of the cutout substrate, were used as the performance indicators. An optimized design, based on orthogonal testing, was employed to determine the optimal parameter combinations for the cutting process. The optimal parameters were found to be a cutting speed of 376.3 r/min, 5.4 cutting circles, and a cutting depth of 4 mm. Finally, a validation test was carried out on the machine, and the results show that under the same parameter conditions, the success rate of cutting was 86.6%, the roundness of the cut was 0.235, and the degree of loss in terms of the cutout substrate was 0.851%, which is consistent with the optimization results. This study provides a reference, including a practical reference, for the design and development of a machine to realize the cutting of P. pulmonarius sticks for a cultivation layer frame. [ABSTRACT FROM AUTHOR]

The high-speed development of the liquor industry brings sorghum demand, which is increasingly strong at the moment. Still, its harvesting mechanization level is low, and with the design of a longitudinal flow sparse and dense curved-teeth sorghum threshing technology, the harvester's work quality is improved by the reduction of seed impurities. This article describes the working principle of the harvester, the overall distribution of threshing elements, and force analysis of the threshing aspects to determine the structure of the threshing elements. The orthogonal test was carried out, with a sparse–dense interphase threshing drum as the research object, selecting operating speed, threshing element bending angle, and threshing element mounting angle as the test factors, with the entrainment loss rate and the net threshing rate as the assessment indexes for the three-factor, three-level test, and the use of Design-Expert to establish a mathematical regression model between the factors and the two indicators, resulting in the following optimized parameters: when the operating speed is 1.0 m·s−1, the bending angle of the threshing element is 80°, and the mounting angle of the threshing element is 45°, the loss rate of entrainment is 1.89%, and the net threshing rate is 95.53%. The machine's design indexes are in line with relevant national standards and can meet the demand for mechanized harvesting of sorghum. [ABSTRACT FROM AUTHOR]

Highlights Composites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML. Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites. [ABSTRACT FROM AUTHOR]

In order to effectively curb the rapid growth trend in Spartina alterniflora in coastal cities of China, this study proposes an innovative mechanical equipment design scheme for eradicating Spartina alterniflora. Based on literature analysis and field research, the AHP (analytic hierarchy process) model is constructed to quantify and prioritize the diverse needs of users for control equipment. Subsequently, the House of Quality (HOQ) in QFD (Quality Function Deployment) is used to analyze the key components and structure of the equipment to ensure its performance and feasibility in practical applications. Finally, combined with the Theory of Inventive Problem Solving (TRIZ), the potential problems encountered in the structural design of the equipment are analyzed, and the corresponding creative principles are applied to solve the contradictions and complete the optimal scheme design. This study, via the acquisition of user needs and further analysis of the machinery's structure, proposes a scheme that can address many problems related to Spartina alterniflora in China and provide new technical ideas for the field of wetland environmental protection. [ABSTRACT FROM AUTHOR]

Shape‐morphing hydrogels can mimic the dynamism of living creatures and are popular for designing soft machines, such as actuators and robots. However, most existing shape‐morphing hydrogels present a single morphing pathway between two shapes, sequential and multistage shape‐morphing in real scenarios has rarely been captured. Although a strategy that assembles functional hydrogel components has been reported, it is likely to simultaneously increase the complexity and weaken the versatility of hydrogel machines. Here, a light‐encoding strategy based on the dynamic coordination between the ferric iron and carboxyl group (Fe3+─COO−) to integrate the frame and actuating units into a single hydrogel without assembly is proposed. The spatiotemporal control of light irradiation is expected to make the light‐encoding process sequential and reprogrammable. Results demonstrate that the light‐encoded patterns determine the morphing route and further activate shape‐morphing owing to swelling mismatch. The actuating units can be successively created by localized irradiation or elaborately turned by re‐coordinating and light‐encoding. This endows the encoded hydrogel with sequential and multistage shape‐morphing behavior, similar to the living creatures. This strategy allows the design of assembly‐free soft machines with sequential and multistage shape‐morphing performance, which will benefit the design of more types of hydrogel machines. [ABSTRACT FROM AUTHOR]

[Objectives] This paper aimed to study the interaction mechanism of straw-soil-strip vertical rotary tillage system in rice stubble field, explore the influence of key operation parameters under the interaction of the three on the clearing and leveling effect of seedbed, and play a guiding role in the integrated straw field management and the optimal design of tillage machinery in the water-drought rotation system. [Methods] A 3D vertical rotation simulation model was constructed based on discrete element method. The process of vertical strip rotation under rice straw mulching was simulated, and the parameter range was optimized and verified by laboratory soil tank test. In the process of strip vertical rotation, the forward speed, the rotational speed of tool shaft and the stubble length were taken as the influencing factors. The response surface method was used to build a mathematical model to analyze the influence of each factor on the quality of the seedbed and carry out the performance optimization test. [Results]When the tillage depth was set to 15 cm, the main and secondary factors affecting the cleaning rate of stubble and the flatness of seedbed were the forward speed, the tool shaft rotational speed and stubble length. The results of multi-objective optimization showed that the quality of seedbed was the best when the tool forward speed was 1.09 m·s-1, the tool shaft rotational speed was 281.5 r·min-1 and the stubble length was 6.89 cm. The straw cleaning rate of seedbed was 81.95% and the seedbed flatness was 8.67 mm. [Conclusions] The interaction model established in this paper was proved to be reliable and could effectively reflect the effect of vertical rotation operation parameters on the clearing of straw in seed bed. [ABSTRACT FROM AUTHOR]

Corn plays a fundamental role in the diet of diverse cultures and has nutritional benefits, being a rich source of carbohydrates, fiber, vitamins and minerals, in addition to containing antioxidants with health properties. In places like Peru and Latin America, where corn production and commercialization are significant for various culinary uses (patasca, tamales, humitas, among others), there is a need to industrialize the peeling process to speed up production and advance technologically, overcoming the manual method disadvantages. Therefore, this research aimed to design and manufacture a precooked corn peeling machine based on the friction of corn with a rough surface, through the incorporation of adjustable paddles and constant flow of clean water to remove impurities, in order to obtain larger quantities of peeled corn production, being able to supply the local market. To design the machine, the VDI 2221 methodology was applied, which included the creation of a list of requirements and a function structure. The state of the art corn shelling technology was evaluated using techniques such as the black box, white box and morphological matrix approach to analyze solution options. Design aspects such as shaft diameter, vane strength, cylinder volume, drive belt selection, number of belts and bearing choice were considered. Finally, manufacturing materials were selected. The design of the corn peeling machine was obtained. In addition, data on peeling speed and amount of corn peeling were found through washing tests using different corn grains. It was found that the optimum time is 7 minutes, which considerably improves the artisanal method, which requires more time and physical effort by the producer and for the manufacture of the machine, non-polluting materials will be used for corn, such as stainless steel AISI 304, which does not corrode in contact with water. It was concluded that, with the design of a corn pericarp peeling machine gave a 96.3% efficiency in the peeling process, which shows that it is possible to peel corn for health care, also for use in different gastronomic dishes. In addition, the manufacture of the machine is possible since the materials used can be easily found in the local market and have low costs. [ABSTRACT FROM AUTHOR]

Mechanised non-contact, non-destructive imaging methodologies have revolutionised plant phenotyping, increasing throughput well beyond what was possible using traditional manual methods. Quantifying the variation in post-harvest material such as seeds and fruits, usually the economically important part of the crop, can be critical for commercial quality assessment as well as breeding and research. Therefore, reliable methods that gather metrics of interest, quickly and efficiently, are of widespread interest across sectors. This study focuses on evaluating the phenotyping capabilities of the Cgrain Value™, a novel grain imaging machine designed for quality and purity assessment and used primarily in commercial cereal production and processing. The performance of the Cgrain Value™ in its generation of high-throughput quantitative phenotypic data is compared with a well-established machine, MARVIN, assessing repeatability and reproducibility across a range of metrics. The findings highlight the potential of the Cgrain Value™, and some shortcomings, to provide detailed three-dimensional size, shape, and colour information rapidly, offering insights into oat grain morphology that could enhance genome-wide association studies and inform the breeding efforts in oat improvement programmes. [ABSTRACT FROM AUTHOR]

To address the gap in research on the mechanical properties of peanuts in the harvesting period, the mechanical properties of peanut seedling vines, pods, and peduncles were studied during the peanut harvesting period. The moisture content of peanut pods, peduncles, and seedling vines was measured, yielding values of 36.03%, 66.76%, and 77.95%, respectively. The tensile characteristic parameters between the pods and peduncles, peduncles and peduncles, and the seedling vines and peduncles during the harvesting period were determined using an electronic universal testing machine at three different loading speeds: 10 mm/min, 20 mm/min, and 30 mm/min. The test results indicated that the peduncle–peduncle, peduncle–pod, and peduncle–seedling vine detachment forces were 19.91 N, 17.41 N, and 8.62 N. The mechanical properties of peanuts during the harvesting period differed from those of peanuts that were excavated and sun-dried. Peanut-digging and turning machines should be designed based on the detachment force required to separate the peduncles from the pods, which, at a loading speed of 20 mm/min, is 17 N. This examination of the mechanical properties of peanuts during the harvesting period could have significant practical implications and a lasting influence on enhancing the efficiency and quality of peanut harvesting, refining harvesting machinery design, and advancing agricultural modernization. [ABSTRACT FROM AUTHOR]

Here an integrated machine was designed with the stepped transportation tea harvesting and grading, in order to improve the quality and efficiency of mechanized harvesting for the high-quality tea. The integrated machine utilizes the length differences of different types of tea bud of Longjing 43 to achieve tea bud grading while efficiently harvesting. The reciprocating cutting blades were utilized to pick the tea bud. Multiple sets of dividing parts and belt groups were combined to achieve the clamping and directional transportation of harvested tea bud. Two sets were selected from the stepped belt groups, and the length difference among the different types of tea bud was used to classify the tea bud during transportation. The distance between the blades and belts was used to separate the tea bud unsuitable for picking. Firstly, the theoretical analysis and preliminary experiments were carried out to determine the factors influencing the integrity rate of picking new tea bud and the grading success rate. These factors were the reciprocating frequency of the blades, the longitudinal distance of the blades, the driving wheel speed of the first leaf feeding mechanism belt, and the height difference between the belts of the leaf feeding mechanism, respectively. Secondly, a Box Behnken experiment with four factors and three levels was conducted using Design Expert software. A systematic investigation was implemented to evaluate the interactive effects of various factors on the integrity rate of picking new tea bud and grading success rate of the machine. Regression models were established with the integrity rate of picking new tea bud and grading success rate of the machine as the response values. The influencing factors on the integrity rate of picking new tea bud were the blade reciprocating frequency, the driving wheel speed of the first leaf feeding mechanism belt, and the longitudinal distance of the blades. There was an interactive effect between the reciprocating frequency of the blade and the longitudinal distance of the blade. The influencing factors on the grading success rate were the blade longitudinal distance, as well as the height difference between the belt of the leaf feeding mechanism. Furthermore, these parameters were optimized to maximize the integrity rate of picking new tea bud and grading success rate of the integrated machine. An optimal combination was achieved, where the blade reciprocating frequency was 1600 times/min, the blade longitudinal distance was 12.5 mm, the driving wheel speed of the first leaf feeding mechanism belt was 800 r/min, and the height difference between the belts of the leaf feeding mechanism was 31.5 mm. Finally, these parameters were optimized to prepare a prototype for the picking experiments. As such, the stepped transportation tea harvesting and grading integrated machine can be expected to efficiently realize the picking and grading tea bud. The integrity rate of picking new tea bud and grading success rate of the machine reached 77.9% and 88.7%, respectively. The error between the experimental and the prediction value was less than 5%, indicating the reliable optimization. This finding can provide the theoretical basis and technical support to improve the level of mechanized tea harvesting and grading. [ABSTRACT FROM AUTHOR]

Despite the critical role that datasets play in how systems make predictions and interpret the world, the dynamics of their construction are not well understood. Drawing on a corpus of interviews with dataset creators, we uncover the messy and contingent realities of dataset preparation. We identify four key challenges in constructing datasets, including balancing the benefits and costs of increasing dataset scale, limited access to resources, a reliance on shortcuts for compiling datasets and evaluating their quality, and ambivalence regarding accountability for a dataset. These themes illustrate the ways in which datasets are not objective or neutral but reflect the personal judgments and trade-offs of their creators within wider institutional dynamics, working within social, technical, and organizational constraints. We underscore the importance of examining the processes of dataset creation to strengthen an understanding of responsible practices for dataset development and care. [ABSTRACT FROM AUTHOR]

The cylindrical worm is usually manufactured by forming methods, such as whirling or milling; however, the aforementioned methods are restricted to machining time and control machining profile with stock. Therefore, this paper introduces a novel machining process designed for ZC concave worms, simultaneously enhancing machining efficiency and profile control ability. Accordingly, a skiving cutter with multiple inserts on an internal helical gear tooth is proposed. A curve-fitting model of the generating rack is first illustrated to generate the ZC worm flanks when considering grinding stock conveniently. The position of the cutting edge is then analyzed via the geometry of an internal barrel worm to ensure non-interference during the cutting process. In addition, the insert body is generated based on the helicoid motion of the cutting edge. Subsequently, it also presents a machining process to grind the insert body. Furthermore, the skiving process employing multiple inserts on a multi-axis CNC machine is presented. Finally, the meshing and boundary points of the cutting trajectory are concurrently defined during the skiving process. The numerical results demonstrate the high precision and absence of overcutting in skived worm profiles. [ABSTRACT FROM AUTHOR]

This paper discusses the development of an automated sorting machine designed as a comprehensive mechatronics educational project. The project integrates mechanical and electrical design, incorporating a robot arm, a microcontroller, sensors, and actuators. The sorting machine uses color identification to sort wooden blocks of three different colors. The blocks are stacked and dropped onto a conveyor belt by a hopper system that employs a solenoid actuator and a servo to release one block at a time at specific intervals. As the belt runs continuously, each block passes under a color sensor, which monitors the color and signals one of three servo-powered mechanical arms to guide the block into the appropriate chute. Each chute is equipped with a capacitive proximity sensor that sends a voltage signal to the robot controller, queuing commands for the robot to pick up the blocks from the bottom of each chute and return them to the hopper to form a continuously running sorting system. This paper details the design and integration of the system's various elements and the development of the control software. The designed system can drop blocks every 8.05 s, sort each block within 5 s of being sensed, and return them to the sorting system every 12 s. It has a color-sensing accuracy of 97%, with a failure rate of around 7%. The system achieved quick and reliable sorting using various low-cost, accessible, and open-source parts. The project exemplifies a cost-effective solution suitable for mechatronics education, demonstrating the numerous challenges involved in developing automated sorting systems. [ABSTRACT FROM AUTHOR]

The electrification of aviation represents a significant technological frontier, promising substantial advancements in sustainable transportation. This paper presents a comprehensive set of taxonomies that systematically categorize and analyze the multifaceted aspects of electric aviation, with a particular focus on machine-related components and systems. It provides detailed classifications of electric aircraft propulsion systems, power management architectures, and energy storage technologies, offering insight into their design, functionality, and integration challenges. The paper explores the ecosystem of electric aviation, including key stakeholders, use cases, and enabling technologies, which are vital for coordinating machine development strategies and fostering sustainable growth. The creation of business models that cater to the dynamic nature of the industry, emphasizing the role of innovative machine designs in shaping market adoption are discussed in the paper. The study highlights the importance of electric aviation for regional development, outlining predictive models for regional market development that consider machine capabilities and infrastructure requirements. [ABSTRACT FROM AUTHOR]

External-rotor dual-armature flux-switching PM (ER-DA-FSPM) machines have high torque density and decent fault tolerance, making them promising candidates for in-wheel machine applications in electric vehicles. The torque output and optimal design parameters of ER-DA-FSPM machines are affected by the stator/rotor torque ratio, which is the focus of this paper. Firstly, this paper analyzes airgap flux density harmonics of ER-DA-FSPM to provide a clear insight into the torque-generation mechanism. Then, this paper investigates the influence of torque ratio on average torque under the same copper loss. It is found that the average torque decreases with torque ratio increasing due to the reduction of the positive torque component generated by the sixth airgap field harmonics and the rise in the negative torque component from the eighth harmonics. Moreover, this paper also provides the optimal parameter recommendation to guide the machine design. The split ratio should increase, and the arc of PMs should decrease for a larger torque ratio, whilst the other parameters are hardly influenced. Next, this paper makes a comparison among the ER-DA-FSPM machine, external rotor flux-switching PM (ER-FSPM) machine, and surface-mounted PM (ER-SPM) machines. It shows that the ER-DA-FSPM machine, with the torque ratio being 2, can lead to a much larger total torque. In addition, in the event of rotor winding failure, which is more possible due to the existence of slip rings than stator winding failure, the stator can still provide an average torque larger than that of ER-SPM machine and 92.0% that of the ER-FSPM machine, respectively. Finally, the theoretical analysis is verified by the experiments. [ABSTRACT FROM AUTHOR]

This paper addresses the robust downgrading (single-machine) makespan scheduling problem, a scheduling challenge where processing times are augmented at a minimum cost to achieve a specified downgrading level in the makespan. The associated costs are modeled as intervals, and we employ the minmax regret criterion to handle uncertainty. The deterministic case is initially examined with a lineartime algorithm. Subsequently, the robust version of the problem is transformed into a single-variable objective, characterized by a piecewise linear function. Then, we develop a combinatorial algorithm with polynomial time complexity to solve the corresponding problem. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications. Design/methodology/approach: This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process. Findings: The authors' analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples. Originality/value: While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans. [ABSTRACT FROM AUTHOR]

This article presents a machine's design, manufacturing, and control processes for packaging creampuffs into boxes. The system includes a creampuff-dropping module operated by pneumatic cylinders, a horizontal box transposition module controlled by the IAI ROBO Cylinder, and multiple conveyors for handling boxes and creampuffs. The Mitsubishi FX3G PLC acts as the main control unit, enabling efficient system operation and managing its sequential processes. A timer device is integrated to display the duration required to pack a set of three boxes. The system's integration enables accurate creampuff placement within the boxes. The study provides valuable insights for enhancing creampuff packing systems. [ABSTRACT FROM AUTHOR]

The pottery wheel machine is an essential piece of equipment in ceramics manufacturing. This paper presents the design of an intelligent pottery wheel machine aimed at addressing the issues of low clay material utilization and the challenges of pottery throwing operations. Traditional techniques require exceptional craftsmanship and proficient mechanical operation, and existing pottery wheel machines still demand attention to equipment usage and clay material knowledge. The intelligent pottery wheel machine integrates central positioning, automatic clay feeding, and clay storage. The pull mechanism stores and precisely feeds clay, regulating usage and minimizing excess. The automated settings of the clay storage and power push system replace the need for manual assessment of clay density and quality, ensuring balanced and high-quality clay extrusion. The continuous feeding setting allows for automatic addition of clay, saving replenishment time and reducing the labor intensity of adding clay. The lifting mechanism ensures that the clay body remains centered, avoiding repeated manual adjustments and allowing users to focus on creating pottery rather than spending excessive time mastering the centering technique. The machine's lightweight, modular design reduces clay waste, making the process more sustainable. By saving clay materials and improving throwing efficiency, it enhances the success rate of throwing. [ABSTRACT FROM AUTHOR]

Rubber industry is one of the major industries in Malaysia. Rubber stamping machine is a machine that cuts rubber sheets into desired shapes and dimensions, facing challenges due to the elastic properties of rubber sheets. These challenges include long process time and non-identical dimension. This review paper focuses on the design of the rubberstamping machine to address the challenges by reducing process time and producing identical dimensions products. The rubber-stamping machine was fabricated, and analysis was performed to verify its efficiency. The core of the designs is to ensure the user safety, friendliness, and increase productivity and product consistency. Based on the findings from existing studies, the review highlights significant improvements in machine design and operational efficiency. The paper also discusses the impact of these innovations on the competitiveness of rubber stamping operations and provides insights into future directions for research in mechanical design and automation systems. This review can be a crucial resource for developers and manufacturers looking to enhance the efficiency and product quality of rubber-stamping machines, contributing to the advancement of manufacturing practices in the rubber industry. [ABSTRACT FROM AUTHOR]