Hasil untuk "Machine design and drawing"

Xiaopeng Yuan, Haoyu Wang, Lei Zhang

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems.

Zhonglin Cao, Omid Barati Farimani, Janghoon Ock et al.

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Katie Winkle

Much Human-Robot Interaction (HRI) research is, in fact, speculative. The relatively far-future horizon of pervasive robot deployment means we are, in effect, designing for a best guess of what the world might look like in the future based on how it looks now. In doing so, we are contributing to the bringing about of that world over others. Drawing on notions of speculative design and sociotechnical imaginaries, this pictorial presents snippets of speculative, sociotechnical fiction writing, which (re-)imagine human-machine interactions in the socialist utopia future world of William Morris' 19th century novel “News from Nowhere”. Each fiction snippet is paired with references to the current academic literature that inspires it, in addition to some takeaway discussion points for the HRI community of today. My hope is to assert speculative, sociotechnical thinking as legitimate HRI design practice, and to demonstrate the value it can bring to our field.

Lina Wang, Xingxing Liu, Wenjie Tian et al.

As the structural complexity of machined components increases and the pace of product updates accelerates, the demands for machining precision in CNC machine tools are becoming increasingly rigorous. Consequently, the continuous enhancement of machining accuracy in machine tools presents a significant challenge that must be addressed within the realms of machine tool innovation and the development of manufacturing equipment. This paper conducts a comprehensive investigation into the tolerance optimization allocation method for geometric accuracy in precision horizontal machining centers utilizing interval theory. Initially, a mapping model is developed to represent each source of geometric error and the overall spatial error, drawing upon multi-body system theory. Subsequently, the global maximum interval sensitivity of each geometric error source in relation to the overall spatial model is analyzed. Finally, an interval optimization model for geometric accuracy is formulated based on interval optimization theory, employing a genetic algorithm to address the accuracy allocation problem associated with various error sources in machine tools.

Roberto López-Chila, Henry Abad-Reyna, Joao Morocho-Cajas et al.

Standardized driving cycles such as the FTP-75 fail to represent traffic conditions in cities like Guayaquil, where high congestion and varied driving behaviors are not captured by external models. This study aimed to develop representative driving cycles for the city’s most congested urban routes, covering the north, south, center, and west zones. Using the direct method, real-world trips were conducted with an M1-category vehicle equipped with an OBDLINK MX+ device, allowing real-time data collection. Driving data were processed through OBDWIZ software Version 4.30.1 and statistically analyzed using Minitab. From pilot tests, the appropriate sample size was estimated, and normality tests were applied to determine the correct measures of central tendency. The final representative cycles were constructed using a weighting criteria method. The results provided quantified evidence of variations in average speed, idle time, and acceleration patterns across the routes, which were transformed into representative driving cycles. These cycles provide a more accurate basis for emission modeling, vehicle certification, and transport policy design in congested cities such as Guayaquil, and this is the applied impact that is highlighted in our contribution. Furthermore, the developed cycles provide a foundation for future research on emission modeling and the design of sustainable transport strategies in Latin American cities.

Qinglin Li, Ruihai Wan, Zhaoyue Wu et al.

As the main working part of a combine harvester, the cleaning device affects the cleaning performance of the machine. The simulation of flow fields in a cleaning chamber has become an important part of the design. Currently, post-processing analyses of flow field simulation still rely on the researchers’ experience, so it is difficult to obtain information from post-processing automatically. The experience of researchers is difficult to describe and disseminate. This paper studied an intelligent method to analyze simulation result data which is based on the object detection algorithm and the reasoning mechanism. YOLOv8, one of the deep learning object detection algorithms, was selected to identify key-point data from the flow field in a cleaning chamber. First, the training dataset was constructed via scatter plot drawing, data enhancement, random screening, and other technologies. Then, the flow field in the cleaning chamber was divided into six key areas by identifying the key points of the flow field. And, an analysis of the reasonable wind velocity in the areas was conducted, and the cleaning results of the grain were obtained by using the reasoning mechanism based on rules and examples. Finally, a system based on the above method was established in Python 3.10 software. With the help of the method and the system in this paper, the flow field characteristics in a cleaning chamber and the effects of wind on the cleaning effect can be obtained automatically if the physical properties of the crop, the geometric parameters of the cleaning chamber, and the working parameters of the machine are given.

M. D. Nardo, G. Gallicchio, F. Cupertino et al.

This paper presents a systematic comparative design study of permanent magnet assisted synchronous reluctance (PMaSyR) machines for a light traction application aimed at considering an holistic approach for a given outer envelope and cooling system specification. Electromagnetic, structural and thermal aspects are all accurately considered in a computationally efficient manner using a hybrid analytical-finite element (FE) design approach. The SyR machine geometries providing the maximum torque with increasing number of poles are identified and their performance deeply investigated with full FE analysis. The study has been carried out considering several requirements in terms of base and maximum speeds with the aim of drawing general design considerations. Results reveal that the optimal pole number from a torque perspective depends on the considered maximum speed. The reasons behind this behavior are fully investigated as well as how and why the optimal geometries change. The optimal SyR machines are then compared also considering the insertion of permanent magnets within the rotor slots with the aim of maximizing the constant power speed range. The rationales behind the selection of the machine to manufacture are then outlined including aspects related to efficiency and demagnetization under the worst short circuit condition in the entire torque-speed range. The optimized machine (after a FE-based design refinement) has been manufactured and tested on an instrumented test bench validating the proposed design approach and the deduced design insights.

Yeong-Maw Hwang, Tsung-Han Ho, Yung-Fa Huang et al.

Deep drawing has been practiced in various manufacturing industries for many years. With the aid of stamping equipment, materials are sheared to different shapes and dimensions for users. Meanwhile, through artificial intelligence (AI) training, machines can make decisions or perform various functions. The aim of this study is to discuss the geometric and process parameters for A7075 in deep drawing and derive the formable regions of sound products for different forming parameters. Four parameters—forming temperature, punch speed, blank diameter and thickness—are used to investigate their effects on the forming results. Through finite element simulation, a database is established and used for machine learning (ML) training and validation to derive an AI prediction model. Importing the forming parameters into this prediction model can obtain the forming results rapidly. To validate the formable regions of sound products, several experiments are conducted and the results are compared with the prediction results to verify the feasibility of applying ML to deep drawing processes of aluminum alloy A7075 and the reliability of the AI prediction model.

Pan Li, Yuwei Wang, Xiaoxian He et al.

Abstract Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent, wearable interactive devices. However, significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight hardware. Here, we introduce a mass-produced, wearable, and interactive photochromic fiber that provides uniform multicolored light control. We designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the fiber. The impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission direction. In addition, we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single fiber. We prepared fibers on mass-produced kilometer-long using the thermal drawing method. The fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as needed. This work provides a new perspective and inspiration to the existing field of fiber display interaction, paving the way for future human–machine integration.

Mesbah Uddin, Nazarii Olkhovskyi

The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></semantics></math></inline-formula> SST turbulence model.

Therese Biedl, Anna Lubiw, Jack Spalding-Jamieson

We give an algorithm to morph planar graph drawings that achieves small grid size at the expense of allowing a constant number of bends on each edge. The input is an $n$-vertex planar graph and two planar straight-line drawings of the graph on an $O(n) \times O(n)$ grid. The planarity-preserving morph is composed of $O(n)$ linear morphs between successive pairs of drawings, each on an $O(n) \times O(n)$ grid with a constant number of bends per edge. The algorithm to compute the morph runs in $O(n^2)$ time on a word RAM model with standard arithmetic operations -- in particular no square roots or cube roots are required. The first step of the algorithm is to morph each input drawing to a planar orthogonal box drawing where vertices are represented by boxes and each edge is drawn as a horizontal or vertical segment. The second step is to morph between planar orthogonal box drawings. This is done by extending known techniques for morphing planar orthogonal drawings with point vertices.

Zheng Wang

Tensile machine is a very sophisticated test equipment, which can test the mechanical properties of some materials. In the drawing machine, the clamping device is to prevent the relative displacement and vibration phenomenon in the process of tensile test. The clamping device is the most important part of the structure of the test machine, and the main task of the clamping device is to control the degree of freedom of the specimen needs to be clamped, so as to further add loads to the various specimens to be held, and finally complete the mechanical property test of the material. But at present, the existing fixture mechanism is mainly hydraulic, pneumatic, mechanical fixture. At present, the existing mechanical fixture still has bending stress during the clamping process, which will affect the accuracy of the experiment. Therefore, a clamping mechanism with adjustable clamping force and flat pushing is designed. An electric control type fixture mechanism is very rare, so in this paper, an electrically controlled fixture is designed on this basis. The main contents are as follows: (1) On the basis of the existing fixture types, the working mode of the fixture is studied, and the working principle related to the fixture is mastered. (2) The main components of the fixture mechanism are analysed, the design scheme is determined, and the selection of the components of the drive part and the selection of the related components within the fixture mechanism is completed. ( 3) The force analysis of the main body of the fixture is carried out by using theoretical mechanics knowledge, and the relevant calculations are completed. The solid modeling of the whole mechanism is completed by using SolidWorks. The force analysis of key parts is carried out by using ANSYS software, and the correctness and safety of material selection and design are verified. The main parts of the fixture mechanism to complete the corresponding force analysis, and the theoretical calculation results are compared and verified, and finally realize the electric control of the fixture mechanism and complete the design of the fixture mechanism.

Peigang Jiao, Yinnan Li, Liu Yang

T Virtual prototype simulation analysis based on 3D modeling software and dynamic simulation software is a high-efficiency and low-cost product development model. The design and assembly of the chassis mechanism of the weeding machine are carried out in the 3D drawing software CATIA. The chassis of the weeding machine is imported into ADAMS and the constraints between the components are added to establish a virtual prototype for simulation. A variety of convex terrains are set for simulation analysis in ADAMS. The curve data is exported in the PostProcess module of ADAMS, and the comparison curve is established in Origin for analysis. The results show that the obstacle-crossing performance of the chassis of the weeding machine in the orchard meets the design requirements. The double-wishbone suspension applied to the chassis of the weeding machine can significantly improve its passing ability and stability.

Ruturaj C. Gaikwad, R. Joshi, Shankar A. Sail et al.

The design and development of a CNC (Computer Numerical Control) writing and drawing machine using Inkscape, a popular open-source vector graphics software, involves several key steps. This paper presents an overview of the process, which includes defining requirements, selecting components, designing the mechanical structure and electronics, assembling and testing, developing software with Inkscape integration, fine-tuning and optimization, incorporating safety features, documenting, manufacturing, and conducting testing and quality control.

Maria Schäfer, Martin Sturm, Steffen Exler

Jährlich stehen allein in Deutschland 10 Millionen Tonnen Spreu als Reststoff zur Verwertung zur Verfügung. Auf der Basis eines innovativen Ernteverfahrens, welches die Bergung des ungereinigten Getreidekorns in einem Gemisch aus Spreu und gehäckseltem Stroh ermöglicht, kann das Spreu, in Form der neuartigen Biomassemischung SpreuStroh, einer wertschöpfenden Nutzung zugeführt werden. In der vorliegenden Arbeit werden verschiedene Aufbereitungsmöglichkeiten und damit Nutzungsmöglichkeiten des Gemisches SpreuStroh aufgezeigt. Basierend auf einer umfangreichen Charakterisierung des SpreuStrohs werden zunächst Anwendungsmöglichkeiten für den Landwirt aufgezeigt. Herauszuheben ist dabei der Nutzen für den Umweltschutz, ermöglicht durch die systembedingte Entnahme von Unkrautsamen und damit die Verringerung des Einsatzes von Pflanzenschutzmitteln. Durch eine weitere Aufbereitung des Gemisches SpreuStroh werden höherwertige Nutzungsmöglichkeiten, wie die Verwendung im 3D-Druck oder als Zuschlagsstoff in Faser-Kunststoff-Verbunden, aufgezeigt.

Pacana Andrzej, Czerwińska Karolina

A turbulent manufacturing market, especially in the metal industry, determines the quality of products and the level of production efficiency, which contributes to a company's market position and competitiveness. The aim of the study was to develop a model to define a manufacturing company's current market position using KPIs in relation to a key product - gearbox casting. The company's position was defined in terms of the relationship occurring between technological capabilities and market position. An additional aim of the study was to identify critical determinants and, ultimately, to identify conditions for strengthening market position. As a test of the proposed model, the position of the analysed company (in terms of technological capabilities and market position) was defined as "Search for occasions" - box 9 within the 3×3 matrix. Technological determinants that weaken the company's position (low level of maintenance capacity and long production cycle time) and determinants with a strong negative impact on market position (low level of human resource development) were identified. An element of novelty is the use of KPIs as variables determining the position of the company within the 3×3 matrix, which is indicative of a specific technological position in the market. Further lines of research will concern the determination of appropriate KPIs in relation to the identified critical areas of the company. Subsequent steps will concern the implications of the model in relation to the company's other key aluminium alloy castings.

Cedric De Boom, Michael Reusens

Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.

Maximilian P Niroomand, David J Wales

The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable.

Sindhu Rakasiwi, H. Kusumo

The plotter is a graphic printer that draws using ink pens, the plotter is also the first output device capable of printing large-sized images of architectural and engineering drawings. A plotter is a type of printer that is specifically designed to produce computer output in the form of images or graphics. There are many types of plotters, one of which is the pen plotter, which has one or more colored pens for drawing on transparent paper or plastic and producing a line output. CNC (Computer Numerical Controller) is one of the machine control tools in large industrial factories. With the CNC can control most of the tools one example is as a controller of a 2-dimensional plotter. This research designs a 2-dimensional plotter using Arduino UNO-based CNC. This 2-dimensional plotter has an accuracy of 97.947% and a precision of 99.985%. This 2-dimensional plotter is capable of operating up to a distance of 4cm with a resolution of 0.01cm.

Takeshi Yoshida, Yuki Onishi, Takuya Kawahara et al.

Abstract In this study, we propose a method to automate fruit harvesting with a fruit harvesting robot equipped with robotic arms. Given the future growth of the world population, food shortages are expected to accelerate. Since much of Japan’s agriculture is dependent on imports, it is expected to be greatly affected by this upcoming food shortage. In recent years, the number of agricultural workers in Japan has been decreasing and the population is aging. As a result, there is a need to automate and reduce labor in agricultural work using agricultural machinery. In particular, fruit cultivation requires a lot of manual labor due to the variety of orchard conditions and tree shapes, causing mechanization and automation to lag behind. In this study, a dual-armed fruit harvesting robot was designed and fabricated to reach most of the fruits on joint V-shaped trellis that was cultivated and adjusted for the robot. To harvest the fruit, the fruit harvesting robot uses sensors and computer vision to detect and estimate the position of the fruit and then inserts end-effectors into the lower part of the fruit. During this process, there is a possibility of collision within the robot itself or with other fruits depending on the position of the fruit to be harvested. In this study, inverse kinematics and a fast path planning method using random sampling is used to harvest fruits with robot arms. This method makes it possible to control the robot arms without interfering with the fruit or the other robot arm by considering them as obstacles. Through experiments, this study showed that these methods can be used to detect pears and apples outdoors and automatically harvest them using the robot arms.