Machining Simulation Research Articles

Mathematical modeling and simulation of generation milling process to manufacture complex face gear surface through multi-axis machine kinematics

Manufacturing of face gear is a complex process, due to its varying geometry, hence the application is not wide as conventional gear geometry. The developments in analytical tools and kinematics of the CNC machine tools motivate researchers to explore the manufacturing process and associated applications. This article presents an efficient method for the generation milling of spur face gear utilizing a nutating head multi-axis CNC machine tool where the tool swings about an arbitrary axis in an inclined plane. The mathematical model of the disk milling cutter and face gear geometry were derived from the shaper geometry by applying the theory of gearing, equation of meshing and coordinate transformation. The cutter location (CL) data was evaluated by aligning the tool orthogonal unit vector and the tooth surface normal vector at the contact point by calculating the appropriate transformation matrices. The evaluation of machine coordinates on a multi-axis CNC milling machine was analyzed with respect to the machine pivot point and the axes movement during generation milling for the face gear drive was studied on the nutating head machine. The NC data is derived from the CL data using the inverse kinematics and the machining simulation was carried out using suitable software. The tooth surface deviation of a maximum of 43.5 μm was observed between the simulated model and the theoretical model, which is well within the requirement of roughing. The generation milling process with the proposed machine configuration is concluded as a profound face gear manufacturing technique.

A Boundary-Preserving Non-Conformal Mapping for Radial Rotor Eccentricity in Finite Element Simulations of Electrical Machines

A Boundary-Preserving Non-Conformal Mapping for Radial Rotor Eccentricity in Finite Element Simulations of Electrical Machines

Theoretical trends in the dynamics simulations of molecular machines across multiple scales.

Over the past few decades, molecular machines have been extensively studied, since they are composed of single molecules for functional materials capable of responding to external stimuli, enabling motion at scales ranging from the microscopic to the macroscopic level within molecular aggregates. This advancement holds the potential to efficiently transform external resources into mechanical movement, achieved through precise control of conformational changes in stimuli-responsive materials. However, the underlying mechanism that links microscopic and macroscopic motions remains unclear, demanding computational development associated with simulating the construction of molecular machines from single molecules. This bottleneck has impeded the design of more efficient functional materials. Advancements in theoretical simulations have successfully been developed in various computational models to unveil the operational mechanisms of stimulus-responsive molecular machines, which could help us reduce the costs in experimental trial-and-error procedures. It opens doors to the computer-aided design of innovative functional materials. In this perspective, we have reviewed theoretical approaches employed in simulating dynamic processes involving conformational changes in molecular machines, spanning different scales and environmental conditions. In addition, we have highlighted current challenges and anticipated future trends in the collective control of aggregates within molecular machines. Our goal is to provide a comprehensive overview of recent theoretical advancements in the field of molecular machines, offering valuable insights for the design of novel smart materials.

Review of molecular dynamics simulations in laser-based micro/nano-fabrication.

Laser technology is integral to the advancement of micro/nano-fabrication. While laser machining offers numerous advantages over alternative micro/nano-fabrication techniques, several challenges and bottlenecks continue to impede its large-scale industrial implementation. In response to these constraints, the molecular dynamics (MD) method has emerged as a formidable tool for optimizing the process parameters of laser micro/nano-fabrication and investigating alternative laser-based micro/nano-fabrication techniques. In this review, the application of MD in laser-based micro/nano-fabrication is comprehensively examined, including numerical simulations of short-pulse, long-pulse, continued laser and hybrid laser machining. The corresponding MD simulation schemes for lasers with different pulse widths are outlined. The mechanisms of laser-material interactions across diverse processing scenarios and the complete process of laser-based micro/nano-fabrication are also elucidated. Furthermore, the prevailing challenges in this domain and potential solutions are discussed, with future research directions being charted based on current knowledge and technological advancements.

In silico simulation of hyperoside, isoquercetin, quercetin, and quercitrin as potential antivirals against the pNP868R protein of African swine fever virus.

African swine fever (ASF) causes disease in pigs with up to 100% mortality rates. There is no effective vaccine to protect against it. This study aimed to perform in silico docking of ASF virus (ASFV) pNP868R protein with potential flavonoid ligands to identify ligands that interfere with mRNA cap formation. The ASFV pNP868R protein was tested with hyperoside, isoquercetin, quercetin, and quercitrin in this in silico simulation. ASFV pNP868R protein was extracted from the Research Collaboration for Structural Bioinformatics Protein Data Bank (RCSB PDB) database with PDB ID 7D8U (https://www.rcsb.org/structure/7D8U). Standard ligands were separated from proteins using UCSF Chimera 1.13. The standard ligand was redocked to protein using AutoDockTools 1.5.6 with the AutoDock4 method for validation. In the docking process, the grid box size was 40 × 40 × 40 Å3 with x, y, and z coordinates of 16.433, -43.826, and -9.496, respectively. The molecular docking process of the proposed ligand-protein complex can proceed if the standard ligand position is not significantly different from its original position in the viral protein's pocket. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (RoG) of the hyperoside with the lowest energy binding need to be analyzed with molecular dynamics using Groningen machine for chemical simulation 5.1.1. Molecular docking and dynamic simulation revealed that hyperoside had the most stable and compact binding to the pNP868R protein. Hyperoside binds to the protein at the minimum energy of -9.07 KJ/mol. The RMSD, RMSF, and RoG values of 0.281 nm, 0.2 nm, and 2.175 nm, respectively, indicate the stability and compactness of this binding. Hyperoside is the most likely antiviral candidate to bind to the pNP868R protein in silico. Therefore, it is necessary to test whether this flavonoid can inhibit mRNA capping in vitro and elicit the host immune response against uncapped viral mRNA.

Proposal of Digital Twin-Enabled New Product Design Framework

Digital twin (DT) has provided numerous opportunities for product development businesses to leverage data acquired from the physical model. Ideally, many companies are improving their use cases by using the collected physical data in a virtual model. Thanks to emerging communication technologies, 5th generation mobile network (5G), the internet of things (IoT), industrial internet of things (IIoT) sensors, etc., which help to collect data from the physical product and send data to cloud computing, called big data. With the gap in the non-availability of standard architecture, many companies are using big data and building their digital models. This paper proposes a novel DT-enabled new product design framework that has built an interaction between the DT and product design environments. This framework enables the data to be captured from various physical entities, stored in a centralized location, and pushed into the digital entity, which processes the data and converts the raw data into information used for decision-making. In the digital entity, multiple technologies are used to process data into information, such as artificial intelligence (AI) and machine learning (ML) models, mathematical models, and simulation models. The processed data that emerges from these models are called synthetic or DT data; this data is later used for control / monitoring and visualization / interaction purposes. In this paper, the data classified as visualization and interaction is integrated with the new product design framework in all four design stages, viz., concept design, detailed design, design verification, and redesign, with various data contributions to the design. A case study was discussed by applying the DT data processed using the AI / ML model in the concept design phase. Using synthetic data, the medium-duty truck frame assembly�s design concept was validated using ten load cases iteratively, and the design was finalized. The results are encouraging that the overall weight of the frame assembly was optimized by 9% without impacting the strength and factor of safety of the frame assembly.

E-validation - Unleashing AI for validation.

The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article proposes an artificial intelligence (AI)-based approach, termed e-validation, to optimize and accelerate the NAM validation process. E-vali-dation employs advanced machine learning and simulation techniques to systematically design validation studies, select informative reference chemicals, integrate existing data, and provide tailored training. The approach aims to shorten current decade-long validation timelines, using fewer resources while enhancing rigor. Key components include the smart selection of reference chemicals using clustering algorithms, simulation of validation studies, mechanistic validation powered by AI, and AI-enhanced training for NAM education and implementation. A centralized dashboard interface could integrate these components, streamlining workflows and providing real-time decision support. The potential impacts of e-validation are extensive, promising to accel-erate biomedical research, enhance chemical safety assessment, reduce animal testing, and drive regulatory and commercial innovation. While the integration of AI and machine learning offers sig-nificant advantages, challenges related to data quality, complexity of implementation, scalability, and ethical considerations must be addressed. Real-world validation and pilot studies are crucial to demonstrate the practical benefits and feasibility of e-validation. This transformative approach has the potential to revolutionize toxicological science and regulatory practices, ushering in a new era of predictive, personalized, and preventive health sciences.

Optimizing construction project management via genetic algorithms – A state-of-the-art review

Construction projects involve complex trade-offs between conflicting objectives such as minimizing duration and costs while ensuring feasibility, safety, and quality. This paper reviews research on leveraging genetic algorithms (GAs)–adaptive bioinspired search techniques–to enhance decision-making in construction management spanning planning, scheduling, and control. The versatility of GAs in tackling combinatorial optimization problems is established through various applications optimizing workflows, resource allocation, and crew coordination for enhanced project performance. Empirical analyses validate GAs generating superior trade-off solutions regarding project timeline, budgetary compliance, profitability, and safety over conventional methods such as mathematical programming or regression. Challenges remain regarding further enhancing the solution quality and fine-tuning the GA parameters. Nonetheless, by efficiently exploring vast solution spaces, GAs enable the scientific translation of construction data into actionable insights for augmenting complex project decisions. Exciting innovation opportunities exist in synergistically hybridizing GAs with promising contemporaries, such as machine learning, simulation, and robotics. Collectively, the existing literature underscores GAs' immense and relatively untapped potential of GAs to develop next-generation AI-powered tools for optimizing construction productivity, cost efficiency, sustainability, and lean automation.

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Unveiling Advanced Computational Applications in Quantum Computing: A Comprehensive Review

The field of advanced computing applications could experience a significant impact from quantum computing, which is a rapidly developing field with the potential to revolutionize numerous areas of science and technology. In this review, we explore into the various ways in which complex computational problems could be tackled by utilizing quantum computers, including machine learning, optimization, and simulation. One potential application of quantum computers is in machine learning, where they could be used to improve the accuracy and efficiency of algorithms. Complex optimization problems, such as those encountered in logistics and finance, can be addressed using quantum computers as well. Furthermore, the utilization of quantum computers could enable the simulation of intricate systems, such as molecules and materials, leading to significant applications in fields like Physics and Material Technology. Although quantum computers are currently in the early stages of development, they possess the potential to propel numerous areas of science and technology forward in a significant manner. Further research and development are needed to fully realize the potential of quantum computing in the field of advanced computing applications.

Dynamic price competition market for retailers in the context of consumer learning behavior and supplier competition: Machine learning-enhanced agent-based modeling and simulation

This study analyzes the impact of consumer learning behavior and supplier price competition on retailer price competition in a complex adaptive system. Using machine Learning-enhanced agent-based modeling and simulation, the study applies fuzzy logic and genetic algorithms to model price decisions, and reinforcement learning and swarm intelligence to model consumer behavior. Simulations reveal that different learning behaviors result in different retailer competition patterns, and that supplier price competition affects the strength of retailer price competition. Simulation results demonstrate that consumer learning behavior influences retailer competition, with self-learning consumers leading to higher-priced partnerships, and collective-learning consumers leading to a shift in price competition among retailers. In contrast, perfect rationality consumers result in low-price competition and the lowest average margin and profit. Additionally, the competitive price behavior of suppliers impacts retailers' price competition patterns, with supplier price competition reducing retailer price competition in the perfect rationality consumer market and enhancing it in the self-learning and collective-learning consumer markets, leading to lower average prices and profits for retailers. This study presents a simulated market for price competition among suppliers, retailers, and consumers that can be expanded by subsequent scholars to test related hypotheses.

Determinant of Dynamics and Interfacial Forces in Ultraprecision Machining of Optical Freeform Surface through Simulation-Based Analysis.

This study delves into the intricacies of ultraprecision machining, particularly in the context of machining optical freeform surfaces using Diamond Turning Machines (DTMs). It underscores the dynamic relationship between toolpath generation, hydrostatic bearing in DTMs, and the machining process. Central to this research is the innovative introduction of Metal Matrix Composites (MMCs) to replace the traditional materials used in designing linear bearings. This strategic substitution aims to dynamically enhance both the accuracy and the quality of the machined optical freeform surfaces. The study employs simulation-based analysis using ADAMS to investigate the interfacial cutting forces at the tooltip and workpiece surface and their impacts on the machining process. Through simulations of STS mode ultraprecision machining, the interfacial cutting forces and their relationship with changes in surface curvatures are examined. The results demonstrate that the use of MMC material leads to a significant reduction in toolpath pressure, highlighting the potential benefits of employing lightweight materials in improving the dynamic performance of the system. Additionally, the analysis of slideway joints reveals the direct influence of interfacial cutting forces on the linear slideways, emphasising the importance of understanding and controlling these forces for achieving higher-precision positioning and motion control. The comparative analysis between steel and MMC materials provides valuable insights into the effects of material properties on the system's dynamic performance. These findings contribute to the existing body of knowledge and suggest a potential shift towards more advanced precision forms, possibly extending to pico-engineering in future systems. Ultimately, this research establishes a new standard in the field, emphasising the importance of system dynamics and interfacial forces in the evolution of precision manufacturing technologies.

Hot deformation behavior of Ni50.4Ti34.6Hf15 high-temperature shape memory alloy

Due to their extreme requirements, conventional Ni–Ti shape memory alloys (SMAs) are no longer suitable for aerospace applications. Adding Hf to Ni–Ti SMAs can significantly enhance their suitability for aerospace applications, particularly in high-temperature environments. In this regard, the hot/thermal deformation behavior of Ni50.4Ti34.6Hf15 high-temperature shape memory alloys (HTSMAs) was systematically investigated by using a Gleeble-3800 thermocompression simulation machine. The results prove that the Ni50.4Ti34.6Hf15 alloys have more strain sensitivity than the temperature during the hot/thermal deformation process. In detail, the flow stress increases with the increase in strain rate and decreases with the increase in deformation temperature. From the microstructure evolution point of view, the recovery effect will increase with the increase in deformation temperature, and the recrystallization effect will increase with the increase in strain rate. Following the obtained stress-strain curves, the flow behavior of Ni50.4Ti34.6Hf15 alloys during hot/thermal deformation was established. A modified constitutive equations model was proposed using careful consideration of deformation temperature and strain rate. It was found that the calculated strain activation energy was 363.945 kJ/mol in the range of 700∼800 °C and 512.489 kJ/mol in the range of 900∼1000 °C. Based on the constitutive equation, the calculated values were consistent with the experimental values, which indicated reliable analysis with high prediction accuracy. Furthermore, the thermal processing maps under different conditions were constructed, and it was determined that the optimum hot/thermal deformation processing parameters of Ni50.4Ti34.6Hf15 alloys were 940∼1000 °C and 0.01–0.04 s−1.

A New Design of Automatic Rabbit Feeding System

In Malaysia, rabbit farming is growing quickly, but there are many difficulties with it. One of these difficulties is the time-consuming procedure of feeding the rabbits. Thus, this paper presents an automatic rabbit feeding system machine that is able to reduce the time required to feed the rabbit. t can operate automatically, which will cut down significantly on labor requirements. force. The device used the eight-step George E. Dieter design, which outlines the best way to create an autonomous rabbit feeding system. SolidWorks software was used to translate the concept into the simulated machine system with the overall dimensions of 1820 mm x 1184 mm x 1630 mm. Analysis engineering and Life Cycle Assessment (LCA) on the simulated machine was conducted and it was found that the machine was able to discharge and distribute pellets up to 4 cages. As a result, this product is able to solve the issue that rabbit farmers encounter when it comes to feeding their animals.

Reconstruction Algorithm for Complex Dexel Models Based on Composite Block Partition

Abstract In machining simulations, dexel models are often used to represent objects to achieve high accuracy and real-time performance. However, this approach leads to the loss of original surface information and topological relationships, thereby affecting the visualization effect of simulations. Furthermore, existing reconstruction methods have the drawbacks of generalization or redundancy. To reconstruct the surface of dexel models efficiently and accurately, this paper proposes an algorithm based on “composite block” partition, which converts the dexel model into a polyhedral model. The algorithm begins by partitioning the entire dexel model within the grids into several composite blocks based on the “Connectivity Principle” and generating their end faces. Subsequently, the transitional zone’s surface is reconstructed based on the connectivity relationships of the boundaries of composite blocks. Finally, an optimization process refines the boundaries to generate smoother side faces at a low computational cost. The paper first validates the algorithm’s reconstruction capability and the effectiveness of edge refinement through the reconstruction of various dexel models with different precision levels. It is observed that edge refinement does not introduce excessive additional computation, doubling the overall efficiency compared to existing algorithms. Furthermore, by changing model volumes and performing separate reconstructions, it’s noted that as the volume increases, the incremental growth in conversion time gradually decreases. This makes the algorithm particularly suitable for reconstructing large-scale complex dexel models. Finally, the application of this algorithm in virtual-real simulation systems and industrial digital twin systems is briefly introduced.

Machine learning-based assessment and simulation of land use modification effects on seasonal and annual land surface temperature variations

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Incorporating Simulators into a Training Curriculum for Forestry Equipment Operators

Forestry equipment simulators offer opportunities for new operators to become familiar with operating logging machines as well as a promising solution to the high costs of training forestry equipment operators. Current literature lacks a synthesis on how best to train forestry equipment operators using simulators. The goal of this review was to identify effective ways to incorporate forestry equipment simulators into an equipment operator training curriculum. We analyzed a total of 14 independent studies in which construction and forestry equipment operators were trained on simulators and engaged in discussions with nine professionals in the field of heavy equipment operator training. In this review, traditional machine training and simulator training practices are introduced. Then, four key aspects of skill acquisition for forestry equipment operators are identified. Information collected from peer-reviewed literature and discussions with industry experts are used to consider how each aspect of skill acquisition is addressed in both traditional training using real machines and simulator-based training. Drawing on these sources, benefits and drawbacks of traditional machine training and simulator-based training for forestry equipment operators are synthesized and discussed. Finally, a model for an integrated and adaptive training curriculum that incorporates principles and technologies from both traditional machine training and simulator training is presented.

Implementation of Finite State Automata to Simulation of Automatic Clothes-Folding

Inconsistencies and differences clothes-folding methods are often a major problem in the military. Everyone has thier method to fold and their method is still traditional. Besides, the neatness of the clothes in the military is emphasized. This method certainly took up their time and energy then the folding was different to each other. To solve this problem, a simulation machine is made to fold the clothes automatically. This system works using Finite State Automa type Non-Deterministic Finite Automata. Finite State Automata are used because they are simple and do not require storage space. The result of this research is a design of an automatic clothes-folding that can be implemented properly and correctly for UNHAN RI’s students. This automatic clothes-folding has succeeded to create consistent folds even though user tried with different clothes.

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

Alfalfa (Medicago sativa L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation R2 values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an R2 value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa R2 values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m2 and 332.32 g/m2, and the biases were −0.47 g/m2 and −2.24 g/m2, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.

Exploring player’s feelings on clarinet mouthpiece geometry using artificial blowing machines and airflow simulations

The mouthpiece geometry of single-reed instruments has been known to have a great impact on sound quality and playability. Although many researchers have investigated the effects of the geometry using artificial blowing machines or user case studies, few attention has been paid to the essential cause of physical changes in the instrument. In this study, we propose a method combining the artificial blowing machine and numerical flow simulations to explore the player’s feelings about the airflow and sound generation in the clarinet mouthpiece. With the artificial blowing machine, the mouth pressure, as well as lip force, were systematically changed using a pressure regulator and strain gauge. At particular conditions, the numerical flow simulation was conducted by solving fluid–structure interactions with the Navier–Stokes equations and the one-dimensional beam equation. The playability of the mouthpiece was shown by the changes in oscillation threshold pressures and sound spectral characteristics, whereas the cause of the changes could be described by the airflow pressures inside the mouthpieces. The results suggest that the combination of the artificial blower and the flow simulations can be effectively used for clarifying the cause of changes in players’ feelings.