Simulation Of Process Research Articles

Effect of water on asphaltene-precipitation behavior of CO2-crude oil mixtures during CCUS processes

Water can extensively exist in reservoirs. Since the solubility of CO2 in water is not negligible under reservoir conditions, the aqueous phase should also be considered during the phase-behavior simulations of the CO2 flooding process. With the presence of an asphaltene phase and an aqueous phase, four phases (i.e., a vapor phase, a hydrocarbon phase, an asphaltene phase, and an aqueous phase) can coexist at a given thermodynamic equilibrium. In this study, a robust and efficient four-phase vapor–liquid-liquid-aqueous equilibrium calculation algorithm is applied to conduct four-phase vapor–liquid-asphaltene-aqueous equilibrium calculations by assuming asphaltene is a dense liquid phase. The algorithm is first validated by comparing the calculated asphaltene precipitation data with the presence of water against the experimental asphaltene precipitation data documented in the literature. The validation shows that the calculation results agree well with the experimental data, indicating that such algorithm can make reliable predictions of asphaltene precipitation with the presence of water. The validated algorithm is subsequently used to construct pressure–temperature (PT) and pressure-composition (Px) phase diagrams to study the effect of water on asphaltene precipitation under different pressure/temperature conditions and under the injection of CO2. We can conclude from the calculated results that although adding water to the reservoir fluid can cause obvious changes in the PT phase diagrams, it does not have a noticeable influence on the asphaltene precipitation onsets. However, it can be seen from the calculated Px phase diagrams that the presence of water during the CO2 flooding process can make asphaltene precipitated more easily. The maximum amount of asphaltene precipitation is decreased by the presence of water due to the fact that a considerable amount of CO2 is dissolved in the aqueous phase instead of the liquid hydrocarbon phase.

Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains

In next-generation manufacturing, the introduction of a surrogate model is crucial for constructing a digital twin. This trend is no exception in powder industry. Various mesh-free approaches such as the discrete element method (DEM) are versatile tools for facilitating simulation-based digital twins of powder industries. Unfortunately, the DEM-based high-fidelity simulation of actual industrial processes poses a significant challenge because of the prohibitive computational costs. In this context, the surrogate model has emerged as a promising technique to solve this problem. However, surrogate modeling of powder processes within arbitrarily shaped boundaries is substantially impossible. To resolve this essential problem in the existing surrogate models, we propose a novel surrogate model, namely, a signed distance function-based graph neural network (SGN), to accelerate the simulations of granular flows. In the SGN, the wall boundary is efficiently modeled by combining the graph structure with a signed distance function. Validation studies were conducted in typical powder systems, such as a sandglass, and a rotating-paddle mixer. A systematic comparison of the simulation results between the high-fidelity and SGN simulations shows that the proposed SGN accurately simulates granular dynamics, with a remarkable increase in computational efficiency. This study significantly contributes to the next-generation manufacturing in the powder industry.

Thermally Regulated Flocculation-Coalescence Process by Temperature-Responsive Cationic Polymeric Surfactant for Enhanced Crude Oil-Water Separation

Polymeric surfactants play a crucial role in the flocculation-assisted coalescence process due to their unique bridging effect. However, the steric hindrance induced by their large molecules severely impedes the coalescence of oil droplets. Herein, temperature-responsive polymeric surfactants (quaternary ammonium chitosan-g-PNIPAM, Q-g-PN) with thermally-modulated structure were designed by integrating thermal responsive moieties onto cationic chitosan. The as-prepared Q-g-PN exhibited enhanced oil-water separation efficiency through a thermally regulated flocculation-coalescence process. At low temperatures, the thermal-responsive Q-g-PN remains in a flexible hydrophilic extended state, facilitating the flocculation of dispersed oil droplets through bridging via electrostatic interaction. At high temperatures, the Q-g-PN structure collapses into a hydrophobic coil state, reducing steric hindrance and enhancing hydrogen bonding to asphaltenes, thus effectively promoting oil droplet coalescence. Bottle tests demonstrated that the demulsification performance of Q-g-PN could reach up to 94% with the Q-g-PN concentration only of 0.001wt% via the proposed temperature-regulated flocculation-coalescence process, which was further confirmed by investigating oil droplet behavior and phase transition of Q-g-PN during through molecular dynamic (MD) simulation of the reaction process. This work presents new insights into enhancing oil-water separation efficiency by regulating the flocculation-coalescence process through precise modulation of the molecular interactions between polymer surfactants and emulsion droplets.

GSOR11 Presentation Time: 12:20 PM: Practice Change from PDR to HDR for Gynecological Brachytherapy Delivery: Process Design and Simulation for Streamlined Transition

GSOR11 Presentation Time: 12:20 PM: Practice Change from PDR to HDR for Gynecological Brachytherapy Delivery: Process Design and Simulation for Streamlined Transition

Impact of Refined Boundary Conditions of Land Objects on Urban Hydrological Process Simulation

Urbanization has led to an increase in impervious areas and, consequently, an increase in the surface runoff volume and runoff rate. This has exacerbated urban flooding and highlighted the importance of modeling urban hydrological processes. The Waterview Community of Hangzhou City (WCHC) was taken as the study area, and three scenarios were developed: the original scenario, the rough description scenario, and the fine description scenario. The urban hydrological processes were simulated through a coupled model incorporating actual measurements and four design precipitation events (1-year, 5-year, 10-year, and 20-year return periods). The results show the following: (1) The refined depiction scenario has the highest accuracy in terms of measured precipitation, with an average error of 0.54 cm. (2) During different precipitation return periods, the refined depiction scenario shows the smallest range of accumulated water, with a more realistic distribution. On average, it differed from the original scenario by 21.45% and from the rough depiction scenario by 32.18%. (3) The simulation results after the refinement of the feature boundaries are more reasonable in terms of the flow rate and flow direction, indicating that the simulation results have better dynamics. The results showed that refined boundary conditions improved the accuracy and dynamics of urban hydrological simulations, especially in terms of their reflection of actual water accumulation under varying precipitation conditions.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Phase equilibrium and separation process of byproduct vinyl acrylate in the ethylene vapor phase method producing vinyl acetate

Vinyl acrylate is one of the byproducts in the ethylene vapor phase method for the production of vinyl acetate (VAc), which not only has a toxic effect on the catalyst but also affects the purity of the product. Currently, phase equilibrium data for vinyl acrylate with water are missing, making the destination of vinyl acrylate in the distillation process uncertain. This has led to a lack of effective removal means, which restricts the optimization of the ethylene vapor phase method. This paper uses a method combining quantum chemistry and molecular dynamics simulation (MD) to investigate the existential form of the water molecule. And based on the ab initio calculation of the first principle, the force field parameters of the special water molecular structure are calculated, and the TIP4P-P force field that can describe the special water structure is established. Using the Gibbs Ensemble Monte Carlo (GEMC) method, the reliability of the improved water molecule force field for modeling the phase equilibrium of a binary system is first verified. Further, vapor–liquid phase equilibrium data for the vinyl acrylate-water binary system at 110 kPa and 150 kPa are calculated and the corresponding x-y and T-x-y phase diagrams are plotted. After performing the thermodynamic consistency test, the phase equilibrium data are fitted to obtain the binary interaction parameters of vinyl acrylate with water. The fitted parameters are applied to the process simulation of the VAc refining process to optimize the conditions and obtain an effective separation scheme for vinyl acrylate. The concentration distribution of vinyl acrylate in the distillation column is investigated, thus clarifying the destination of vinyl acrylate in the distillation process and facilitating the optimization of the ethylene vapor phase method.

Numerical fluid structure simulation analysis of temperature-dependent dynamic viscosity with a simplified deep drilling model

ABSTRACT This paper simulates the temperature-dependent dynamic viscosity of the cutting fluid’s flow, thus analysing the approximated real behaviour in a simplified model. In order to take the heat transfer between the workpiece and the cutting fluid into account, the Finite Element Method (FEM) was bidirectionally coupled. A mathematical formula for determining the dynamic viscosity of the cutting fluid as a function of temperature was used for the temperature evolution. Simulations were performed using the standard k-ω-SST, k-ω-SST-SAS, and k-ω-SST-DES turbulence models for comparison. The results show that the influence of dynamic viscosity and temperature plays an important role and should therefore not be neglected in process simulations. For example, increasing the temperature from T = 25 °C to T = 150 °C reduced viscosity by 95%. The modelling approach presented here is suitable for future analysis simulations and can be applied not only to different drill geometries but also to numerous machining processes where dynamic viscosity plays an important role.

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control

Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes.

Simulation and modeling of methylene blue dye removal by an agri-food waste (Peanut shells)

Our study explores the potential of utilizing peanut shells, an abundant and low-cost food waste, as a natural adsorbent for the removal of industrial dyes, specifically methylene blue. We conducted a detailed investigation of the adsorption process, which revealed that it conforms to both Langmuir and Freundlich isotherms, indicating the ability of peanut shells to adsorb methylene blue effectively. To optimize the adsorption yield, we employed a Box-Behnken experimental design. This approach allowed us to assess the impact of several key factors, including the mass of the adsorbent, dye concentration, pH, temperature, stirring rate, and the ionic strength of salts in the solution. The mathematical model and simulation of the adsorption process helped us identify the best conditions for maximizing the dye removal. Our experiments resulted in a maximum adsorption efficiency of 97%, achieved under optimal conditions: 1.5 g of adsorbent, a methylene blue concentration of 40 mg/L, pH 11, temperature of 65°C, 0 mg/L ionic strength, and a stirring rate of 165 rpm. These findings suggest that peanut shells are a viable, eco-friendly alternative for dye removal, offering a sustainable solution for wastewater treatment and contributing to waste reduction.

Research on the Tensile and Impact Mechanical Properties of Millet Ear Petals

In response to the low threshing efficiency of millet ear petals, this study investigated the tensile and impact mechanical properties of millet petals during the millet threshing process. Jingu 21, Zhangza 16, and Changza 466 were used as experimental subjects to study the effects of tensile angle and growth part on fracture strength, and to determine the influence of impulse and growth part on drop and breakage rates. The results indicated that both growth part and tensile angle have a highly significant impact on the tensile fracture strength of the millet petals. The tensile fracture strength decreases with the increase in the tensile angle, and increases with the growth part from top to bottom. The variety, growth part, and impulse significantly affect the impact drop and breakage rates of the millet petals, with the main factors affecting the drop rate being impulse, variety, and growth part, in that order. When the impulse is 2.296 N·s, the threshing effect for Jingu 21 is optimal, with a drop rate of 65.091% and a breakage rate of 13.487%. This research provides theoretical insights into the simulation of the millet ear threshing process and the optimization of the performance of millet threshing equipment.

Various optimized artificial neural network simulations of advection-diffusion processes

The aim of this research is to describe an artificial neural network (ANN) based method to approximate the solutions of the natural advection-diffusion equations. Although the solutions of these equations can be obtained by various effective numerical methods, feed forward neural network (FFNN) techniques combined with different optimization techniques offer a more practicable and flexible alternative than the traditional approaches to solve those equations. However, the ability of FFNN techniques to solve partial differential equations is a questionable issue and has not yet been fully concluded in the existing literature. The reliability and accuracy of computational results can be advanced by the choice of optimization techniques. Therefore, this study aims to take an effective step towards presenting the ability to solve the advection-diffusion equations by leveraging the inherent benefits of ANN methods while avoiding some of the limitations of traditional approaches. In this technique, the solution process requires minimizing the error generated by using a differential equation whose solution is considered as a trial solution. More specifically, this study uses a FFNN and backpropagation technique, one of the variants of the ANN method, to minimize the error and the adjustment of parameters. In the solution process, the loss function (error) needs to be minimized; this is accomplished by fitting the trial function into the differential equation using appropriate optimization techniques and obtaining the network output. Therefore, in this study, the commonly used techniques in the literature, namely gradient descent (GD), particle swarm optimization (PSO) and artificial bee colony (ABC), are selected to compare the effectiveness of gradient and gradient-free optimization techniques in solving the advection-diffusion equation. The calculations with all three optimization techniques for linear and nonlinear advection-diffusion equations have been run several times to obtain the optimum accuracy of the results. The computed results are seen to be very promising and in good agreement with the effective numerical methods and the physics-informed neural network (PINN) method in the literature. It is also concluded that the PSO-based algorithm outperforms other methods in terms of accuracy.

Innovative design and process simulation of aviation bracket based on topology optimization and additive manufacturing

Abstract In this paper, topology optimization(TO) and laser powder bed fusion (LPBF) technology were combined to explore the key technology of parts integration design and manufacturing oriented to performance, structure and manufacturing process. With the help of topology optimization(TO), the lightweight design of the aviation bracket achieved the weight loss goal of 87.69% in this work. The thermophysical properties of TC4 were calculated by thermodynamic calculation, and the temperatue interval of Laser Powder Bed Fusion for TC4 is 1800 ℃- 3000 ℃ was determined . Additionally, the LPBF macro-scale simulation was carried out. The results showed that the maximum temperature in the printing process is lower than 2500℃, and the maximum displacement is 4.87 mm, which only appears in the non-design space. Finally, the safety factor and maximum displacement of aviation bracket are 1.3 and 0.8 mm, and the maximum Mises stress is 654 MPa, all of which meet the design requirements.

Adsorption Thermodynamics for Process Simulation.

Adsorption has rapidly evolved in recent decades and is an established separation technology extensively practiced in gas separation industries and others. However, rigorous thermodynamic modeling of multicomponent adsorption equilibrium remains elusive, and industrial practitioners rely heavily on expensive and time-consuming trial-and-error pilot studies to develop adsorption units. This article highlights the need for rigorous adsorption thermodynamic models and the limitations and deficiencies of existing models such as the extended Langmuir isotherm, dual-process Langmuir isotherm, and adsorbed solution theory. It further presents a series of recent advances in the generalization of the classical Langmuir isotherm of single-component adsorption by deriving an activity coefficient model to account for the adsorbed phase adsorbate-adsorbent interactions, substituting adsorbed phase adsorbate and vacant site concentrations with activities, and extending to multicomponent competitive adsorption equilibrium, both monolayer and multilayer. Requiring a minimum set of physically meaningful model parameters, the generalized Langmuir isotherm for monolayer adsorption and the generalized Brunauer-Emmett-Teller isotherm for multilayer adsorption address various thermodynamic modeling challenges including adsorbent surface heterogeneity, isosteric enthalpies of adsorption, BET surface areas, adsorbed phase nonideality, adsorption azeotrope formation, and multilayer adsorption. Also discussed is the importance of quality adsorption data that cover sufficient temperature, pressure, and composition ranges for reliable determination of the model parameters to support adsorption process simulation, design, and optimization.

Nanoplastics Distribution during Ice Formation: Insights into Natural Surface Water Freezing Conditions.

The migration characteristics of nanoplastics (NPs) in the natural freezing process are complex and have attracted increasing attention in simulating natural freezing in recent years. However, simulated freezing conditions often fall short of replicating natural freezing processes, and studies on the vertical distribution of NPs remain inadequate. This study established a more realistic simulation of the natural freezing process in surface water by controlling both the air temperature (T1) and the water temperature (T2). Additionally, we introduced a new parameter, the local distribution coefficient (Kiw1), to compare with the effective distribution coefficient (Kiw2). The values of Kiw1 and Kiw2 for PS-500 nm were 0.18 and 0.21, respectively, at T1 = -20 °C and T2 = 1 °C. The results revealed the NPs concentration differed in ice, near-ice liquid, and far-ice liquid. Both properties of NPs and environmental factors could regulate the vertical distribution of NPs. The findings underscored the importance of freezing temperature regulated by T1 and T2, elucidating the roles of various influencing factors on the vertical distribution characteristics of NPs and unraveling the mechanisms of NPs distribution in the ice-water system. This study can provide valuable insights for understanding the migration of NPs in surface water in cold regions.

Simulation and Deformation Analysis of Construction Process of Large Eccentric Frame‐Core Tube Structure

ABSTRACTThe large eccentric frame‐core tube structure may generate nonnegligible horizontal deformation in the eccentric direction during the construction process, which should be given due attention. The construction process of a 388‐m‐high super high‐rise structure was simulated with the effects of concrete properties and construction processes. The reinforcement effect of steel bar in reinforced concrete shear wall and the hoop effect of steel tube in concrete filled steel tube (CFST) columns were considered by using the two‐cell simulation method, respectively. The accuracy of the finite element model was effectively verified based on the measured data under different construction process. The results show that the construction process has a large influence on both the vertical and horizontal deformation. The measures of construction leveling effectively reduce the vertical and horizontal deformation of super high‐rise structures, which is important for controlling the structural deformation. The proportion of creep‐shrinkage deformation in the total deformation is large. In this case, the proportion of creep‐shrinkage deformation in the shear wall is 45% to 50%, whereas it is about 30% in the frame columns. The eccentricity caused by floor slab gravity will be mitigated if the construction of the frame slab lags behind the construction of the steel frame, which is beneficial to the control of the horizontal structural deformation. After construction completion, the deformation caused by nonload factors is limited compared to the deformation caused by live loads.

Multi Combustor Turbine Engine Acceleration Process Control Law Design

Abstract Focusing on the collaborative fuel supply and acceleration problem of multi combustor turbine engine, a variable geometry composite regulation acceleration control law design method based on the combination of compressor intermediate stage air bleed(CIB) and low-pressure turbine guide vanes(LTGV) is proposed. The multi combustor collaborative fuel supply law and variable geometry regulation law during acceleration process are obtained by Particle Swarm Optimization-Sequence Quadratic Program (PSO-SQP) optimization, and the composite regulation acceleration control strategy of dual/single combustion chamber idle acceleration process is explored. The multi combustor acceleration process simulation results indicate that the variable geometry composite regulation method can effectively alleviate the stability margin limitation of the compressor during the initial acceleration stage. Compared with conventional acceleration methods, the maximum relative reduction in acceleration time of our method reaches 26%; Enhancing the combustion exergy efficiency during the acceleration process can significantly improve the engine's acceleration performance.; For the acceleration process from single combustion chamber idle operation to normal operation of a dual combustion chamber, simultaneous fuel supply from both combustion chambers during acceleration has a shorter acceleration time. The research results of this paper can provide important references for the design of transition state control systems for multi combustor turbine engines.

Prototype to mitigate risks for the closure of a company with the support of Information Technologies

The problems of business closures (SMEs) are constant in the world and in Ecuador due to the lack of application of appropriate management models that allow the conversion of strategies. The deductive method was used for exploratory research of the information related to the research topic. The objective is to design a prototype to mitigate risks and prevent threats to avoid the closure of this type of companies. The result was a conceptual model to minimize risks, a risk control prototype, a prototype flow chart, a simulation of processes with five scenarios, determination of the formula to detect the probability of closure as an SME and risk analysis and evaluation. It was concluded that the proposed prototype to detect threats and vulnerabilities provides an optimal security model for small and medium-sized companies; as a result of the simulations, a security percentage of 77.92% was established, which means that the greater the number of risks and threats, the greater the probability that a small and medium-sized company will close.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B<sub>4</sub>C Composites: A Comparative Study with Nature-Inspired Algorithms

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B4C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.