Tạp chí Khoa học và Công nghệ Trường Đại học Công nghiệp Hà Nội (Bản điện tử)
Tạp chí của Trường Đại học Công nghiệp Hà Nội
MODELING THE IMPACT OF MARKER PARAMETERS ON FABRIC CONSUMPTION IN GARMENT MANUFACTURING
The fabric cost of a garment is closely associated with its fabric consumption. Fabric consumption varies depending on the number of garments and the number of sizes included in a marker, as well as the marker efficiency. This study investigates the effects of the quantity of garments (SP), the quantity of sizes in a marker (SC), and marker efficiency (HS) on fabric consumption, and proposes a method for estimating the fabric consumption of men’s shirts. MarkerMaking V6R2 software was used to create patterns, grade them into five sizes, and generate markers for men’s shirts on a fabric width of 1.5 m, from which the fabric consumption per garment was determined. The R software was employed to analyze the relationships between fabric consumption and SP, SC, and HS. As a result, a multivariate model describing the relationship between fabric consumption and these three factors was developed. Within the scope of this study, marker efficiency has an impact on the variation in fabric consumption (73%), followed by the garments quantity (21.5%) and the sizes quantity in a marker (3%). The proposed model enables the estimation of fabric consumption based on variations in the quantity of garments, sizes, and marker efficiency, thereby contributing to more efficient fabric utilization in industrial garment manufacturing.
VALORIZATION OF PHOSPHORUS SLAG FROM TANG LOONG FOR SUSTAINABLE CONSTRUCTION AND INDUSTRIAL APPLICATIONS
This study investigates the recycling of phosphorus slag from the Tang Loong Industrial Park (Lao Cai, Vietnam) as a secondary mineral resource forbuilding material applications. Large volumes of phosphorus slag generatedfrom yellow phosphorus production pose significant environmental challenges,while the slag itself is rich in calcium-silicate phases (CaO 50 - 52%, SiO 2 37 40%) with a specific gravity of approximately 2.75g/cm . After purification via acombined washing and gravity separation process, the resulting slag powderexhibited improved quality and was evaluated in multiple product systems. Incoating formulations, the slag effectively replaced mineral fillers at a slag/resinratio of 1.25/1, achieving high opacity and strong adhesion. In HDPE-basedcomposites, the addition of 5 - 20wt% slag improved tensile strength andelongation at break. In wall putty applications, the slag successfully replacedconventional mineral aggregates while achieving adhesion values close to ormeeting the requirement of TCVN 7239:2014. These results demonstrate thatproperly treated phosphorus slag can serve as an effective alternative to naturalmineral powders across different material systems, offering a practical route forlarge-scale valorization of industrial waste while reducing environmentalimpact and enhancing sustainable resource utilization. 3
Pham Thi Minh, Nguyen Van Chien, Dam Xuan Thang
OPTIMIZATION OF SCHISANDRA HENRYI EXTRACTION USING RESPONSE SURFACE METHODOLOGY
Schisandra henryi is a woody climbing species of the genus Schisandra, naturally distributed mainly in southwestern China, with recent records of wild populations in the Ngoc Linh mountainous region of Vietnam. The fruits of this species possess medicinal value and are characterized by a chemical profile rich in dibenzocyclooctadiene lignans and phenolic compounds. In this study, an ultrasound-assisted extraction process using 96% ethanol as the solvent was optimized to enhance extraction yield and the recovery efficiency of schisandrin A from S. henryi fruits. The effects of solvent-to-material ratio, extraction time, and temperature were simultaneously evaluated using response surface methodology (RSM). The developed RSM models exhibited a high degree of goodness of fit and strong predictive capability. The results indicated that an extraction temperature of 50°C, an ultrasonic extraction time of 64.37min, and a solvent-to-material ratio of 15.22mL/g were the optimal conditions, yielding a schisandrin A content of 0.18% and an extraction yield of 23.28%. Under these optimized conditions, the experimental values were in good agreement with the predicted values, confirming the reliability of the proposed models.
Hoang Thu Thuy , Ngo Thi Phuong , Ta Thi Thao Cam Thi Inh , Do Thi Thanh Huyen , Le Minh Ha
EFFECTS OF DIFFERENT NUTRIENT MEDIA ON THE GROWTH AND DEVELOPMENT OF LEVEL III SPAWN OF THE GOLDEN OYSTER MUSHROOM (PLEUROTUS CITRINOPILEATUS)
The golden oyster mushroom (Pleurotus citrinopileatus) is an edible mushroom with high nutritional value, rich in protein, vitamins, and minerals, and has potential applications in medicinal and functional foods. Research and refinement of the production process are crucial for improving the yield and quality of commercial mushrooms. This study aimed to evaluate the effects of different nutrient media on the growth and development of grade III spawn (P. citrinopileatus). Five nutrient media formulations were developed with different carbon, nitrogen, and mineral sources. The growth rate and morphological characteristics of the mycelium were monitored for 21 days under a temperature range of 24 - 28°C. The results showed that formulation M4 (40.5% rice grain, 40.5% sawdust, 3% maize bran, 15% wheat bran, and 1% CaCO₃) resulted in faster mycelial growth, with dense, white, and uniformly distributed mycelium that firmly adhered to the substrate, reaching a mean mycelial length of 13.52cm. This research contributes to determining the optimal medium formula for the third stage of mushroom spawn production, laying the groundwork for large-scale propagation and improving the efficiency of cultivating golden oyster mushrooms.
Nguyen Duc Hai, Nguyen Chung Ha, Pham Thi Mai Huong,Ha Thi Dung, Trinh Thi Hai, Vu Thi Cuong, Vu Dinh Giap, Nguyen The Huu, Nguyen Manh Ha, Do Thi Hanh
STUDY ON THE EFFECT OF POLYESTER/COTTON FIBER RATIO IN TEXTILE FIBERS ON SOME OF THE THERMAL AND MOIST COMFORT PROPERTIES OF INTERLOCK FABRICS
This study evaluates the effect of the polyester/cotton fiber ratio in textile fibers on some of the thermal and moist comfort properties of Interlock fabrics through the use of 04 samples of fabrics with composition ratios: 100%cotton, 65% polyester/35% cotton, 83% polyester/17% cotton, 100%polyester. The fabric samples were prepared and conditioned according to TCVN 1748:2007 (ISO 139:2005), then experimented: Determining the densityof the fabric according to TCVN 5794:1994; Determination of yarn ring length according to TCVN 5799:1994 standard; Determination of fabric thicknessaccording to TCVN 5071:2007 (ISO 5084:1996). Fabric samples afterdetermining structural parameters are determined to absorb water according to TCVN 5091:90 standard; determination of capillary capacity (fabriclongitudinal absorbency) according to AATCC TM197 standard; determination of air permeability standard TCVN 5092:2009 (ASTM D 737:2004). The results showed: The polyester/cotton fiber ratio has an effect on water absorption,capillary conductivity, and air permeability. Fabrics with a higher percentage of polyester fibers will have poor absorbency and capillary conductivity, but will have higher air permeability.
Tho Luu Thi, Van Hai Nguyen, Thanh Nam Pham
INVESTIGATION OF MECHANICAL AND FUNCTIONAL PROPERTIES OF COATED FABRICS FOR MATERIAL SELECTION IN FALL-PROTECTION AIRBAGS
Falls are a common type of accident among the elderly and represent a major cause of serious injuries. Therefore, wearable smart airbag systems have been developed to mitigate impact forces and protect the human body during fall events. The protective performance of these systems largely depends on the properties of the materials used for airbag fabrication, inwhich the selected fabrics must meet specific technical requirements. Thipaper presents the results of a study on the mechanical and functionalproperties of coated fabrics used in airbag manufacturing through a series of tests, including tensile strength, elongation at break, bursting strength, puncture resistance, water resistance, and air permeability. The results provide a scientific basis for the selection and evaluation of fabric materials in the design and development of wearable smart airbags, contributing to improved protection efficiency for users during fall events.
A MACHINE LEARNING BASED MULTI-ATTRIBUTE RECOMMENDATION SYSTEM FOR WOMEN’S APPAREL USING RANDOM FOREST
Personalized clothing recommendation is a challenging task due to the complex interactions among body morphology, individual preferences, and usage context. This study proposes a multi-output women’s clothing recommendation framework based on a Random Forest (RF) model, utilizing20 input features including anthropometric characteristics, demographic information, personal desires, consumption behavior, and contextual factors.Five clothing attributes are simultaneously predicted: silhouette, color group, fabric type, fit type, and garment length. Experimental results indicate moderate to good performance, with Accuracy ranging from 0.6339 to 0.8125and Balanced Accuracy from 0.6724 to 0.8607. Among these attributes, silhouette achieves the highest predictive accuracy (0.8125, Kappa = 0.7136). Apparel length and fabric type show satisfactory results, whereas color group and fit type are more difficult to predict due to their subjective nature. Overall, the findings confirm the potential of Random Forest for multi-attribute clothing recommendation systems that meet personalization requirements.
MODELLING FOR INFRARED VACUMM DRYING PROCESS
This paper presents the modeling results of the drying process of sliced potatoes in different pressure drying environments, with or without infrared heating. The theoretical model is built based on the volumetric averaging method to establish phase equilibrium within a volume element. Infrared energy is modeled as radiant heat exchange with heat flux density considered as a type 2 boundary condition. The model was solved using MATLAB, and the results were evaluated as highly accurate. The model showed the moisture and temperature distribution in the material throughout the drying process. Calculation results are shown for different pressure and radiation intensity cases to evaluate the effects of drying conditions on drying time. When pressure decreases, the drying rate increases while the material temperature decreases; when radiation intensity increases, the evaporation rate increases but also the material temperature increases
EVALUATION OF THEORETICAL MODELS FOR PREDICTING THERMAL CONDUCTIVITY OF BO CHINH GINSENG ROOT BASED ON EXPERIMENTAL DATA
Thermal conductivity is an important thermophysical property in the simulation of freezing and freeze-drying processes of biological materials. This study evaluates the predictive performance of thermal conductivity models for Bo Chinh ginseng root (Abelmoschus sagittifolius) over the temperature range from -30 to 30°C by comparing several commonly used theoretical models with previously published experimental data. The models examined include the parallel and series bounds, Maxwell-Eucken, effective medium theory (EMT), Levy (1981), and the co-continuous model proposed by Wang (2008). Statistical analysis indicates that the Wang model provides the best agreement with experimental measurements, yielding MAE and RMSE values of 0.028 and 0.031W/m·K, respectively, whereas models based on single continuous-phase assumptions or idealized geometric configurations exhibit larger deviations, particularly in the frozen region. The results suggest that model accuracy is strongly influenced by the underlying structural assumptions, with the co-continuous approach offering improved representation of heat transfer behavior in heterogeneous materials. The findings provide a basis for selecting appropriate models in thermal calculations and process simulations involving Bo Chinh ginseng root.
Nguyen Hay, Nguyen The Bao, Ngo Thi Minh Hieu, Le Quang Huy
ROBUST CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS IN CARS
The active suspension system plays an important role in enhancing ride comfort and road holding in automobiles. This paper focuses on designing a robust controller for an active suspension system to improve both these criteria. First, a half-car model in the longitudinal direction is established with active suspension implemented on the front and rear axles. The dynamic equations are transformed into the state-space representation, and a robust controller is designed with the objective of improving ride comfort while considering the road-holding capability of the wheels. Simulation results in both the frequency and time domains show that the active suspension system with the robust controller achieves an improvement of approximately 53.38% in ride comfort and about 77% in road holding compared to a conventional passive suspension system.
STATE-OF-CHARGE ESTIMATION OF A LITHIUM-ION TRACTION BATTERY PACK USING THE EXTENDED KALMAN FILTER IN A BATTERY MANAGEMENT SYSTEM
This paper investigates the State of Charge (SOC) estimation for Lithium-ion battery packs using the Extended Kalman Filter (EKF) integrated within a Battery Management System (BMS). To replicate real-world operating conditions, an inhomogeneous battery pack model with internal resistance variations ranging from -3% to +5% and a liquid cooling system was developed. Simulation results over a 600-second dynamic discharge cycle demonstrate that the EKF algorithm achieves highly accurate state tracking, reducing min SOC and max SOC values from 90% to 87% with an absolute error maintained below 0.1%. The study further indicates that integrating active thermal management, with a constant pump power of 125W, maintains the battery temperature at 300.2K. This provides optimal conditions for rapid algorithm convergence and high robustness against physical parameter uncertainties. These findings confirm the feasibility of implementing EKF in modern BMS applications to enhance safety and extend battery cycle life.
Vu Hai Quan , Karpukhin Kirill Evgenievich, Nguyen Trong Duc, Bui Van Hai, Karpukhin Filipp Kirillovich
OPTIMIZING SEAT OSCILLATION THROUGH THE DYNAMIC MODEL OF A PURPLE ONION HARVESTING SYSTEM
This study constructs a 5-degree-of-freedom oscillation model for an onion harvesting machine system consisting of a tractor, harvester, and seat. The seat is modeled as a mass-spring-dampening system mounted on theharvester. The forces acting on the wheels are simulated as a complex combination of sine wave functions and random noise. A fourth-order RungeKutta method is used to solve the differential equations to analyze theoscillation response over time at the seat position. The ISO 2631-1:1997 standard was referenced to assess the impact of vibrations on workers through the RMS value of vibration acceleration. Based on this, the study proposes an optimization solution for parameters (mass, stiffness, damping coefficient, velocity) using a genetic algorithm to optimize vibration reduction at the chair. Through optimization, the result obtained was RMS = 0.2192m/s² compared to the pre-optimization value of RMS = 2.6349m/s².The results of the study can help increase the durability of the machine and ensure the health of the machine operators.
Nguyen Thuan Hai Dang , Le Trung Hau, Le Duy Long , Tran Phuc Hoa
A SIMULATION STUDY ON THE EFFECT OF BOOST PRESSURE ON COMBUSTION CHARATERISTICS OF BIOGAS ENGINE
This study investigates the influence of boost pressure ratio on the combustion characteristics and overall performance of a biogas-fueled internal combustion engine. The simulation was carried out using the DieselRK software to evaluate variations in engine power, thermal efficiency, incylinder pressure, and pollutant emissions under different boost pressure ratios ranging from 1.0 to 2.0. The engine model was operated at full load with a constant excess air ratio (λ = 1.2) over a speed range of 1000 2400rpm. Simulation results show that increasing boost pressure significantly improves combustion efficiency and engine output due to enhanced air–fuel mixing and higher intake density. The indicated mean effective pressure and brake power increased by up to 90% at the highest boost ratio, while the specific energy consumption decreased by approximately 5%. However, NO x emissions were found to increase with boost pressure as a result of higher combustion temperature. The study identifies an optimal boost pressure ratio between 1.6 and 1.8 for biogas engines, achieving the best balance between performance enhancement, fuel economy, and emission control.
Nguyen Thanh Binh, Trinh Xuan Phong, Nguyen Phi Truong
MODELING AND EVALUATION OF INSTANTANEOUS FUEL CONSUMPTION USING VEHICLE SPECIFIC POWER: A COMPARATIVE STUDY OF REGRESSION MODELS
Accurate prediction of instantaneous fuel consumption is essential for improving energy efficiency and reducing emissions in urban transportation systems. This study investigates the performance of three regression models including Linear Regression (LR), Optimizable Tree (OT), and Optimizable Ensemble (OE), for estimating fuel consumption using Vehicle Specific Power (VSP) as the sole input variable. The models are trained and evaluated on two datasets representing in-distribution and out-of-distribution conditions. The results show that OT achieves the best performance under in-distribution conditions, with a coefficient of determination (R ) of approximately 0.90 and the lowest RMSE and MAE values. However, its performance deteriorates significantly when applied to out-of-distribution data, indicating limited generalization capability. In contrast, LR demonstrates more stable performance across both datasets, despite having lower accuracy under indistribution conditions. The OE model does not show significant improvement and consistently yields higher prediction errors. These findings highlight the trade-off between model accuracy and generalization ability. While nonlinear models such as decision trees are effective in capturing complex relationships within familiar data, simpler linear models may offer better robustness under varying operating conditions. The study also suggests that the use of a single input variable (VSP) limits the effectiveness of more complex models, indicating the need for incorporating additional features or temporal dynamics to improve prediction performance. 2
EXTRAPOLATIVE PREDICTION OF MAIN INJECTION FLOW RATE IN A COMMON-RAIL DIESEL INJECTOR USING SUPPORT VECTOR REGRESSION
This study presents a method for predicting the main injection flow rate under high-speed operating conditions (CH4) in a common-rail fuel injection system using a Support Vector Regression (SVR) model. Experimental data were collected on a dedicated injector test bench equipped with a temperature control unit to maintain the fuel temperature at 25°C, ensuring stable and repeatable test conditions. In the proposed approach, the main injection flow rates measured at low-load (CH1) and medium-load (CH2) operating modes were employed as input variables to construct the predictive model. Meanwhile, experimental data obtained directly under the high-speed condition (CH4) were used exclusively as an independent test dataset to evaluate the model’s generalization and extrapolation capability under unseen operating conditions. The results demonstrate that the SVR model achieves high and stable predictive performance. For the training dataset, the model yields an R² of 0.9928, an RMSE of 0.0578mm³/cycle, and an MAE of 0.0299mm³/cycle. For the test dataset, the corresponding values are R² = 0.9850, RMSE = 0.0764mm³/cycle, and MAE = 0.0587mm³/cycle. These results confirm that the nonlinear relationship between main injection quantities at different operating modes can be effectively exploited even when a reduced set of input variables is used.
Nguyen Xuan Khoa, Duong Ngoc Thiep, Nguyen Tien Han
APPLICATION OF SIMCENTER AMESIM IN MODELING AND EVALUATING THE EFFECTIVENESS OF BRAKE BLENDING STRATEGIES ON ELECTRIC VEHICLES
Regenerative Braking technology is the key to improving energy efficiency on electric vehicles. However, managing the interaction between the regenerative braking system and the traditional mechanical braking system requires complex Brake Blending strategies to ensure safety and dynamic stability. This paper applies the 1D system simulation method on Simcenter Amesim software to investigate the operating characteristics of a commercial electric vehicle model (based on Tesla Model 3 specifications). Three brake blending strategies, namely Series (energy optimization), Parallel (simplified control), and Hybrid (balanced), were modeled and evaluated for performance through simulation scenarios. The research results indicate a substantial difference in energy recovery efficiency among the strategies (ranging from ~23.7% to ~69.7%), while simultaneously highlighting the trade-off between control system complexity and practical operational performance. This study provides valuable reference parameters for selecting appropriate brake energy management solutions tailored to specific electric vehicle segments.
A STUDY ON THE IMPACT OF IGNITION TIMING ON THE PERFORMANCE AND COMBUSTION CHARACTERISTICSOF HYDROGEN-FUELED ENGINES AT VARIOUS ENGINE SPEEDS
This study examines the impact of ignition timing on the performance, in- cylinder thermodynamic characteristics, and NO x emissions of a hydrogenfueled spark-ignition engine, utilizing AVL-BOOST simulation software. A single-cylinder engine model was developed with fixed geometric and operating parameters, while the ignition timing was varied to account for the fast-burning characteristics of hydrogen. Simulations were conducted at engine speeds of 2,500rpm; 3,000rpm, and 3,500rpm under stoichiometric conditions. The results indicate that advancing the ignition timing increases brake power up to an optimal range of approximately (8÷10) BTDC, at which the minimum brake specific fuel consumption is also observed, reflecting improved combustion phasing and fuel conversion efficiency. Further ignition advance leads to a deterioration in performance due to increased negative work and heat losses. The maximum in-cylinder pressure and temperature increase with advancing ignition timing and engine speed, consistent with earlier combustion phasing and enhanced heat release. NO x 0 emissions exhibit a strong dependence on ignition timing and engine speed, reaching peak values near the operating conditions corresponding to the highest in-cylinder pressure and temperature, confirming the dominant role of thermal NO x formation. Overall, the predicted trends are physically consistent with the combustion characteristics of hydrogen-fueled spark-ignition engines and provide useful insights for ignition timing optimization.
Pham Van Trong, Nguyen Duy Vinh, Dinh Xuan Thanh
A STUDY ON THE DEVELOPMENT OF THE CHARACTERISTIC CURVES OF AN ELECTRORHEOLOGICAL DAMPER FOR PASSENGER CARS
Semi-active suspension systems are increasingly applied in the automotive industry due to their balance between performance and manufacturing cost. Among them, electrorheological (ER) dampers stand out thanks to their fast response time, low energy consumption, and flexible adjustability of damping force through controlled voltage. This paper develops a mathematical model of an ER damper based on the Bingham plastic constitutive law, from which the force-displacement and force– velocity characteristics are established. Simulation results indicate that the damping force exhibits nonlinear dependence on both piston displacement and velocity, and increases significantly with higher control voltages.Furthermore, the study highlights the influence of PWM levels on thevariation of damping force, providing a scientific basis for designing adaptivecontrollers and optimizing the performance of semi-active suspensionsystems for passenger cars.
Kieu Duc Thinh, Doan Duy Duc, Bui Trung Duc
RECONSTRUCTION OF HIGH-CURVATURE FREE-FORM SURFACES FROM REVERSE ENGINEERING DATA USING CURVATURE FIELD ANALYSIS AND VARIATIONAL OPTIMIZATION
Reverse engineering (RE) has become a key technological solution in modern product design and development, particularly for industries involving complex free-form geometries such as automotive, aerospace, and mold manufacturing. However, reconstructing free-form surfaces with high curvature, complex boundaries, and stringent requirements on geometric smoothness and optical surface quality remains a significant challenge in practical applications. This paper proposes a novel method for high-curvature free-form surface reconstruction from reverse engineering data by integrating curvature field analysis with variational optimization. The proposed approach addresses three major challenges: (1) optimal surface patch layout determination through Gaussian curvature field analysis; (2) reconstruction of base curve networks with an optimal balance between data fidelity and geometric smoothness using a variational formulation; and (3) generation of the final surface using tensorproduct Bézier surface modeling with enforced G² continuity constraints. The proposed method is validated using 3D scanned data of a complex automotive surface, specifically a rear bumper of a Porsche Macan. Experimental results demonstrate that the reconstructed surface exhibits superior geometric quality and high technical accuracy, with a mean deviation of 0.087mm. Moreover, full G² continuity is consistently maintained across patch boundaries, resulting in smooth curvature transitions and stable optical reflection behavior. These results confirm the effectiveness of the proposed method for reconstructing complex free-form surfaces that satisfy both stringent engineering accuracy requirements and high aesthetic quality standards. Future research will focus on extending this framework to surfaces with more complex topological structures and exploring the integration of deep learning-based point cloud segmentation to further automate the surface reconstruction pipeline.
Thien Nguyen Van , Dung Le Van, Phan Nguyen Huu, Minh Dao Binh , Hung Do Duy
STRUCTURE AND PROPERTIES OF AlCrN AND TiAlN THIN FILMS COATED ON GAS NITRIDED SKD61 STEEL BY CATHODIC ARC PLASMA DEPOSITION
In this study, AlCrN and TiAlN hard thin films were deposited on nitrided SKD61 steel by cathodic arc plasma evaporation method. Structure and properties of thin films were investigated by X-ray diffraction, optical microscope, stereo microscope, microhardness test, adhesion VDI 3198 standard, and corrossion resistance test. The results revealed that the a duplex structure of AlCrN (~ 2.2µm) or TiAlN (~ 2.5µm) and nitrided layers (~ 80µm) were formed on the SKD61 steel. The AlCrN and TiAlN thin films were composed of (AlN + CrN) and (AlN + TiN 0.3 ) phases, respectively. The adhesion of AlCrN and TiAlN films on the nitrided steel was very good. The surface hardness and corrossion resistance of stainless steel were improved by duplex treatment process of cathodic arc plasma deposition and nitriding.
Nguyen Van Thanh , Van Thanh Doan , Huy Van Vu, Pham Duc Cuong, Duong Van Thiet , Trinh Van Trung
SIMULATION-BASED OPTIMISATION OF URBAN TRAFFIC FLOW USING MULTI-CRITERIA AND FORECASTING MODELS
Urban traffic congestion adversely affects operational efficiency, safety and environmental sustainability in developing cities. This study presents an integrated framework combining microscopic traffic simulation (SUMO), multi-criteria decision-making (TOPSIS and PSI) and short-term traffic demand forecasting (ARIMA) to optimise traffic flow along the urban corridor between Hanoi University of Industry (HaUI) and Hanoi National University. Empirical traffic data were collected through on-site surveys and camerabased monitoring, calibrated against a SUMO simulation model and subsequently used to rank two traffic management alternatives. The results demonstrate that signal phase reconfiguration with dedicated turn lanes (Option 2) reduces average vehicle waiting time by approximately 2.71% and increases average travel speed by 3.11% relative to the current state. PSIbased multi-criteria ranking confirms Option 2 as the optimal strategy. ARIMA (2,1,1) forecasting further reveals an estimated reserve operational capacity of approximately 407% between current demand and the infrastructure saturation threshold, indicating that poor signal control rather than physical infrastructure capacity is the primary bottleneck.
OPTIMIZATION AND PREDICTIVE MODELING OF SURFACE ROUGHNESS IN CNC MILLING OF 40Cr ALLOY STEEL USING TAGUCHI-ANN APPROACH
This study presents an experimental and predictive modeling investigation on surface roughness in CNC milling of 40Cr alloy steel. A Taguchi L9 design, ANOVA, cutting force estimation, tool wear consideration, and ANN modeling were employed. Surface morphology interpretation using SEM illustrations and force–roughness relationships were integrated. The results show that feed per tooth is the dominant factor affecting surface roughness. The proposed hybrid statistical-AI framework improves machining parameter optimization and prediction accuracy.
Phang Xuan Son, Vu Thi Hue, Pham Ngoc Thanh
MATHEMATICAL REGRESSION MODELING OF THE LASER DIRECT METAL DEPOSITION PROCESS FOR INCONEL 718
This paper presents a comprehensive mathematical regression analysis of the Direct Laser Metal Deposition (DLMD) process for Inconel 718, with the aim of quantifying the relationships between key process parameters and coating geometrical characteristics. Laser power, scanning speed, and powder feed rate were selected as the primary process variables. A series of designed experiments was conducted, and polynomial regression models were established to describe the effects of both individual parameters and their interactions on coating geometrical characteristics, including width, height, and dilution. The ANOVA results revealed high coefficients of determination for clad height ( 2 1 R = 0.9858), clad dilution ( 2 2 R = 9669), and clad weight ( R = 0.907), confirming the excellent fitting accuracy and predictive capability of the proposed models. Among the investigated process parameters, laser power was identified as the dominant factor influencing clad geometry, whereas scanning speed and powder feed rate exhibited comparatively minor effects. In summary, the developed regression equations demonstrated high predictive accuracy, enabling reliable estimation of optimal parameter ranges for producing dense and dimensionally stable Inconel 718 deposits. 2 3
Nguyen Van Toan, Mai Xuan Hai, Doan Tat Khoa
KINODYNAMIC RRT PLANNING WITH BACKSTEPPING TRACKING FOR CONSTRAINED MOBILE ROBOT MOTION IN CLUTTERED ENVIRONMENTS
Autonomous mobile robots often require motion plans that are not only collision-free but also consistent with velocity and acceleration limits that matter during execution. This paper studies a coupled planning-and-tracking pipeline for a planar second-order robot model. A kinodynamic RRT* planner generates reference trajectories by forward propagation under bounded acceleration and speed, and a backstepping controller tracks the resulting reference using the planner-provided acceleration signal. The approach is evaluated in simulation on three obstacle-field maps, including a dense layout and a narrow-passage scenario, using 30 randomized runs per map. Compared with a kinodynamic RRT baseline without rewiring, kinodynamic RRT* reduces trajectory cost and path length while increasing computation time due to neighborhood search and rewiring. On the same planned references, the backstepping tracker produces smaller average tracking errors than a tuned PID controller, with mean position and velocity errors of 0.042m and 0.104m/s, respectively. The results illustrate how aligning the planning model and the tracking design can improve repeatability of closed-loop motion in cluttered environments, while keeping the overall computation practical for offline planning and low-rate replanning.
Bui Thanh Lam, Nguyen Minh Quang, Nguyen Tien Thanh, Luu Vu Hai
IMPROVING SURFACE ROUGHNESS PREDICTION RELIABILITY USING TOOL WEAR-BASED MODELING
Surface roughness prediction in turning is typically formulated as a function of cutting parameters, while progressive tool wear is often neglected. In machining of difficult-to-cut alloys, however, flank wear modifies effective cutting geometry and may reduce predictive reliability. This study evaluates the effect of incorporating flank wear ( ) into surface roughness ( ) prediction using a quadratic Response Surface Methodology (RSM) model. A Central Composite Design with 20 dry turning experiments was conducted. Two formulations were compared: a parameter-based model ) and a wear-integrated model = ( = ( , , , , ). Model performance was assessed using R² and Leave-One-Out Cross-Validation (LOOCV). The baseline model achieved , = 0.69, whereas incorporating V b increased R to 0.95 and reduced LOOCV error by approximately 40%. The results demonstrate that tool wear significantly enhances prediction reliability and should be considered a state variable in surface roughness modeling. 2
Van-Canh Nguyen, Dung Hoang Tien, Quang-Tu Ngo, Nguyen Chi Bao, Nguyen Thanh Cong , Nguyen Dang Kham
EXPERIMENTAL INVESTIGATION OF COMPLIANT POLISHING OF FREE-FORM SUS 304 SURFACES USING THE TAGUCHI METHOD
This study aims to perform experimental analysis for the compliant polishing process of free-form surfaces made from SUS 304, an important material widely used in medical, construction, and various industrial elds due to its superior properties. The model integrates macro-scale analysis with micro-scale mechanisms to accurately describe the material removal mechanism, based on Hertzian contact theory. The breakthrough of this research lies in the application of an equivalent material substitution method to precisely determine the mechanical parameters of the polishing tool, a key factor previously overlooked in traditional models. The practical application in polishing SUS 304 material demonstrated superior eﬀectiveness by achieving a surface roughness of 17nm. Additionally, this research not only provides a solution for polishing free-form SUS 304 surfaces but also establishes an important theoretical foundation for the development of advanced machining technologies in the future.
Tran Viet Kieu, Nguyen Van Dai , Chu Ba Lam, Hoang Minh Duc, Vu Van Chung, Than Van Hung , Pham Thi Thieu Thoa
DYNAMICS-BASED STRUCTURAL WEIGHT OPTIMIZATION OF A LARGE-SCALE 3D CONCRETE PRINTER FRAME WITH TIME-DEPENDENT PRINTHEAD DISPLACEMENT CONSTRAINTS
This study proposes a dynamics-based structural weight optimization approach for a large-scale gantry-type 3D concrete printer frame, in which time-dependent printhead displacement is explicitly considered as a performance constraint. Unlike conventional methods based on linear static analysis, the proposed framework integrates flexible multibody dynamicsimulation into the optimization loop, enabling direct evaluation of inertialeffects and structural vibrations on printhead accuracy. An automated computational framework is developed to couple finite element-based modal analysis, flexible multibody dynamics, and global optimization within a unified iterative process. Elastic characteristics extracted from a parametric finite element model are incorporated into a flexible dynamic model to evaluate printhead displacement under realistic operating conditions. A Genetic Algorithm (GA) is employed to iteratively update design variables,minimizing structural mass while satisfying displacement constraints. The results demonstrate a structural weight reduction of 63.09% while maintaining maximum printhead displacement below 1mm, highlighting the effectiveness of the proposed approach for dynamic-constrained design optimization.
Duc Khoi Trieu, Van Binh Phung, Reynolds Addo-Akoto , Hong Nhu To
APPLICATION OF THERMAL SPRAY TECHNOLOGY IN THE REFURBISHMENT OF AUTOMOTIVE CAMSHAFT COMPONENTS
This paper presents the research results on the application of High- Velocity Oxy-Fuel (HVOF) thermal spray technology for the refurbishment of automotive camshaft components manufactured from 40Cr steel using WC-12Co cermet coatings, as an environmentally friendly alternative to conventional hard chromium electroplating. The study employs the Taguchi experimental design method based on an L9 orthogonal array to evaluate the effects of key process parameters, including powder feed rate (m), rotational speed of the workpiece (n) and spray gun traverse distance (S), on the adhesion strength of the coating. The results indicate that the maximum adhesion strength of 72.8MPa was achieved under the optimal process conditions (m = 32g/min, n = 100rpm, S = 8mm/rev). Microstructural analysis using SEM confirms that the coating exhibits a dense lamellar structure with low porosity (< 1%), where the dominant bonding mechanism is mechanical interlocking combined with metallurgical diffusion at the coating-substrate interface. The study demonstrates the high feasibility of HVOF spraying technology in enhancing the service life and functional performance of refurbished camshafts, meeting the stringent requirements of the automotive industry.
APPLICATION OF CAD/CAE TO OPTIMIZING EXTRUSION DIE FOR DYL8812-3 ALUMINUM ALLOY PRODUCT
This paper presents the application of CAD/CAE for optimizing extrusion dies for the DYL8812-3 aluminum alloy product. It focuses on using Siemens NX for die design and QForm-Extrusion for simulating aluminum prole extrusion. Due to the non-uniform material ow, the prole passing through the die orice may bend, twist, or deform. The QForm-Extrusion software features a specialized interface for quick die geometry import and automatic nite element model generation. It can also recognize die bearing zones, convert them into parametric form, and enable modications and optimization, helping users achieve the most uniform distribution of longitudinal velocity. Additionally, the software provides a comprehensive analysis of die stress and deection. With parallel computing, simulation time is signicantly reduced. Numerical siulation using Qform software has optimized the bearing length and aluminum runner width of the DYL8812-3 extrusion die.
Pham Van Nguyen , Nguyen Trong, Mai Tran Duc Quy , Nguyen Truong Giang , Dongyi Lin
ADAPTTRACK: AN ENHANCED BYTETRACK APPROACH USING A VELOCITY-ADAPTIVE KALMAN FILTER
Multiple Object Tracking (MOT) remains a challenging task in computer vision, particularly under occlusion and detection uncertainty. While ByteTrack improves tracking performance by leveraging both high- and lowconfidence detections, it still relies on a Kalman Filter with fixed noise parameters, limiting adaptability to diverse motion patterns. In this paper, we propose an enhanced ByteTrack framework by integrating an Adaptive Kalman Filter to dynamically adjust noise parameters during prediction. This approach improves state estimation and tracking robustness across varying conditions. Additionally, lightweight object detection models are employed to enable deployment on cost-effective hardware. Experimental results demonstrate that the proposed method achieves improved tracking performance while maintaining computational efficiency, making it suitable for real-time applications.
Hai Hoang Hong , Long Nguyen Huu, Dung Nguyen Tien ,Nhat Minh Phung , Long Hoang Duc