Control Technique Research Articles

Generating broadband cylindrical vector modes based on polarization-dependent acoustically induced fiber gratings using the dispersion turning point

Cylindrical vector beams (CVBs) with special polarization distribution have been extensively investigated due to the unique ways of interacting with matter. Although several configurations have been developed to generate CVBs, such as Q-plates and subwavelength gratings, the bandwidth of a single CVB is inherently narrow due to the phase geometry, which would limit its application for femtosecond lasers. Here, a broadband CVB mode converter based on an acoustically induced fiber grating (AIFG) and a tuning method of dispersion turning point (DTP) is demonstrated both theoretically and experimentally with the 3-dB bandwidth of 125 nm, which is more than 10 times that of conventional AIFGs. Not only can the DTP wavelength be tuned from the original 1500 nm to 1650 nm by thinning the fiber, but also the stable generation of a single broadband HE21odd/even mode can be controllably implemented by adjusting the polarization state of the incident light, owing to the larger beat length difference between HE21 and other CV modes. Additionally, the femtosecond CVBs and orbital angular momentum (OAM) modes are successfully generated and amplified by combining the broadband AIFG with a figure-9 mode-locked fiber laser. Meanwhile, it is verified by simulation that the choice of broadband CV mode and the tunability of DTP wavelength can be realized by designing ring-core fibers with different structures, which can furthermore improve the flexibility of generating high purity CVBs. This study provides a highly controllable technique for the generation of broadband CVBs and OAMs paving the way for high-capacity CVBs communication.

Analisis Pengendalian Kualitas Menggunakan Statistical Process Control untuk Mengurangi Produk Cacat pada Mebel UD Sadewo Jagat di Ngawi

UD Sadewo Jagat is a company engaged in furniture production, this company provides sales of finished, semi-finished products and maintenance for various types of teak wood furniture. Finished products include tables, chairs, shelves and various other types of displays. However, in this study, we only analyzed defective products for wooden box table products because the production process of wooden box table products because the production process of wooden box tables experienced problems, namely the number of defects exceending 5% of the company’s standard provisions. The aim of this research is to analyze product quality control techniques using the Statistical Process Control method in reducing defective products, and to find out what factors cause defects in products produced by UD Sadewo Jagat. This research uses the Statistical Process Control (SPC) method, which is one of the analytical methods used to analyze the causes of defects that occur, both in terms of product quality and in terms of the quality of the production process. Quality control analysis is carried out using. The results of the check sheet analysis show an average of 9,6% product defects per month. From the results of the Pareto diagram, it can be seen that the highest level of defects is crack defects, with a total of 80 units or 44,94% of total product defects in 2023. Meanwhile, the results of the control chart that has been made can be seen that quality control is uncontrolled and not according to standards. From the results of the fishbone diagram, it is concluced that the factors causing product defects are human factors, materials, environment, and methods.

Load frequency control of uncertain power systems via a novel worst-case GADRC scheme

This article proposes a worst-case generalized active disturbance rejection control (WGADRC) technique for power systems with parametric uncertainties. The proposed scheme is based on the worst-case plant selection strategy. Firstly, a worst-case model of the plant is identified out of the various possible models of an uncertain system, and afterward, the GADRC scheme is employed to design a controller. The best feature of the proposed scheme is that the designed controller ensures superior performance under nominal as well as uncertain plant conditions than other control schemes in the existing literature. To authenticate the supremacy of the proposed methodology, the load frequency control (LFC) issues associated with numerous power systems such as single-area thermal power system, IEEE 39 bus 10 machine test power system, and higher order hydro-thermal power system involving unmodeled parametric uncertainties, unexpected nonlinearities, and physical discrepancies are considered.

On the mechanism of transonic buzz passive suppression with global stability analysis

The control surface buzz may occur when the aircraft cruise at transonic Mach numbers, which would may lead to flight accidents. Transonic buzz could be a problem in aircraft design. The triggering of transonic buzz has been investigated in the previous publications. It is essentially a single degree of freedom flutter caused by the coupling of one structural mode and one pre-instability flow mode, thus, type A/B buzz usually occurs close to the buffet onset. With this mechanism, the buzz can be suppressed by improving the flow stability and buffet onset. Three buffet passive control techniques are applied to test the suppression effect on transonic buzz. The relationship between the flow stability and buzz onset is studied to verify the mechanism of buzz. CFD simulation and global stability analysis are applied to compute the flow stability with and without control techniques. Results show that, three control techniques can improve the flow stability and postpone the buffet onset. Meanwhile the buzz can be effectively suppressed. With the aeroelastic ROM and the root loci method, the coupling process between the structural mode and the controlled flow modes are revealed, indicating the buzz suppression effects are positively correlated with its improvement of the flow stability with control. This study can help to understand the mechanism of buzz deeply and propose the new thought of buzz suppression.

Robust PI-PD Controller Design: Industrial Simulation Case Studies and a Real-Time Application

PI-PD controllers have superior performance compared to traditional PID controllers, especially for controlling unstable and integrating industrial processes with time delays. However, computing the four tuning parameters of this type of controller is not an easy task. Recently, there has been significant interest in determining the tuning rules for PI-PD controllers that utilize the stability region. Currently, most tuning rules for the PI-PD controller are presented graphically, which can be time-consuming and act as a barrier to their industrial application. There is a lack of analytical tuning guidelines in the literature to address this shortfall. However, the existing analytical tuning guidelines do not consider a rigorous design approach. This work proposes new robust analytical tuning criteria based on predefined gain and phase margin bounds, as well as the centroid of the stability region. The proposed method has been tested using various simulation studies related to a DC–DC buck converter, a DC motor, and a heat exchanger. The results indicate that the proposed tuning rules exhibit strong performance against parameter uncertainty with minimal overshoots. Furthermore, the suggested technique for simultaneous control of yaw and pitch angles has been tested in a real-time application using the twin rotor multi-input multi-output system (TRMS). Real-time results indicate that, compared to other methods under investigation, the suggested approach provides nearly minimal overshoots.

Applications of Capillary Electrophoresis for the Detection of Adulterants in Dietary Supplements

In recent years, the consumption of dietary supplements, particularly those incorporating plant-based ingredients, has increased greatly, driven by the perception of their natural origins and purported minimal health risks. However, one significant safety concern revolves around the adulteration of dietary supplements, wherein unscrupulous manufacturers may illegally incorporate pharmaceutical substances or their analogs into these products to achieve increased efficiency and bolster sales. This review assesses the role of capillary electrophoresis (CE) in ensuring the quality control of dietary supplement products over the past two decades. This study provides an overview of various applications of CE in analyzing dietary supplements, outlining the typical attributes of natural product analysis using CE. These analyses demonstrate the broad versatility of CE, exemplified by its diverse applications and detection modes. Moreover, the review highlights the growing prominence of CE as a separation technique in quality control, by comparison with more conventional methods like high-performance liquid chromatography (HPLC). Through this exploration, the review elucidates the pivotal role of CE in upholding the integrity and safety of dietary supplements, in connection with a landscape of evolving regulatory challenges and consumer demands.

A compact meta-learned neuro-fuzzy technique for noise-robust nonlinear control

Neuro-fuzzy systems show promise for adaptive control but can become complex due to the need to learn many parameters. This paper presents a resilient nonlinear controller that combines a simplified neuro-fuzzy system (Simp_NFS) and simplified neural network (Simp_NN) with only two meta-learnable parameters. This architecture enables fast and stable adaptation in uncertain nonlinear discrete-time systems. Simp_NFS utilizes interpretable hyperplane-based rules without antecedent parameters, simplifying the learning process to consequent weights. Simp_NN reduces complexity by replacing hidden-output weights with their mean. The hybrid auto-adaptive controller (HAC) combines the advantages of Simp_NFS and Simp_NN, significantly reducing the number of adaptive parameters compared to standard neuro-fuzzy methods for real-time control with limited resources. Simp_NFS provides structural adaptivity to handle uncertainties, while Simp_NN ensures reliable disturbance attenuation. The stability of HAC is proven using Lyapunov analysis. Extensive testing on challenging single-input single-output (SISO) and multi-input multi-output systems (MIMO) demonstrates that HAC improves performance by up to 82.55% compared to existing techniques. Key innovations include an ultra-compact meta-learned architecture, transparent online evolution of hyperplane clusters, and enhanced modeling capability for nonlinear uncertain systems. This interpretable neuro-fuzzy approach could enhance autonomy and safety by maintaining model transparency. The implementation of HAC is publicly available on GitHub at https://github.com/m-ferdaus/HAC.

Synchronous Tracking Control Strategy of Dual-Actuator Excited Shaking Table

The electro-hydraulic shaking table in the laboratory is specifically designed to replicate the desired earthquake ground motion for engineering structure, requiring precise synchronization of its dual exciters and accurate tracking of the target signal. The system faces challenges such as control errors and actuator asynchronization due to the nonlinear effects of the shaking table system and the structural model. To address the issues of inadequate tracking and synchronization accuracy, this study introduces a novel strategy for synchronizing the motion of the exciters and establishes a fuzzy logic controller (FLC) for the shaking table to minimize synchronization errors of dual exciters. Furthermore, an adaptive inverse controller (AIC) is developed for the exciters to enhance tracking accuracy. By combining FLC and AIC, this approach leverages the strengths of both control techniques to achieve superior control performance. The proposed control is implemented through MATLAB\Simulink, and numerical analyses are conducted under various conditions. The numerical results demonstrate the effectiveness and feasibility of the proposed control strategy.

A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM

Accurate multi-energy load forecasting is prerequisite for achieving balance between supply and demand in building energy system. The continuous development of building flexibility control techniques has led to increased complexity and variability in characteristics of multi-energy flexibility loads under application of various building flexibility control strategies. Existing load forecasting methods lack the ability to recognize the flexibility features and face challenges in considering complex coupling relationships and variable characteristics of multi-energy flexibility loads. To address this gap, we propose a multi-energy flexibility load forecasting method, denoted as (BFFR-MTL-LSTM), which incorporates building flexibility feature recognition (BFFR) and multi-task learning (MTL) model utilizing Long Short-Term Memory (LSTM) neural networks as the shared layer. A novel ToU-K-means method combining ToU with K-means algorithm is proposed firstly as BFFR technique to accurately recognize the flexibility features under various energy consumption patterns. The forecasting accuracy of proposed method is validated using actual operational data under various energy consumption patterns with average R2 of 0.973. Additionally, through comparisons with numerous existing models, the proposed method demonstrates a forecasting accuracy improvement ranging from 33 % to 47 %. This validation supports that the proposed method adeptly recognizes energy consumption patterns, resulting in more accurate forecasts of multi-energy flexibility loads.

Creep deformation characteristics and control technology in deep mine soft rock roadway

In order to control the strong ageing creep and large deformation of deep soft rock roadway effectively, with the 61–71 track on the uphill of the mining area in Suzhou, Anhui as the research background, the triaxial creep test of mudstone was conducted using the TYJ-1500 M rock mechanics testing system. The creep deformation and failure characteristics of mudstone were analyzed. Additionally, the creep deformation characteristics of deep soft rock roadways were obtained through FLAC3D numerical simulation experiment, and the control techniques for deep soft rock roadway was proposed. The results showed that the axial strain and lateral strain of the specimen were mainly instantaneous strain and creep strain under triaxial stress conditions, and the both confining pressure and the axial pressure have a significant impact on the deformation and creep failure strength of the specimen. Under the condition of high ground stress and complex geological structure, the high stress concentration of roadway roof and floor and two bottom angles is the main cause of creep failure of soft rock roadway, and the large degree of surrounding rock fragmentation and unreasonable support mode reduce the bearing capacity of surrounding rock and aggravate the creep failure of roadway. The 'anchor net cable shotcrete + floor and two corners in floor bolt-grouting + deep and shallow hole grouting + secondary reinforcement support' combined support method was proposed and industrially tested, with average deformation of the roof, floor, and two sidewalls being 111.9 and 62.5 mm, respectively, representing 13.2 and 10.3% of the deformation under the original support scheme.

Comparison of Advanced Multivariable Control Techniques for Axial-Piston Pump

This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structure of a singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances.

Efficient Removal of Tetracycline from Water by One-Step Pyrolytic Porous Biochar Derived from Antibiotic Fermentation Residue.

The disposal and treatment of antibiotic residues is a recognized challenge due to the huge production, high moisture content, high processing costs, and residual antibiotics, which caused environmental pollution. Antibiotic residues contained valuable components and could be recycled. Using a one-step controllable pyrolysis technique in a tubular furnace, biochar (OSOBs) was produced without the preliminary carbonization step, which was innovative and time- and cost-saving compared to traditional methods. The main aim of this study was to explore the adsorption and removal efficiency of tetracycline (TC) in water using porous biochar prepared from oxytetracycline fermentation residues in one step. A series of characterizations were conducted on the prepared biochar materials, and the effects of biochar dosage, initial tetracycline concentration, reaction time, and reaction temperature on the adsorption capacity were studied. The experimental results showed that at 298 K, the maximum adsorption capacity of OSOB-3-700 calculated by the Langmuir model reached 1096.871 mg/g. The adsorption kinetics fitting results indicated that the adsorption of tetracycline on biochar was more consistent with the pseudo-second-order kinetic model, which was a chemical adsorption. The adsorption isotherm fitting results showed that the Langmuir model better described the adsorption process of tetracycline on biochar, indicating that tetracycline was adsorbed in a monolayer on specific homogeneous active sites through chemical adsorption, consistent with the kinetic conclusions. The adsorption process occurred on the surface of the biochar containing rich active sites, and the chemical actions such as electron exchange promoted the adsorption process.

Monitoring process stability in robotic wire-laser directed energy deposition based on multi-modal deep learning

Robotized wire-laser directed energy deposition (DED) is a highly anticipated technology with excellent material utilization and deposition efficiency for fabricating complex-structure metal parts. Nevertheless, the immaturity of the monitoring and control techniques for the deposition process stability are the main challenges in achieving automatic and repeatable manufacturing of metal parts. This study proposes a novel approach to monitor the process stability, i.e., the intersection point between the laser beam and the wire to the top layer distance (IPTD), in robotic wire-laser DED based on a designed multi-modal IPTD estimation model and coaxial visual sensing. The novelty is that directly extracting deposition height features from coaxial molten pool images is attempted based on deep learning. In particular, the designed multi-modal IPTD estimation model, consisting of a convolutional neural network (CNN) part and a fully connected network, combines the features of molten pool images and process parameters to estimate the IPTD state. Six model architectures and three image pixel sizes are used in the IPTD classification tasks to determine the CNN part architecture and the input image pixel size of the multi-modal deep learning model based on classification results. The regression performances of established single-modal and multi-modal IPTD estimation models are studied and discussed. Compared to other model architectures, the ResNet-18 model possesses the highest convergence rate during training and the best classification accuracy of 0.9975 on the testing dataset with the image pixel size of 180 × 105. The fitting accuracy and generalization performance of the multi-modal IPTD estimation model are marked superior to the single-modal IPTD estimation model. Validation experiments reveal the effectiveness of the proposed monitoring approach of process stability. This study will lay a solid foundation for the future control of process stability in robotic wire-laser DED.

Explicit consideration of geomaterial uncertainties in the load resistance factor design of piles driven into rock-based intermediate geomaterials

The study presents a method for estimating the end bearing of piles driven into rock-based intermediate geomaterials (IGMs). Existing design methods, rooted in soil mechanics, often fall short when applied to IGMs. Moreover, the geomaterial (geological and ground properties) uncertainties are neglected and the piles are designed based on subsurface information from distant boreholes. To address this gap, the study introduces an approach that explicitly considers geomaterial uncertainties by combining empirical models and geostatistical simulation to predict end bearing. Using data from 87 piles driven into various rock-based IGMs with dynamic load testing as the construction control technique, this study introduces the Proposed Design Method (PDM) for the prediction of end bearing. PDM is compared to the Current Design Method (CDM), which is based on traditional soil-mechanics methods. Predictions from these methods are subsequently evaluated against the measured end bearings. The results demonstrate that while the PDM yields comparable predictions to the CDM, it offers a more robust consideration of geomaterial uncertainties. The PDM leads to an approximate 13.60% to 19.35% increase in uncertainties compared to CDM across various IGMs. These findings advance the understanding of pile behavior in the presence of geomaterial uncertainties and inherent variability.

Real-time regulation of detention ponds via feedback control: Balancing flood mitigation and water quality

Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves, and spillways can have controllable gates. The real-time optimal operation of its hydraulic devices can be achieved with techniques such as Model Predictive Control (MPC). A distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model is developed and integrated with an MPC algorithm to estimate the operation of valves and movable gates in real-time. The control optimization problem is adapted to switch from a flood-related algorithm focusing on mitigating floods to a heuristic objective function that aims to increase the detention time when no inflow hydrographs are predicted. The case studies show the potential results of applying the methods developed in a catchment in Sao Paulo, Brazil. The performance of MPC compared to alternatives that do not change the operation over time with either fully or partially open valves and gates are tested. Comparisons with HEC-RAS 2D indicate volume and peak flow errors of approximately 1.4% and 0.91% for the watershed module. Simulating two consecutive 10-year storms shows that the MPC strategy can achieve peak flow reductions of 79%. In contrast, the passive scenario has nearly half of the performance (41%). A 1-year continuous simulation results show that the passive scenario with 25% of the valves opened can treat 12% more runoff compared to the developed MPC approach, with an average detention time of approximately 6 h. For the MPC approach, the average detention time is nearly 14 h, indicating that both control techniques can treat similar volumes; however, the proxy water quality for the MPC approach is enhanced due to the longer detention times achieved.

Semi-supervised learning for real-time anomaly detection in pulsed transfer wire arc additive manufacturing

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such as WAAM.

RBFNN-based global fast terminal sliding mode control for fully controlled doubly fed induction generator

In this paper, a global fast terminal sliding mode control (GFTSMC) with radial basis function neural network (RBFNN) is proposed for fully controlled doubly fed induction generator (FC-DFIG) wind power system. Initially, to realize the maximum power point tracking (MPPT) of the FC-DFIG power generation system, a GFTSMC technique is introduced. Additionally, a new sliding mode reaching law is explored to enhance the respective speed and mitigate the sliding mode shaking amplitude. Subsequently, RBFNN is deployed to approximate external interferences, model uncertainties, and other unpredicted factors. The effectiveness and finiteness of the proposed control technique are affirmed through comprehensive simulation and experimental studies.

Macro Step Bunching/Debunching Engineering on 4° off 4H-SiC (0001) to Control the BPD-TED Conversion Ratio by Dynamic AGE-Ing<sup>®</sup>

This paper presents an investigation into the surface morphology control of 4H-SiC (0001) wafers cut to 4º off during thermal processing, aiming to suppress the propagation of basal plane dislocations (BPD) into the epitaxial growth layer. Developing methods for debunching rough surfaces with macro step bunching (MSB) using thermal processes removes many of the limitations of the conventional epitaxial growth process. This study presents a surface morphology control method that includes debunching of steps by thermal sublimation etching/growth using the Dynamic AGE-ing® (DA) method. By controlling the surface morphology before and after growth using this method, the dependence of the BPD-threading edge dislocation (TED) conversion ratio on surface morphology was systematically revealed. By selecting the optimal pre- and post-growth surface morphology, a 100 % BPD-TED conversion ratio was obtained for the 10 mm × 25 mm area. It was indicated that an innovative and stable surface morphology control technique using the DA sublimation process could solve numerous technological challenges in various fields.

Electromagnetic effects on solitons propagation of the (3+1)-dimensional extended Zakharov-Kuznetsov dynamical model with applications

This paper investigates wave solutions and electromagnetic wave phenomena governed by the (3+1)-dimensional extended Zakharov-Kuznetsov equation (EZKE) utilizing the Sardar sub-equation method. With a focus on electromagnetic wave generation and propagation, we rigorously analyze fundamental properties, soliton solutions, and dynamic behaviors of the EZKE. Through this analytical technique, we unravel the complex interplay among various wave types, including solitary waves and electromagnetic structures, elucidating their formation mechanisms and interaction dynamics. Furthermore, we delve into the stability characteristics of the EZKE, enhancing our understanding of its mathematical and physical implications. Our findings not only contribute to theoretical insights into nonlinear wave phenomena in (3+1)-dimensional space but also hold practical significance in plasma physics, nonlinear optics, and electromagnetic wave propagation. This study advances the development of innovative wave manipulation and control techniques, with applications ranging from plasma confinement in fusion devices to the design of advanced photonic devices for telecommunications and sensing purposes.

Pulse echo method for characterizing the ultrasonic properties of argan oil compared to vegetable oils with similar fatty acid profiles

Abstract This study investigates the use of a non-destructive ultrasonic pulse-echo method to characterize argan oil and other vegetable oils with similar fatty acid profiles. Ultrasonic parameters such as velocity, acoustic attenuation and reflection coefficient were measured across different temperatures. These parameters were used to create predictive models of ultrasonic velocity using partial least squares regression. Results demonstrated significant correlations between ultrasonic properties, oil temperature and fatty acid content. Specifically, ultrasonic velocity was found to be directly proportional to the content of saturated fatty acids and inversely proportional to the content of monounsaturated and polyunsaturated fatty acids. The reflection coefficient was also found to correlate with the fatty acid composition and temperature of the oils. Additionally, both ultrasonic velocity and acoustic attenuation decreased as the temperature increased from 25 °C to 50 °C. At 25 °C, the acoustic attenuation for argan oil was 22.37 Np/m, decreasing to 11.37 Np/m at 50 °C. These correlations allowed for the differentiation between food-grade argan oil, argan oil from seeds regurgitated by goats, sesame oil and peanut oil. The developed predictive models exhibited high coefficients of determination, with a value of 97.03 for argan oil, indicating strong agreement between measured and predicted ultrasonic velocities. This research highlights the potential of ultrasonic techniques for oil characterization and quality control.