Processing Methods Research Articles

Village-level Processing of Pangi (Pangium edule Reinw.) Seeds by the Obu Manuvu Improves the Edibility and Level of Some Nutritional Factors [RESEARCH NOTE

Pangi (Pangium edule Reinw) seed is known to be a poisonous material. However, it becomes safe for consumption after processing. This study determined the effect of village-level processing by the Obu Manuvu community of Marilog District, Davao City, Philippines on the antinutrient and nutritional composition of Pangi seeds. Processing methods involved washing with running water for about 15 s, boiling in water for 30 min, and soaking in flowing water in a nearby river overnight. Results showed that after processing, the levels of antinutrients such as cyanogenic glycosides and oxalates were reduced significantly (p < 0.05) by 99.15% and 78.23%, respectively. Further, the tannin content was also significantly reduced by 96.15%. The crude fat content significantly increased (p < 0.05) by about 54%. Crude ash and fiber decreased, while protein did not change significantly (p < 0.05). Minerals such as calcium and zinc increased (p < 0.05) by 221.43% and 64.39%, respectively while the iron and manganese levels remained unaffected. Findings of the study suggested that the processing method by the Obu Manuvu effectively reduces the level of antinutrients, thereby improving the edibility and safety of Pangi seeds. Further, the processing method improves the level of some nutrients, specifically crude fat, calcium, and zinc.

Video2Haptics: Converting Video Motion to Dynamic Haptic Feedback with Bio-Inspired Event Processing.

In cinematic VR applications, haptic feedback can significantly enhance the sense of reality and immersion for users. The increasing availability of emerging haptic devices opens up possibilities for future cinematic VR applications that allow users to receive haptic feedback while they are watching videos. However, automatically rendering haptic cues from real-time video content, particularly from video motion, is a technically challenging task. In this article, we propose a novel framework called "Video2Haptics" that leverages the emerging bio-inspired event camera to capture event signals as a lightweight representation of video motion. We then propose efficient event-based visual processing methods to estimate force or intensity from video motion in the event domain, rather than the pixel domain. To demonstrate the application of Video2Haptics, we convert the estimated force or intensity to dynamic vibrotactile feedback on emerging haptic gloves, synchronized with the corresponding video motion. As a result, Video2Haptics allows users not only to view the video but also to perceive the video motion concurrently. Our experimental results show that the proposed event-based processing methods for force and intensity estimation are one to two orders of magnitude faster than conventional methods. Our user study results confirm that the proposed Video2Haptics framework can considerably enhance the users' video experience.

Co-vulcanization of natural rubber and Eucommia ulmoides gum for mediation of the nonlinear rheology behaviors

Co-vulcanization of natural rubber (NR) and high-moduli Eucommia ulmoides gum (EUG) is promising while the reduction in crystallinity of crosslinked EUG is adverse for developing high-performance materials. Proposed herein is a novel method to prepare high-strength and high-stretchability NR/EUG vulcanizates with rapid vulcanization of EUG into slightly crosslinked particles, followed by further vulcanization of NR to form crosslinked matrix network. Deep eutectic solvents (DESs) are employed to facilitate the vulcanization of EUG during its blending with NR. The two-step vulcanization results in vulcanizates exhibiting superior mechanical properties under shear, tensile, and compressive conditions, significantly exceeding those of vulcanizates prepared by traditional processing methods. The reinforcement mechanism is elucidated by controlling thermomechanical coupling conditions and is supported by comprehensive structural characterizations. It is suggested that the EUG crystalline regions, maintained through the special processing method, work in conjunction with the stress-induced crystallization of the matrix to enhance the vulcanizates, nearly doubling the deformation stress at 800% strain. The crystalline regions can mediate the deformation stress during shear and compression and weaken nonlinear rheological behavior. The established structure-performance relationship is guidable for preparing high-performance NR/EUG blend vulcanizates.

Camel milk whey powder formulated using thermal (spray-drying process) and non-thermal (ultrasonication) processing methods: Effect on physicochemical, technological, and functional properties

Whey protein concentrates (WPCs) are gaining importance as a functional ingredient due to their high technological and functional properties. In this study, Camel milk whey (CW) separated from skimmed camel milk, then either spray-dried (SD) at 170, 185 and 200 °C, or treated by ultrasonication (US) (20 kHz (for 5, 10 and 15 min followed by freeze-drying to obtain camel milk whey powder (CWP). The structural analysis of CWP was carried out by Fourier-Transform Infrared Spectroscopy (FTIR) and X-Ray Diffraction (XRD) which showed no significant difference in the functional groups profile of US samples compared to control and SD samples. US samples showed some degree of crystallinity that was comparable to the control samples, while SD samples exhibited very low degree of crystallinity. The surface morphology, particle size, and surface charge of CWP were evaluated using scanning electron microscopy (SEM) and Zetasizer. The lowest particle size of 215.1 nm with surface charge of −21.6 mv was observed in SD-185 WPC. Moreover, SD samples revealed whiter color compared to the US-treated samples which were having lower L* values (P < 0.05). US-15 sample exhibited high protein solubility (100 %), whereas the SD-200 sample showed reduced solubility (92.7 %). Improvement in the emulsifying activity of CWP samples was observed after SD and US, with highest emulsifying activity index (EAI) values of 143.75 m2/g and 143.11 m2/g were reported for SD-185 and US-15 CWP samples, respectively. To conclude, SD and US were found to improve the physico-chemical, technological, and functional properties of CWP, and thus can be utilized as a promising strategy to preserve and enhance techno-functional properties of CWP

Fabrication of angle-gradient echelle grating on metallic glass using shaped vibration cutting with time-varying trajectory

In order to solve the problem of narrow band coverage of traditional infrared gratings, a new type of diffraction grating is invented and named as angle-gradient echelle grating. The blaze angle of the grating gradually changes along the direction of the grating grooves, which can be regarded as etching countless sub-gratings with different blaze angles on a grating, breaking the limitation of the number of blaze angles of traditional gratings. In addition, in order to solve the processing problem of angle-gradient echelle grating, a new processing method, shaped vibration cutting (SVC) with a time-varying trajectory is developed. By exploring the influence of different processing parameters on the grating processing results, a suitable processing plan was proposed. Eventually, angle-gradient echelle grating with blaze angles varying from 30° to 55° and surface roughness of as low as 20 nm was fabricated on metallic glass, a material that is difficult to machine.

Membrane-cryogenic hybrid CO2 capture—A review

The membrane-cryogenic hybrid process is a promising CO2 capture process, which combines the advantages of membrane and cryogenic, such as high efficiency (up to 98 % CO2 captured) and low energy consumption (specific energy consumption around 1.7 MJ/kg CO2 avoided). Through pretreatment by membranes, CO2 concentration can be increased, which makes it possible to separate CO2 via phase change in the cryogenic unit. This work reviews the current status of the development of membrane-cryogenic hybrid processes. The synergy between membrane and cryogenic separation is summarized to identify the bottleneck of such processes and provide insights for process improvement. It was found that cold temperatures would be beneficial to reduce CO2 activation energy and then improve CO2 selectivity of membranes. To further improve the CO2 separation performance, the potential intensification methods of the membrane-cryogenic hybrid process including cold-membrane synthesis, process optimization via heat integration are discussed and envisioned.

Dissociation extraction: Theoretical foundations and applications

Dissociation extraction (DE) is a separation method that has been known for many decades but is not yet widely used in industry. However, there are a number of applications for which DE may be applied, for example, for new biorefinery separations. To facilitate future applications, we derived a new shortcut method for DE processes and demonstrated its application in the conceptual design of a separation process for acidic and basic organic mixtures. The new method allows for the calculation of the minimum energy requirement and a fast comparison of dissociation extraction with other separation methods. The shortcut method was validated using experimental data from the literature and is easy to implement because it is based only on the physical distribution coefficients and dissociation constants of the acids or bases to be separated.In the second part of this paper, we identify several areas that require effective separation techniques, where dissociation extraction can have an impact. These include biorefinery separation, polymer recycling, and fatty acid fractionation.In conclusion, we hope to promote DE as a known but less-used separation method for difficult separation problems in both fossil-based and bio-based sustainable chemical products.

Application of selected indicators to assess contamination of municipal landfill leachate and its impact on groundwater

Leachate formation is one of the most important factors taken into account during the operation and long-term management of municipal waste landfills. Systematic assessment of groundwater and leachate contamination may be useful in selecting the appropriate method of leachate management or treatment processes. The use of indicators to quantify the contamination potential of leachate and groundwater in the vicinity of MSW could help landfill managers assess their quality. Therefore, the aim of the study was to assess the representativeness of selected indicator methods for analyzing the temporal variability of leachate and groundwater properties in the vicinity of two municipal waste landfills in a Central European country (Poland). The leachate pollution index (LPI), sub-LPI and adjusted leachate pollution index (r-LPI) were used to assess the quality of leachate water, while the landfill water pollution index (LWPI) was used to assess the variability of groundwater quality. The results confirmed that LWPI is an effective method for assessing the quality of groundwater in the vicinity of municipal waste landfills. The obtained results confirm the negative impact of landfills, despite the insulation used. LWPI showed poor quality of groundwater and visible impact of the landfill (landfill W, average LWPI - 2.34) and moderately polluted waters and minor impact of the landfill (landfill S, average LWPI - 1.37). In most cases, it was observed that two parameters, EC and TOC, are the main factors contributing to the deterioration of groundwater quality. The sub-LPI analysis showed that leachates from both landfills have a very low content of heavy metals, so they should not have a negative impact on the biological treatment process. The obtained r-LPI values were in all cases higher than the calculated LPI values. For landfill S, the average r-LPI was 26.3 (Z-1) and 25.7 (Z-2). However, the average LPI was 13.5 (Z-1) and 13.2 (Z-2). For landfill W, the average r-LPI was 14.6 and the average LPI was 11.4. Analysis conducted on multi-year leachate and groundwater data using specific indicators can help managers better understand the impact of MSW on surrounding areas and help avoid potential operational problems in the future.

Selective weighted least square and piecewise bilinear transformation for accurate satellite DSM generation

One of the main products of multi-view stereo (MVS) high-resolution satellite (HRS) images in photogrammetry and remote sensing is digital surface model (DSM). Producing DSMs from MVS HRS images still faces serious challenges due to various reasons such as complexity of imaging geometry and exterior orientation model in HRS, as well as large dimensions and various geometric and illumination variations. The main motivation for conducting this research is to provide a novel and efficient method that enhances the accuracy and completeness of extracting DSM from HRS images compared to existing recent methods. The proposed method called Sat-DSM, consists of five main stages. Initially, a very dense set of tie-points is extracted from the images using a tile-based matching method, phase congruency-based feature detectors and descriptors, and a local geometric consistency correspondence method. Then, the process of Rational Polynomial Coefficients (RPC) block adjustment is performed to compensate the RPC bias errors. After that, a dense matching process is performed to generate 3D point clouds for each pair of input HRS images using a new geometric transformation called PWB (pricewise bilinear) and an accurate area-based matching method called SWLSM (selective weighted least square matching). The key innovations of this research include the introduction of SWLSM and PWB methods for an accurate dense matching process. The PWB is a novel and simple piecewise geometric transformation model based on superpixel over-segmentation that has been proposed for accurate registration of each pair of HRS images. The SWLSM matching method is based on phase congruency measure and a selection strategy to improve the well-known LSM (least square matching) performance. After dense matching process, the final stage is spatial intersection to generate 3D point clouds, followed by elevation interpolation to produce DSM. To evaluate the Sat-DSM method, 12 sets of MVS-HRS data from IRS-P5, ZY3-1, ZY3-2, and Worldview-3 sensors were selected from areas with different landscapes such as urban, mountainous, and agricultural areas. The results indicate the superiority of the proposed Sat-DSM method over four other methods CATALYST, SGM (Semi-global matching), SS-DSM (structural similarity based DSM extraction), and Sat-MVSF in terms of completeness, RMSE, and MEE. The demo code is available at https://www.researchgate.net/publication/377721674_SatDSM.

Developing a machine learning-based evaluation system for the recruitment of maritime professionals

The maritime sector predominantly relies on subjective evaluations of seafarers' skills and experience in conventional recruiting procedures. Nevertheless, subjective evaluation methods are highly susceptible to biases and inconsistencies. This study proposes a novel recruitment process within the maritime industry by merging psychological tests and machine learning methods in the recruitment process. Using psychological tests such as MMPI-I as features in machine learning methods for the recruitment process represents a step-change approach within the industry to promote more objective assessments of maritime professionals during recruitment processes and identify suitable candidates based on data from same-rank maritime professionals. This new methodology contributes innovatively to traditional maritime sector recruitment methods and potentially addresses a significant gap in the existing literature. The proposed methodology aims to predict future values by analysing existing data sets. Data were collected from 183 volunteer cadets from different backgrounds using an application form and the MMPI-I Personality Inventory. The dataset was classified using several machine learning algorithms, and their performance metrics were compared. The top five classification algorithms (Decision tree, PNN, random forest, gradient boost trees, and naive Bayes) with the best performance were evaluated, and the most accurate classification performance was achieved with the GBT algorithm. The results show that the highest values are for Gradient Boosted Trees (86%) and Random Forest (80%). The GBT algorithm has scored higher values for other metrics as well. The findings of this study indicate that it is possible to train algorithms that can adequately forecast the credentials and appropriateness of marine recruits. Through more development, these data-driven solutions could enhance existing subjective recruitment practices. This approach could improve the objectivity and prediction accuracy of marine recruiting processes, hence facilitating the selection of highly qualified individuals.

Comprehensive models to estimate the Isobaric heat capacity of deep eutectic solvents based on Machine learning algorithms

Deep eutectic solvents (DESs) have recently gained significant attention due to their sustainable and environmentally friendly properties. Acquiring exact knowledge regarding the isobaric heat capacity (Cp) of DESs is essential for energy-related processes, in which these green solvents are utilized. Hence, this study deals with the development of comprehensive models for estimating the Cp of DESs. To reach this target, 682 experimental data, encompassing the Cp of 36 different DESs over widespread ranges of pressure and temperature, were assembled from the literature. The foregoing data were employed to establish new models based on four machine learning techniques, including Gaussian process method (GPM), Adaptive neuro-fuzzy inference system (ANFIS), Radial basis function neural network (RBF-NN) and Multilayer perceptron neural network (MLP-NN). The evaluations performed based on the statistical indices and graphical tools demonstrated that although all suggested models present excellent estimations, that designed based on the MLP-NN method yields the highest accuracy with Mean absolute percentage error (MAPE) and coefficient of determination (R2) values being 0.4% and 99.93, respectively, for the validation data. A comparison between the results of the literature correlations and those of novel models confirmed the obvious superiority of the latter. Moreover, the proposed models properly described the Cp variations of different DESs versus pressure and temperature. The order of importance of various factors in controlling the Cp of DESs was also determined based on the sensitivity analysis. Eventually, the intelligent models showed excellent performance in estimating the Cp of unseen DESs.

Utilization of banana crop ligno-cellulosic waste for sustainable development of biomaterials and nanocomposites.

Banana (Musa spp.) is a tropical fruit cultivated in over 130 countries, producing significant lignocellulosic biomass. However, much of the agro-industrial waste from banana plants is neglected, contributing to environmental pollution. Around 60% of the plant's biomass is generated after fruit harvesting, representing an untapped resource. This review examines the potential of banana plant waste for developing biocomposite and biodegradable materials. It covers the extraction and modification of banana fibers for composites, with a focus on the fabrication of nano biocomposites using banana fibers as reinforcement and polysaccharides or proteins as matrices. The review also evaluates the biodegradability and environmental impact of these materials through Life Cycle Assessment studies. Future research directions include refining processing methods, improving fiber-matrix compatibility, and enhancing the durability of banana fiber composites for packaging applications.

Highly protective and functional strengthening strategies for 3D printed continuous carbon fiber reinforced polymer composites: manufacturing and properties

Carbon fiber-reinforced polymer (CFRP) composites have gradually emerged as a crucial material in the aerospace industry. However, inadequate impact resistance and thermal stability limit survival in extreme environments. Inspired by the specific functions of multiple layers of tissue, from bird feathers to the musculoskeletal system. We propose low-cost composite strengthening strategies and efficient novel three-dimensional graphene aerogel manufacturing methods. A novel graphene aerogel/carbon fiber reinforced polymer (GAC) composite prepared by in-situ laser-induced graphene (LIG) layers on the surface of 3D-printed CFRP. GAC composites enhance CFRP's impact protection, thermal insulation, and electromagnetic shielding capabilities. For protection, GAC composites reduce low-velocity impact damage by 26.2%. The graphene layer can increase the thermal buffering capacity of the material four times, and the long-term service temperature can be increased by 40% compared to traditional CFRP. For functionality, the composites enable electromagnetic interference (EMI) shielding effectiveness of up to 50.2 dB. Furthermore, it can achieve surface heating above 400 °C and rapid de-icing through electrothermal effects. The simple and efficient processing method of GAC composites and tunable functionalities holds promise for the development of highly protective and intelligent aircraft skins.

Carbonic anhydrase activity and metabolite variation of different microalgae species at alkaline pHs

A significant increase in atmospheric greenhouse gases over the last century has led to the development of several methods and technologies to remove carbon dioxide (CO2). Microorganisms produce carbonate minerals through the natural mineralization of CO2; however, the feasibility of this process remains in research. This work aimed to study the cultivation of different microalgae under alkaline pH to maintain their potential for carbon mitigation. According to the results, the highest carbonic anhydrase activity has been reached (4.64 mg/g) for Chlamydomonas reinhardtii at a pH of 10. C. reinhardtii, at a pH of 9.5, yielded the highest chlorophyll content (23.58 mg/g), while Spirulina at a pH of 8.5 produced the highest biomass (882.9 mg/L). Also, a positive correlation existed between pH and lipid content for C. reinhardtii. Spirulina, however, exhibits the opposite effect. According to a principal component analysis, there is an opposite relationship between pH and carbonic anhydrase (CA) activity for C. reinhardtii and filamentous-type cyanobacteria from Salda Lake. The following order of the suitability of the microalgae species for high carbon capture is determined by the Analytic hierarchy process method: Spirulina>C. reinhardtii > Chlorella vulgaris > Coccus-type cyanobacteria > Filamentous-type cyanobacteria from Salda Lake. Additionally, this study provided important results regarding the cyanobacteria species isolated from an alkaline lake, Lake Salda. This would contribute to future studies of carbon capture and carbon mitigation mechanisms.

Implementation of global navigation satellite system softwaredefined radio baseband processing algorithms in system on chip

&lt;p&gt;The global navigation satellite system (GNSS) is an international navigation system that determines users' locations globally using a constellation of satellites. Conventional hardware-based receivers often face challenges related to cost-effectiveness and lack of reconfigurability. To address these issues, GNSS software receivers have emerged, executing baseband processing methods on host computers. However, host PC-based GNSS software receivers encounter obstacles during real-time signal acquisition, such as computational complexity and data loss. This research paper introduces a real-time system on chip (SoC)-based GNSS software receiver to mitigate these concerns. The receiver utilizes the USRP N210 radio frequency (RF) front end to acquire GNSS signals in real-time. Baseband processing algorithms are executed using the Zynq 7000 SoC board, with modifications applied to the acquisition module. The effectiveness of the SoC-based GNSS receiver is evaluated under both stationary and dynamic conditions. Experimental outcomes indicate that the receiver provides precise user localization and facilitates prototype development. This methodology not only overcomes the limitations of conventional hardware-based receivers but also leverages the benefits of SoC architecture to process GNSS signals in a flexible and efficient manner.&lt;/p&gt;

Pneumonia stage analyzes through image processing

A physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes.

Optimization of thermal shock response spectrum as infrared thermography post-processing methodology using Latin hypercube sampling and analytical thermal N-layer model

In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-to-noise ratio (SNR) were used.

Foamed calcium sulfoaluminate cement with controlled porous network for daily or seasonal heat storage in ettringite

Calcium sulfoaluminate (CSA) cement-based materials have high ettringite content enables heat storage, enhancing the thermal inertia of buildings. This study aims to optimize the porous network of foamed CSA material in terms of porosity, permeability and mechanical strength so that they can be effective in building applications as supporting structures with heat storage capacity. To achieve these goals, a foaming process method using hydrogen peroxide (H2O2) and surfactant was used to control the porous network. The H2O2 and surfactant contents varied from 0.5% to 1.2% and 0.01% to 0.05%, respectively, and generated a wide range of material densities from 589 to 1184 kg/m3 - and hence a wide range of properties in terms of porosity (43 - 70%), permeability (2.6 10-13 - 7.1 10-12 m2) and compressive strength (0.8 -9.4 MPa). Then, an abacus correlating material properties, found by surface fitting, linking porosity, gas permeability and compressive strength, allowed the material to be optimized in accordance with the needs of each building application. The CSA paste consisting of 1% H2O2 and 0.03% surfactant, with 66% porosity, 4.1 10-12 m2 permeability, and 1.8 MPa compressive strength, was the optimal mix-design for solar storage box applications (for short-term storage). The CSA paste consisting of 0.5% H2O2 and 0.01% surfactant, with 43% porosity, 5.6 10-13 m2 permeability, 9.4 MPa compressive strength, appeared to be optimal for self-supporting wall storing solar heat (long-term storage).

In-situ grown carbon nanotubes on waste glass powder: resource-efficient preparation and machine-learning based dispersion evaluation

Carbon nanotubes (CNTs) are the strongest candidates as reinforcing and functionalizing materials in civil engineering. However, CNTs have severely limited the application of carbon nanomaterials in civil engineering materials because of high-cost preparation and inhomogeneous dispersion. In this paper, a new method of direct in-situ grown CNTs on the surface of waste glass powder (WGP) by microwave pyrolysis is employed. It can control the particle size and growth of CNTs while effectively improving the CNTs dispersion and saving natural resources. The dispersibility of in-situ grown CNTs and MWCNT in aqueous and cement pore solutions is evaluated based on DLS and improved machine learning and image processing methods. The conductivity as well as the compressive strength of the electrically conductive cementitious materials increases linearly with increasing dispersion. This work facilitates further optimization of CNT-cement nanocomposite dispersion and has great potential for improving WGP utilization efficiency.

Feasibility analysis and parameter optimization of ultrasonic assisted electrolytic plasma polishing of small modulus gears

Precision polishing of small modulus gears is a difficult task due to the complexity of their geometry. Electrolytic plasma polishing technology as a green and efficient processing method, it can effectively decrease the friction between parts and improve the service life. However, considering the uneven distribution of gas layers on the surface of workpieces in electrolytic plasma polishing, which affects the precision of workpiece shapes after polishing, this study proposed the use of ultrasonic vibrations to generate pressure waves to improve the fluid characteristics. Comparative experiments on small modulus gear electrolytic plasma polishing with and without ultrasonic vibration were conducted. A high-speed camera was utilized to monitor the distribution of gas layers around the workpiece during the polishing process in real-time. The optical measurement methods were used to evaluate the gear precision. The results indicate that ultrasonic assistance is beneficial in improving the uniformity of the gas layers surrounding the workpiece, achieving higher gear precision, and obtaining a lower surface roughness after polishing. Through orthogonal experiments, this study analyzed the effects of various process parameters on the surface roughness of gear profiles and the uniformity of material removal after polishing. These parameters included power voltage, polishing time, workpiece immersion depth, ultrasonic frequency, and workpiece posture angle. An optimal combination of process parameters was identified. The results show that the best surface roughness and gear precision were achieved with a power voltage of 300 V, a polishing time of 4 min, a workpiece immersion depth of about 10 mm, an ultrasonic frequency of 40 kHz, and a workpiece tilt angle of 45°.