Key Process Variables Research Articles

Optimizing Hot Forging of 38MnSiVS5 Steel: A Multi-Objective Approach with Grey-Fuzzy Method

The present research focuses on the hot forging of 38MnSiVS5 micro-alloyed steel, examining the impact of key process variables, such as working temperature, deformation percentage and rate of cooling on mechanical properties, notably the ultimate tensile strength and impact energy. To optimize the process, Taguchi's parametric design, utilizing an orthogonal array in combination with grey relational analysis and fuzzy logic analysis, has been implemented. By applying grey-fuzzy logic analysis, the optimization of complex multiple responses is streamlined into a single grey-fuzzy reasoning grade. The study employs the Grey fuzzy logic method to concurrently optimize both responses. The grey-fuzzy reasoning grade serves as a performance index, aiding in the determination of the optimal process parameter settings for both the ultimate tensile strength and impact energy responses simultaneously.

Diagnosis and prognosis of soybean roaster failures using particle swarms

In industry, the monitoring and diagnosis of production processes are crucial issues in ensuring plant reliability, performance and quality. In particular, food processing operations, such as coffee roasting, are subject to numerous risks of failure that can impact on productivity and the quality of the final product. In this context, the main objective of this study was to develop an innovative method for the diagnosis and prognosis of failures in a coffee roasting process. The proposed method differs from standard approaches by using the particle swarm optimization (PSO) algorithm applied to the analysis of signatures of key process variables. This new approach has improved fault detection, with a recognition rate of over 90% for the main types of fault identified, such as heating problems, air obstructions or leaks. In addition to diagnostics, the method has also demonstrated its effectiveness in prognosticating the state of health of the process, with an average error on the prediction of remaining service life reduced to 15%, compared with 35% for fixed-threshold methods. This work has therefore enabled us to develop an innovative method offering superior performance to standard approaches for the diagnosis and prognosis of failures in the roasting process.

Influence of processing variables on fabrication of AA7475/B4C/Al2O3 hybrid surface composites via FSP

The motive of this research is to fabricate AA7475/B4C/Al2O3 hybrid surface composites (HSCs) using friction stir processing (FSP) for various applications in aerospace, marine, and automotive industries. The research systematically varied key processing variables, including rotational speed (900–1300 rpm), feed rate (30–60 mm/min), and reinforcement ratios (from 30% B4C and 70% Al2O3 to 70% B4C and 30% Al2O3), to optimize responses such as ultimate tensile strength (UTS), tensile elongation (TE), and microhardness (MH). A face-centered central composite design (FCCCD) of response surface methodology (RSM) was employed to develop mathematical models correlating the input parameters with the response variables. An analysis of variance (ANOVA) was conducted to assess the effects and interactions of the processing parameters on the composite properties. A validation test confirmed the reliability of these optimal conditions. ANOVA results indicated that the tool rotational speeds had the most significant effect on the tensile, ductility, and hardness properties of the HSCs. However, changes in feed rate and reinforcement ratio had minimal impact on these properties. The study identified optimal conditions for different responses: UTS of 452.62 MPa, TE of 6.89%, and MH of 168.52 HV at a rotational speed of 1300 rpm, feed rate of 45 mm/min, and a 50:50% reinforcement ratio (50% B4C and 50% Al2O3). The HSCs fabricated at 1300 rpm, 45 mm/min, and a 50:50% reinforcement ratio exhibited significant necking before failure, leading to a ductile failure mode.

Optimizing macroalgal biochar-based catalysts for monophenols recovery during wood dust catalytic pyrolysis: A comparative study using response surface methodology and artificial neural network

This study aimed to explore the optimization of macroalgal biochar-based catalysts (MBBCs) for enhanced monophenolic production in biomass pyrolysis. By utilizing response surface methodology (RSM) and artificial neural network (ANN), key variables in the activation process during preparation of MBBCs were identified including activation temperature, activator ratio, and CO2 residence time. The results showed that the ANN model, with an R2 of 0.9816, accurately predicted optimal catalyst activation conditions, closely matching the experimentally determined optimal conditions (ENPC800-1:2–30). The bio-oil yield decreased under ENPC800-1:2–30 catalyst, but the monophenolic content increased significantly, reaching a relative content of 60 %, with a total concentration of phenol, cresols, and ethylphenol amounting to 35.54 mg/gbio-oil. The present study revealed significant changes in non-condensable gas composition, including increased hydrogen and carbon monoxide which aid in enhancing their energy applications. Thus, the optimized MBBCs greatly help in improving the bio-oil and non-condensable gas quality and thereby providing vital insights over sustainable chemical production through biomass pyrolysis.

Recovery of Magnetic Ni Particles from Spent Catalyst Leachate by Direct Cementation

An alternative method based on cementation for the recovery of nickel from spent Ni/Al2O3 reforming catalyst pregnant leach solution (PLS) was proposed to overcome the limitations of traditional two-step extraction and precipitation processes. Thermodynamic analysis was used to evaluate the potential interference of key reactions, such as nickel and sacrificial metal leaching, with the selective cementation of nickel from the PLS. Key variables in the cementation process were optimized using response surface methodology (RSM) combined with Box–Behnken design (BBD). Under optimal conditions—pH 2.2 ± 0.1, processing time of 15 min, and Al/Ni molar ratio of 2.65—a maximum nickel recovery of 73.2% was achieved. Extensive characterization confirmed the high quality of the cemented nickel product: (i) ICP-OES indicated nickel purity of 99.47%, (ii) XRD patterns verified the presence of pure face-centered cubic nickel, (iii) SEM-EDS and vibrating sample magnetometry confirmed the high purity of the metallic nickel particles.

Optimisation of Electrospinning Parameters for High-Strength Oriented Pan Nanofibre Mats

Abstract This study explores the optimisation of electrospinning parameters to enhance the strength and orientation of polyacrylonitrile (PAN) nanofibre mats, which are pivotal in filtration applications and other areas requiring high-strength, uniformly distributed nanofibres. The focus is on the electrospinning process, particularly on a rotating drum collector, which is instrumental in determining the morphology of nanofibres. Key process variables, including the distance between the collector and the syringe, feed rate, applied voltage, and polymer concentration, have been meticulously examined to understand their influence on the physical properties of PAN nanofibres. Our findings indicate that parameters such as voltage, collector-to-syringe distance, and drum rotation speed (RPM) significantly impact the mechanical performance of the nanofibre mats. The optimised electrospinning conditions produced PAN nanofibre mats with tensile strengths ranging from 1.61 MPa to 21.78 MPa, demonstrating a notable improvement in strength due to controlled fluid dynamics and expulsion of polymer solution during the process.

Adaptive soft-sensor update by Latest Sample Targeting Frustratingly Easy Domain Adaptation

Soft-sensors are widely used in manufacturing processes to estimate key process variables; however, their performance may deteriorate when process characteristics change. Although Just-In-Time (JIT) modeling techniques have been proposed for adaptive soft-sensor design, they do not always adapt to abrupt changes. Transfer learning (TL) has been suggested as a means to address this issue, with Frustratingly Easy Domain Adaptation (FEDA) being used for soft-sensor design. This study proposes a new TL method called Latest Sample Targeting-FEDA (LST-FEDA) for JIT-based soft-sensor, which can handle both sudden and gradual changes in process characteristics. LST-FEDA updates soft-sensors using a fixed number of latest samples whenever a new sample is obtained. The effectiveness of the proposed method was demonstrated using simulation data from a vinyl acetate monomer (VAM) process and actual operation data from a fluorine-based monomer (FM) process. LST-FEDA accurately estimated objective variables during sudden malfunctions and scheduled maintenance, contributing to efficient and safe process operation.

Experimental Investigations on the Fabrication of Low Alloy Steels Using Wire Arc Additive Method

Wire arc additive manufacturing (WAAM) has emerged as a transformational technology with the capacity to redefine the landscape of alloy steel component production. This method utilizes an electric arc to selectively fuse a continuous wire feed, progressively constructing intricate metal structures layer by layer. GMAW-based WAAM adaptability enables the creation of complex geometries and customized part designs, making it applicable across diverse industrial sectors. This paper investigates WAAM-specific applications in alloy steel fabrication, focusing on key process variables such as voltage, travel speed, and Gas mixture ratio. The experimental parameters for a single layer are examined with voltage ranging from 20 to 22, travel speed from 23, 25, and 27 mm/min, and Gas mixture ratio 1,5,9 mm/min. It utilizes materials like TM B9, a 1.2- diameter flux-cored wire. Additionally, the study considers the nominal composition (wt-%) of 9 Chromium, and 1 Molybdenum, with small additions of nitrogen and niobium to improve the creep resistance. Furthermore, the final findings of the study suggest that the optimal combination of process variables for achieving highquality weld beads in WAAM of alloy steel components can be determined through Response Surface Methodology (RSM). RSM is a statistical method for analysing and modeling the relationship between the response of interest and some independent variables. In this case, the response variable would be the quality of the weld bead, which encompasses factors such as bead geometry, integrity, and absence of defects.

Adsorption of cadmium from wastewater with activated carbons derived from pig fur biowaste: A comparative study of in-situ and ex-situ activation routes

The environmental challenges associated with cadmium contamination in wastewater have necessitated the development of high-performing activated carbons (ACs) for effective wastewater treatment. Adsorption capacity depends on both the surface area and the adsorption-active functional groups developed on the adsorbent's surface during activation. Proper manipulation of key process variables using the appropriate activation route produces highly efficient and economically viable ACs. This research investigates the viability of pig fur biowaste as a novel precursor for activated carbons using two distinct activation methods—in-situ and ex-situ. Using a central composite design (CCD) of the Response Surface Methodology (RSM), the study systematically examines the effects of impregnation ratio, carbonization temperature, and carbonization time on the cadmium adsorption capacities of the resulting ACs. The optimal conditions for in-situ activation were found to be 691 °C, 175.11 min, and an impregnation ratio of 1.784 g/g, resulting in a cadmium adsorption capacity of 91.57 %. For ex-situ activation, the optimal conditions were 468.8 °C, 80.81 min, and an impregnation ratio of 2.915 g/g, which achieved a higher cadmium adsorption capacity of 91.21 %. Both types of activated carbons maintained high efficiency after five regeneration cycles, indicating they are suitable for long-term applications requiring repeated regeneration. Although both methods produced ACs with comparable cadmium removal efficiency, the ex-situ activation route proved to be more economically viable due to its lower temperature and shorter processing time. This study demonstrates the potential of pig fur biowaste as a sustainable and underutilized resource for AC production and highlights the ex-situ activation route as the more cost-effective approach for producing high-performance adsorbents.

Techno-economic assessment of bio-hythane upgrading processes based on ionic liquids

Hythane, the blending of hydrogen (H2) and methane (CH4), is attracting attention as it could accelerate the H2 economy. H2 and CH4 can be extracted from organic material by fermentation and anaerobic digestion, respectively, but this alternative bio-hythane also contains carbon dioxide (CO2) that must be removed. This work addresses the use of Aprotic N‑Heterocyclic Anion-based ionic liquids (AHA-ILs) for bio-hythane upgrading. Three AHA-ILs ([P2228][2-CNPyr], [P2228][3-IPyra], and [P2228][4-BrIm]) were evaluated at process scale by conducting rate-based COSMO-based/Aspen Plus simulations of the upgrading processes. A wide range of operating conditions were screened by modifying key process variables such as the absorption pressure and the desorption temperature, and the bio-hythane composition. Energy and AHA-IL consumptions were analyzed together with variable operating cost (VOC). Energy demand and VOC were used to identify the Pareto optimal for each case study using a Python code. In addition, a detailed economic study was performed for the best three systems. The proposed processes are very versatile and are capable of handling bio-hythane of different compositions, being 10 %mol H2, 60 %mol CH4, and 30 %mol CO2 the most suitable for better process performance. For this case, the energy demand is about 0.51–0.56 kWh/Nm3 and VOC is about 1 cent. $/Nm3. The AHA-IL [P2228][3-IPyra] exhibited the best results for both variables. The energy requirement is in line with the minimum energy required for amine scrubbing technology, while VOC can be reduced by up to 17–48 % compared with electricity-intensive water scrubbing, pressure swing adsorption or membranes. Although higher investment is necessary, the assessed bio-hythane upgrading processes have the potential to operate around 0.1 $/Nm3, with VOC contributing about 8 % to the total cost.

Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights

PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations.

Estimation of quality variables in a continuous train of reactors using recurrent neural networks-based soft sensors

The first stage in the industrial production of Styrene-Butadiene Rubber (SBR) typically consists in obtaining a latex from a train of continuous stirred tank reactors. Accurate real-time estimation of some key process variables is of paramount importance to ensure the production of high-quality rubber. Monitoring the mass conversion of monomers in the last reactor of the train is particularly important. To this effect, various soft sensors (SS) have been proposed, however they have not addressed the underlying complex dynamic relationships existing among the process variables. In this work, a SS based on recurrent neural networks (RNN) is developed to estimate the mass conversion in the last reactor of the train. The main challenge is to obtain an adequate estimate of the conversion both in its usual steady-state operation and during its frequent transient operating phases. Three architectures of RNN: Elman, GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) are compared to critically evaluate their performances. Moreover, a comprehensive analysis is conducted to assess the ability of these models to represent different operational modes of the train. The results reveal that the GRU network exhibits the best performance for estimating the mass conversion of monomers. Then, the performance of the proposed model is compared with a previously-developed SS, which was based on a linear estimation model with a Bayesian bias adaptation mechanism and the use of Control Charts for decision-making. The model proposed here proved to be more efficient for estimating the mass conversion of monomers, particularly during transient operating phases. Finally, to evaluate the methodology utilized for designing the SS, the same RNN architectures were trained to online estimate another quality variable: the mass fraction of Styrene bound to the copolymer. The obtained results were also acceptable.

Influence of extruder geometry and bio-ink type in extrusion-based bioprinting via an in silico design tool

Planning a smooth-running and effective extrusion-based bioprinting process is a challenging endeavor due to the intricate interplay among process variables (e.g., printing pressure, nozzle diameter, extrusion velocity, and mass flow rate). A priori predicting how process variables relate each other is complex due to both the non-Newtonian response of bio-inks and the extruder geometries. In addition, ensuring high cell viability is of paramount importance, as bioprinting procedures expose cells to stresses that can potentially induce mechanobiological damage. Currently, in laboratory settings, bioprinting planning is often conducted through expensive and time-consuming trial-and-error procedures. In this context, an in silico strategy has been recently proposed by the authors for a clear and streamlined pathway towards bioprinting process planning (Chirianni et al. in Comput Methods Appl Mech Eng 419:116685, 2024. https://doi.org/10.1016/j.cma.2023.116685). The aim of this work is to investigate on the influence of bio-ink polymer type and of cartridge-nozzle connection shape on the setting of key process variables by adopting such in silico strategy. In detail, combinations of two different bio-inks and three different extruder geometries are considered. Nomograms are built as graphical fast design tools, thus informing how the printing pressure, the mass flow rate and the cell viability vary with extrusion velocity and nozzle diameter.

Path-Sampling and Machine Learning for Rare Abnormal Events: Application to Polymerization CSTRs

Previously, we developed novel, unidirectional, static, multivariate, alarm systems for rare un-postulated abnormal events, demonstrated successfully for an exothermic CSTR. Herein, our techniques are improved significantly for a more-complex polystyrene CSTR, operating in its unstable region, capable of abnormal shifts to two undesirable regions; i.e., unsafe and unreliable regions. BG-FFS, a path-sampling algorithm, is utilized to simulate efficiently multiple rare abnormal trajectories; then, the XGBoost machine learning algorithm is utilized to develop accurate predictive models for committer probabilities; i.e., pB as a function of key process variables – such models, when deployed in real-time, result in improved bidirectional dynamic multivariate alarm systems, capable of response actions using real-time pB predictions. Then, using our rationalization strategies, the initial alarm systems are evaluated and modified, followed by DRAn (Dynamic Risk Analysis) studies and sensitivity analyses to investigate the effects of varying other process parameters to achieve more effective response actions.

Feasible Actuator Range Modifier (FARM), a Tool Aiding the Solution of Unit Dispatch Problems for Advanced Energy Systems

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units.

Performance extrapolation of an ultraviolet-based photocatalytic air purifier against near-ambient-level formaldehyde

The practical utility of an air purifier (AP) with built-in adsorbent/catalyst-based filtration systems is assessed for the treatment of indoor volatile organic compounds (VOCs) through extrapolation of its performance based on a lab-scale chamber study. The feasibility of a commercial prototype AP unit with TiO2-based filters is tested in this work against 0.5–5 ppm formaldehyde (FA) in a 17 L chamber with an air recirculation rate of 565 h−1 under the control of key process variables (e.g., initial FA concentration, flow rate, and dark/light conditions). The performance of the AP unit is evaluated by the clean air delivery rate (CADR), quantum yield, and space time yield in diverse operation settings (e.g., photocatalysis only or along with adsorption). The effects of ultraviolet (UV) irradiation are evident as the CADR value of 5 ppm FA sharply increases from 5.67 (UV-off) to 15 L/min (UV-on): however, the CADR values increase only slightly (12 to 15 L/min) as the recirculation flow rate changes from 100 to 160 L/min, respectively. Further, FA concentration vs. time relationship exhibit an apparent bimodal exponential decay, with the fastest and near 100 % removal at [FA] < 0.5 ppm. The performance of the proposed AP platform in relatively large real-world volume (e.g., 4 m3, 2.4 h−1 air recirculation rate) is further estimated through extrapolation to offer valuable guidelines for the construction of AP systems in real-world applications.

Low-temperature thermocatalytic removal of formaldehyde in air using copper manganite spinels

The removal of formaldehyde (FA) is vital for indoor air quality management in light of its carcinogenic propensity and adverse environmental impact. A series of copper manganite spinel structures (e.g., CuMn2O4) are prepared using the sol-gel combustion method and treated with reduction or oxidation pretreatment at 300 °C condition. Accordingly, CuMn2O4–O (“O” suffix for oxidation pre-treatment in air) is identified as the best performer to achieve 100% conversion (XFA) of FA (50 ppm) at 90 °C; its performance, if assessed in terms of reaction kinetic rate (r) at XFA = 10%, is 5.02E-03 mmol g−1 h−1. The FA removal performance increases systematically with decreases in flow rate, FA concentration, and relative humidity (RH) or with increases in bed mass. The reaction pathways and intermediates of FA catalytic oxidation on CuMn2O4-A are studied with density functional theory simulations, temperature-programmed characterization experiments, and in-situ diffuse reflectance infrared Fourier transform spectroscopy. The synergistic combination of large quantities of adsorbed oxygen (OA) species and oxidized metal species (e.g., Cu2+) contribute to the enhanced catalytic performance of CuMn2O4–O to oxidize FA into CO2 with the reaction intermediates of H2CO2 (DOM), HCOO−, and CO. The present study is expected to provide valuable insights into the thermocatalytic oxidation of FA over spinel CuMn2O4 materials and their catalytic performances in relation to the key process variables.

Development of a mathematical model for the prediction of drill quality in voice-activated ultrasonic vibration assisted drilling process.

In different industries, drilling quality play an important role in the success of operations by producing millions of quality drilled parts. The production of high-quality parts at the lowest possible cost to the manufacturer is the primary goal of contemporary process and assembly industries. To assess the quality index of drilling holes; circularity index and surface roughness are important metrics. In order to produce better drilled parts at lower prices, this study work attempts to determine the key process variables using voice acting modules to investigate the induced vibrations effects that have the greatest impact on surface roughness and circularity index in a vertical CNC drilling machine. In order to forecast the surface roughness and circularity index of drilled holes within a given operational range, a mathematical model has been proposed by adopting a coupled algorithm using design expert and statistical approach. As an experimental design technique, Response Surface Methodology with small central composite design is used to enable the evaluation of various process parameters related with machining and vibration parameters and their effects on surface roughness and circularity index. The created model is improved using statistical tools to ensure the best match to the experimental data. It has been observed with the increase of RPM and vibration frequency the surface roughness increases up to a certain limit and then decrease whereas with the increase feed the surface roughness increases. But in terms of circularity index with the increase of the process parameters the plotted graph trends under perturbation analysis shows the similar pattern CI decreases up to a certain limit and then increases.

Integration of just-in-time learning with variational autoencoder for cell culture process monitoring based on Raman spectroscopy.

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real-time monitoring is through the application of just-in-time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman-based JITL method relies on the K-nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE-JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables.

Abrasive waterjet machining of titanium alloy using an integrated approach of taguchi-based passing vehicle search algorithm

AbstractThe titanium alloy, Ti6Al4V, is a popularly used material in aerospace and medical applications due to its specific attributes, such as excellent strength-to-weight ratio and resistance to corrosion. Ti6Al4V is challenging to machine using conventional methods due to its poor thermal conductivity, which necessitates the use of unconventional machining methods like abrasive water jet machining (AWJM). In this work, AWJM was preferred for machining Ti6Al4V, considering three key process variables: nozzle traverse speed (Tv), abrasive flow rate (Af), and stand-off distance (Sd). The performance of the AWJM process was assessed using three main metrics: Material removal rate (MRR), Surface roughness (SR), and Kerf taper angle (θ), which were crucial for evaluating the effectiveness of the AWJM. Taguchi’s L9 array, a design of experiments method, was used to plan the experiments. The adequacy of the developed models was assessed by analysis of variance (ANOVA). ANOVA results have shown that Tv, Af, and Sd were found to have a significant effect on MRR, SR, θ with contributions of 73.15%, 49.72%, and 78.35% respectively. The Passing Vehicle Search algorithm was adopted to find the global optimal solution. Simultaneous optimization results using the PVS algorithm have shown the optimal MRR, SR, and θ values of 0.17 g/min, 3.28 μm, and 2.45, respectively, at Tv of 237 mm/min, Af of 450 g/min, and Sd of 2.0 mm. The optimization results with Pareto points will help to achieve desired outcomes by selecting appropriate input conditions.