Process Parameters Research Articles

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Comparative numerical analysis between butt and lap joints of Mg alloy friction stir welding with considering tilted tool effect

Magnesium (Mg) alloy, as an important lightweight structural metal, can be effectively joined by the friction stir welding (FSW) method. Understanding FSW temperature and material flow coupling process in butt and lap welding configurations has important theoretical significance. In this study, the tilted tool induced incomplete contact distance is calculated by process parameters and slip rate, and the deflection of the contact interface is related to the tool torque. The non-uniform boundary conditions are considered. The numerical model is proposed and experimentally validated by using the measured temperatures and thermo-mechanically affected zone (TMAZ) profiles. The comparative numerical analysis between butt and lap welding with three levels of process parameters is further conducted. The peak temperature and material flow velocity along the specified circumferences in FSBW are more than 10 K and 4 mm/s higher than these in FSLW, respectively. When the rotation rate increases from 600 rpm to 800 rpm, the peak temperature and material flow velocity along the specified circumferences increase about 20 K and 9 mm/s, respectively. When the welding speed decreases from 150 mm/min to 30 mm/min, the temperature and material flow velocity fluctuations along the specified circumference decreases about 25 K and 14 mm/s, respectively.

Effect and mechanism of solution flow rate during interfacial polymerization on morphology and performance of hollow fiber membranes

Interfacial polymerization (IP) using m-phenylenediamine and trimesoyl chloride (TMC) is an essential method for producing thin-film composite (TFC) membranes. Nevertheless, IP is highly sensitive to changes in process parameters, especially the TMC solutions flow rate during inside-out TFC hollow fibers (HF) production. Research on the impact of flow conditions (i.e., laminar vs. turbulent flows) of these organic solutions on the final membrane characteristics and corresponding mechanism is still lacking. This research tested different TMC flow rates (0.1–2 mL/min) to produce inside-out TFC HF. With a TMC flow rate increase, (i) the leaf-like structures of the polyamide (PA) layer decreased in size, (ii) the thickness of the PA layer slightly increased, and (iii) the pure water permeability decreased. However, the elemental composition, chemical bonds, and NaCl rejection remained stable. Different fluid regimes are considered to affect the performance of HF membranes by affecting the heat dissipation process during IP reaction. A TMC flow rate in the laminar flow state (i.e., low flow rates) is recommended for synthesizing HFs. The current research provides new insights into the IP reaction mechanism of HF membrane, thus offering valuable guidance to optimize and produce membrane products with better performance.

The influence of laser texturing on the tribological behavior of titanium alloy Ti6Al4V in medical applications

This paper analyzes the tribological behavior of the Ti6Al4V ELI alloy subjected to laser texturization for medical purposes. Laser texturing enables one to observe specific patterns of the material surface at established depths. Microtexturing of the samples was performed using a 355 nm picosecond laser. The influence of the microtexturing process (depending on the process parameters) on the geometric parameters of the proposed laser texturing pattern was evaluated. Selected samples were subjected to tribological testing using the ball-on-plate technique in dry and lubricant-sliding methods (in Ringer solution). The wear properties were evaluated by comparing the coefficient friction, wear volumes, and wear ratio. A scanning electron microscope characterized the morphologies of the wear scar and the wear mechanism. The experimental results show that the surface texturing and the changes in microgrooves can reduce wear. The results indicate, that samples after laser texturing were characterized by 15% higher microhardness, compared to those in the initial state. It was found, a 26% reduction in friction coefficient and 29% in the wear volume compared to the smooth, untextured surface samples under lubricated conditions. The decrease in value of the coefficient friction and wear volume for the samples after the laser texturing process is an effect of synergistic of entrapped wear debris in micro-grooves and increased hardness for samples after laser textured.

Optimization of Ammonia Nitrogen Removal and Recovery from Raw Liquid Dairy Manure Using Vacuum Thermal Stripping and Acid Absorption Process: A Modeling Approach Using Response Surface Methodology

Dairy manure adds a substantial amount of nitrogen to wastewater due to its high levels of associated nutrients. Removal and recovery of ammonia nitrogen (NH3-N) from raw liquid dairy manure (RLDM) is greatly valued. This study was focused on the vacuum thermal stripping–acid absorption (VTS-AA) process for NH3-N from RLDM, followed by modeling and optimization. Using the response surface methodology (RSM)-based central composite design (CCD) approach, the critical operational parameters of the vacuum thermal stripping process, including temperature (50–70 °C), pH (9–11), vacuum pressure (35–55 kPa), and treatment time (60–90 min), were optimized. With the specified parameters set at temperature 69.9 °C, pH 10.5, vacuum pressure 53.5 kPa, and treatment time 64.2 min, the NH3-N removal efficiency attained was 98.58 ± 1.05%, aligning closely with the model prediction. Furthermore, the recovered ammonium sulfate ((NH4)2SO4) closely matched their commercial counterparts, confirming the effectiveness of the VTS-AA process in recovering NH3-N from RLDM. The distinct advantage of the employed technology lies in the concurrent energy demand reduction achieved by introducing a vacuum system. These findings contribute valuable insights into the practical implementation of the VTS-AA process for treating raw dairy manure, particularly in large-scale operational contexts.

Study on thermal runaway propagation inhibition of battery module by flame-retardant phase change material combined with aerogel felt

Phase change materials (PCMs) are susceptible to fire and may accelerate heat transfer when thermal runaway propagation (TRP) in lithium-ion battery (LIB) modules, requiring the design and safe use of insulation structures with excellent flame-retardant properties. In this work, the sandwich structure composed of flame-retardant phase change material (FRPCM) combined with aerogel felt (AEGF) is used to study the inhibitory effect on TRP of pouch battery modules (BMs). The structural materials are applied between cells, and the changes of temperature, voltage, mass and other parameters in the TRP process are analyzed by changing the thickness of FRPCMs and AEGFs, and the changes of heat flux of materials were also qualitatively analyzed. The results show that the increase of FRPCM and AEGF thickness can increase the interval time of TRP and reduce the overall thermal risk of the BMs. Compared to Test-1, the increase in TRP time between cells 1 and 2 in Test-2 to Test-5 ranged from 52.7% to 358.7%, and the longest thermal runaway interval reached 844 s. Both the structural materials in Test-6 and Test-7 block the TRP. Combining the thermal conductivity of PCMs and the thermal insulation properties of AEGFs, this work can effectively conduct heat to the environment and block the TRP when applied to BMs, which provides a valuable reference for efficient inhibition and blocking of TRP.

The Experimental Investigation of the Machinability of Armor Steels

In recent years, the use of materials with ultra-high hardness such as Millux Protection 600T has become widespread in industry. However, the effect of different parameters used in the machining process of such steels, especially factors such as feed rate, cutting speed, depth of cut, and radial depth of cut, on the surface roughness of the final product is still not fully understood. In this context, a study was conducted to evaluate the effects of the mentioned armor steel on surface roughness. Using the Taguchi optimization method, the interaction between processing parameters at specified levels was examined, and attempts were made to determine the optimum values. Within the scope of the study, nine different experiments were conducted using Taguchi's L9 orthogonal array, and the surface roughness data obtained from each experiment were recorded. According to the analysis results, it was determined that the feed rate is the most significant parameter in the milling process, followed by radial depth of cut, depth of cut, and cutting speed, respectively. To achieve optimal results, it is recommended that the feed rate be set to level 1, cutting speed to level 2, depth of cut to level 1, and radial depth of cut to level 3. These findings could provide important guidance in the machining of such steels.

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

Impact of blocking layers based on TiO2 and ZnO prepared via direct current reactive magnetron sputtering on DSSC solar cells

The optimization of dye-sensitized solar cells (DSSCs) technology towards suppressing charge recombination between the contact and the electron transport layer is a key factor in achieving high conversion efficiency and the successful commercialization of this type of product. An important aspect of the DSSC structure is the front blocking layer (BL): optimizing this component may increase the efficiency of photoelectron transfer from the dye to the semiconductor by reduction charge recombination at the TiO2/electrolyte and FTO/electrolyte interfaces. In this paper, a series of blocking layer variants, based on TiO2 and ZnO:TiO2, were obtained using the reactive magnetron sputtering method. Material composition, structure and layer thickness were referred to each process parameters. Complete DSSCs with structure FTO/BL/m-TiO2@N719/ EL-HSE/Pt/FTO were obtained on such bases. In the final results, verification of opto-electrical parameters of these cells were tested and used for the conclusions on the optimal blocking layer composition. As the conclusion, application of blocking layer consists of neat TiO2 resulted in improvement of device efficiency. It should be noted that for TiO2:ZnO/CuxO and TiO2/CuxO cells, higher efficiencies were also achieved when pure TiO2 was used as window layer. Additionally it was proven that the admixture of ZnO phase inspires Voc and FF growth, but is overall unfavorable compared to pristine TiO2 blocking layer and the reference cell, according to the final cell efficiency.

Real-time prediction method of carbon concentration in carburized steel based on a BP neural network

ABSTRACT Gears are key components of mechanical transmission systems. They need to be carburized and quenched to meet the requirements of internal toughness and external hardness, to obtain higher hardness and wear resistance. Heat treatment engineers can calculate carbon concentration distribution through the finite element analysis method, facing the challenge of high computational cost and high memory requirements. To solve this problem, a real-time prediction method of 1D and 2D carburizing concentration based on a Back Propagation (BP) neural network (BPNN) is proposed. First, carburizing experiments were conducted to verify the accuracy of the carburizing numerical model. Then, by establishing an accurate carburizing model, 37,800 and 177,147 1D and 2D carburizing training samples were generated using the finite element model (FEM) method, respectively. Finally, for 1D carburizing, the average relative error of the training model on the test set is 0.2911%. For 2D carburizing, the average relative error of the reconstructed BPNN model on the test set was 0.4381%, and a real-time performance evaluation was performed. The carbon concentration prediction time for each set of process parameters is only 0.12 s, nearly 175 times faster than FEM calculation, which meets the accuracy and real-time requirements for real-time prediction of carburizing carbon concentration.

Transfer Learning-Based Artificial Neural Network for Predicting Weld Line Occurrence through Process Simulations and Molding Trials

Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs.

One‐Step Synthesis and Deposition of Metal Oxides: NiO Quantum Dots as a Transport Layer for Perovskite Photovoltaics

One‐step synthesis and deposition of nickel oxide quantum dots using a gas‐phase microplasma process is demonstrated and their applicability as a transport layer for solar cell devices is shown. The process uses a solid nickel metal wire as sacrificial electrode, and the concentration of oxygen gas required in the synthesis is investigated. The quantum dots are characterized for physical, chemical, and optical properties and critical process parameters such as the process throughput are also estimated. Direct one‐step deposition directly on perovskite solar cells is carried out using a computer‐controlled x–y stage and the performance of the solar cell device is assessed.

Mechanical properties and thermal shock resistance of TiB2‒B4C composite ceramic tool materials at microscopic scale

AbstractA finite model incorporating microstructure was developed to investigate the thermal shock resistance of TiB2‒B4C composite ceramic cutting tool materials. Numerical simulations were conducted to analyze changes in the transient temperature field and thermal stress field during the thermal shock process. TiB2‒B4C composite ceramic tool materials were fabricated using discharge plasma sintering technology. With different B4C contents, different sintering temperatures and holding times as study variables, optimization of the sintering process parameters and ceramic material components was achieved through testing the mechanical and microscopic morphology of the ceramics. Thermal shock water quenching experiments were performed, and the strength‐decay method was employed to assess the thermal shock resistance of TiB2‒B4C ceramics. The findings indicate that the optimal sintering conditions are temperature of 1700°C and holding time of 5 min. The TiB2‒B4C ceramic material containing 20 vol% B4C exhibits superior comprehensive mechanical properties and thermal shock resistance, and its relative density, fracture toughness, hardness, and flexural strength were 99.3%, 6.8 MPa m1/2, 22.8 GPa, and 598 MPa, respectively. The critical thermal shock temperature difference falls within the range of 400°C‒500°C.

Process parameters settings of friction stir welding using multi-response optimization for aluminum alloys

The defense and aerospace industries utilize the AA2050, an aluminum-lithium alloy of the third generation. Principal component analysis (PCA) and Taguchi-grey relational analysis (GRA) are utilized in this study to enhance the process constraints for friction stir welding (FSW). The quality of the weld can be influenced by various process factors such as the speed at which the weld is traversed, the speed at which the tool is rotated, the angle at which the tool is tilted, the diameter of the shoulder, and the shape of the tool pin. The combination of PCA and the well-known Taguchi-GRA approach enables the objective estimation of response weights. Here, a Taguchi L16 orthogonal array was designed, and sixteen tests were carried out using it. These experiments were conducted with various assessments including tensile strength, yield strength, elongation percentage, hardness of the weld zone, bending load, etc Through the utilization of Taguchi-GRA-PCA analysis, the most suitable process parameters were identified as a traverse speed (TS) of 160 mm min−1, a rotating speed (RS) of 900 rpm, a tilt angle (TA) of 2 degrees, a shoulder diameter (SD) of 16 mm, and with a straight square tool pin profile (TPP). ANOVA revealed the relevance of all five characteristics, with rotational speed being the most influential, accounting for 43.56% of the entire result. A confirmation experiment done under ideal conditions revealed a significant improvement in total weld quality of 19.06%.

Optimizing Process Parameters for Controlled Drug Delivery: A Quality by Design (QbD) Approach in Naltrexone Microspheres

Optimizing Process Parameters for Controlled Drug Delivery: A Quality by Design (QbD) Approach in Naltrexone Microspheres

Rapid accomplishment of cost-effective and macro-defect-free LPBF-processed Ti parts based on deep data augmentation

Machine learning methods can accurately predict the density of as-built parts by laser power bed fusion (LPBF), providing a reference for optimizing process parameters. However, obtaining massive training data via experiments is time-consuming and high-cost. Herein, a novel data augmentation method based on the Wasserstein generative adversarial network and regularization strategy (LC-WGAN) was developed to generate new training data for density prediction modelling. Four machine learning (ML) algorithms, support vector regression (SVR), multilayer perceptron (MLP), random forest (RF) and gradient boosting decision tree (GBDT), were adopted to construct the density prediction models. Results show that the proposed LC-WGAN effectively enhanced the prediction performance of all four models. R2 of MLP dramatically increased by 73.06 %, while that of RF and GBDT was marginally improved by 10.07 % and 7.48 %, respectively. RF trained by augmented dataset has the highest prediction performance on test set with MAE, RMSE and R2 of 0.7589, 0.9584 and 0.9822, respectively, which is suitable for density prediction. Additionally, SHAP analysis reveals that energy density is the most important parameter with an overall positive impact on density. The proposed method can provide valuable insights for the limited sample modelling in other fields.

Influence of acid hydrolysis on the degree of polymerization of cellulose from colored cotton substrates

This study investigated the influence of sulfuric acid hydrolysis on the cellulosic fibers and films’ degree of polymerization (DP). Cotton textile residues (dyed and white) and hydrophilic cotton were used as the raw material. The films were formed by the dissolution of fibers in ionic liquid and regeneration in water. Results showed that dyed cotton residues required hydrolysis at 0.5 M sulfuric acid for 30 min to achieve complete dissolution during viscosimetry assay. Due to different dyeing processes and chemical compositions, the color of the fibers influences the DP. Undyed samples submitted to hydrolysis had DP around 60% lower than samples without pretreatment. This effect was not observed when comparing films with and without hydrolysis, because the depolymerization occurs in the dissolution and regeneration step. This study expands the applicability of viscometry to colored substrates, serving as a starting point for future research on the impact of process parameters and dyes on cellulose polymerization.

Design space identification of a coupled two-stage batch reactor system

The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. It was originally developed for a single unit system. Many industrial processes frequently involve multiple unit operations. Assessing the interaction of Critical Process Parameters (CPPs) with Critical Quality Attributes (CQAs) across stages enables informed decision-making and the capacity to balance different requirements. Analysis and visualization of the complex, multi-dimensional DS is a challenging task. This paper presents a framework for identification such DSs, considering both joint and decoupled strategies using a detailed analysis of a two-stage batch reactor case study. We assess and discuss the practicality and relevance of these methods.

Effect of process parameters on plasma partial oxidation approach for removal of leaking LPG

In order to address the challenge of preventing explosions in the accidental disposal of leaking liquefied petroleum gas (LPG), a plasma partial oxidation (PPO) approach for converting the gas into liquid products is proposed for the first time, which eliminates the explosion risk in leaking LPG from the pathway of combustible gas removal. The effect of the initial C4H10 (Simulated LPG) concentration, discharge power, and residence time on the reaction performance were investigated, and the main reaction pathway was proposed. The residence time had the greatest effect on C4H10 conversion (27.6 – 78.0%) and yield of total liquid products (26.0 – 63.9%). The rise in discharge power substantially increased the C4H10 conversion (29.9 – 51.2%) and maintained a liquid product selectivity of more than 88.0%. When the initial C4H10 concentration is lower than 5%, the C4H10 concentration can reduce directly to 1.8% at a moderate discharge power (39 W) and residence time (0.81 s), which can eliminate the risk of explosion. This article provides a new approach for the disposal of LPG spills and offers new insights into the effects of main process parameters and their mechanisms for optimizing the PPO reaction performance of converting C4H10 into liquid products.

Preparation and characterization of composite-modified PA6 fiber for spectral heating and heat storage applications

Abstract Composite coating technology was used to prepare a modified polyamide 6 (PA6) polymer material masterbatch with low mutual interference and good spinnability using molybdenum oxide, tungsten trioxide, graphene oxide, etc., as modifiers. Experiment shows that using nanoscale composite-coated modified masterbatches capable of spectral heating and heat storage in the preparation of nylon 6 fiber, adding modified masterbatches with a mass ratio of 6% online, and controlling various process parameters can result in good functionality and usability as well as enable consistent production. Optimal production conditions are achieved by utilizing pre-dried PA6 chips with a moisture content of 420 ppm. The master batch is subjected to drying at a temperature of 95°C for 12 h. During this process, the master batch is introduced at an addition ratio of 6%. Subsequent spinning is conducted at a speed of 4,300 m·min−1 and a temperature of 255°C. Cooling is facilitated by air set at a temperature of 17°C, flowing at a speed of 0.45 m·min−1. In the production line, one roller operates without heating, while the second roller is maintained at a temperature range of 160°C. The stretching process is performed at a ratio of 1.3. To ensure proper lubrication, an oil rate of 1.3% is applied. These meticulously controlled process parameters collectively contribute to the most favorable production status.