Selection Of Suitable Parameters Research Articles

Statistical modeling and optimization of process parameters for additive manufacturing of styrene–ethylene–butylene–styrene block copolymer parts using solvent cast 3D printing technique

AbstractAdditive manufacturing of thermoplastic elastomers is challenging using fused deposition modeling due to their high melt viscosity, low column strength, and poor fusion among layers. Solvent‐cast 3D printing (SC‐3DP) is an efficient alternative to successfully 3D print such materials. However, selection of suitable 3D printing parameters is crucial to realize parts with optimum physicomechanical properties. In this work, statistical modeling and SC‐3DP parameter optimization for styrene–ethylene–butylene–styrene (SEBS) block copolymer were performed. The effect of 3D printing process parameters on shrinkage, relative density, and tensile strength was analyzed using response surface methodology. Experiments were planned as per central composite design and analysis of variance was performed to evaluate the significant parameters. SEBS content in the polymer solution significantly affected the shrinkage of SC‐3DP samples. Moreover, relative density and tensile strength were significantly affected by print speed and layer height. A significant interaction between print speed and layer height was also noticed for tensile strength and relative density of printed samples. Multi‐objective optimization using genetic algorithm was also performed to minimize shrinkage and maximize relative density and tensile strength. Finally, a case study was conducted comparing the physicomechanical properties of SC‐3DP samples printed at optimized process parameters and compression molded samples.Highlights Statistical models were developed using response surface methodology. Genetic algorithm based multi‐objective optimization was performed. Optimum solvent‐cast 3D printing (SC‐3DP) process parameters were determined.

Repercussion of hybridization on AWJM surface quality of cotton and corn hybrid composite

ABSTRACT Hybrid composites made of natural fibers are replacing composites made of synthetic fibers, which is better for the environment. In this study, a Cotton/Corn Hybrid Composite was prepared using compression moulding and evaluated the Abrasive Water Jet Machining (AWJM) process variables on surface quality. The AWJM investigations were conducted using experimental design approaches, with variables including water pressure, nozzle transfer speed, and standoff distance. Among these variables, it was determined that nozzle transfer speed had the most influential impact on surface quality. The optimal machining parameters P2, s1, and d1 were identified, resulting in a surface roughness of 4.459 μm. This demonstrates that accomplishing better surface quality is attainable through the suitable parameter selection. Further, the prediction model’s efficiency was validated by establishing a strong relationship between the experimental, predicted, and validated surface roughness values. This highlights the model’s reliability in predicting and optimizing surface quality in the AWJM process.

Minimum entropy constrained cooperative inversion of electrical resistivity, seismic and magnetic data

Geophysical methods are widely used to gather information about the subsurface as they are non-intrusive and comparably cheap. However, the solution to the geophysical inverse problem is inherently non-unique, which introduces considerable uncertainties. As a partial remedy to this problem, independently acquired geophysical data sets can be jointly inverted to reduce ambiguities in the resulting multi-physical subsurface images. A novel cooperative inversion approach with joint minimum entropy constraints is used to create more consistent multi-physical images with sharper boundaries with respect to the single-method inversions. Here, this approach is implemented in an open-source software and its applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and magnetic data is investigated. A synthetic 2D ERT and SRT data study is used to demonstrate the approach and to investigate the influence of the governing parameters. The findings showcase the advantage of the joint minimum entropy (JME) stabilizer over separate, conventional smoothness-constrained inversions. The method is then used to analyze field data from Rockeskyller Kopf, Germany. 3D ERT and magnetic data are combined and the results confirm the expected volcanic diatreme structure with improved details. The multi-physical images of both methods are consistent in some regions, as similar boundaries are produced in the resulting models. Because of its sensitivity to hydrologic conditions in the subsurface, observations suggest that the ERT method senses different structures than the magnetic method. These structures in the ERT result do not seem to be enforced on the magnetic susceptibility distribution, showcasing the flexibility of the approach. Both investigations outline the importance of a suitable parameter and reference model selection for the performance of the approach and suggest careful parameter tests prior to the joint inversion. With proper settings, the JME inversion is a promising tool for geophysical imaging, however, this work also identifies some objectives for future studies and additional research to explore and optimize the method.

Modelling and optimization method for energy saving of computer numerical control machine tools under operating condition

The widespread application of machine tools in industry results in very high energy consumption. In order to save energy, it is an effective means to reduce the processing power while improving the processing efficiency of machine tools. Since milling parameters directly affect machine performance, the selection of suitable milling parameters is particularly important. In this regard, this study proposes an integrated modelling and optimization method for energy saving of computer numerical control (CNC) machine tools under operating condition. Firstly, the operating condition of the CNC machine are analyzed and the processing power and efficiency mathematical models are established based on the four milling parameters (n, fz, ae, ap). Subsequently, the Pareto optimal solution is introduced to establish an optimization model integrating the processing power and time. To solve the optimization problem, an adaptive chaotic multi-objective chimp algorithm with fast convergence and satisfactory optimization is proposed considering three aspects: search space, convergence factor, and chaotic mapping. Afterwards, further degrees of freedom and sensitivity analyses were carried out on the constructed model using the Sobol method. Finally, comparative analysis and validation are conducted through simulation cases and actual processing experiments, respectively. The results show that both validation scenarios have the same conclusion. Compared with three existing algorithms, the proposed method reduces the processing power by 5.63 %, 4.65 % and 4.51 % and improves the processing efficiency by 27.88 %, 15.11 % and 15.31 %, respectively. Based on the above enhancements, the energy consumption is reduced by 58.21 %, 45.54 % and 41.45 % respectively.

A Study on Micro-Pit Texture Parameter Optimization and Its Tribological Properties

In this paper, the effect of micro-dimple textures (produced by a laser) on the tribological properties of bearings is investigated. This study offers guidelines to reduce the friction torque of the bearing pair and addresses the problem of difficult start-ups after shutdowns. Micro-pits with different texture diameters and depths were machined on the surface of journal bearings. Then, the impact of several different texture parameters on the tribological performance of the bearing pairs was studied using an orthogonal experimental design. Subsequently, the surface morphology of the bearings before and after the friction and wear test was observed using scanning electron microscopy (SEM) and energy-dispersive spectrometry (EDS). These observations were then used to determine the type/state of friction and wear, which also improves our understanding of how texture affects the service life of bearings. The results indicate that the bearings’ micro-pit surface hardness follows an approximate parabolic spatial distribution that decreases along the micro-pit wall. Furthermore, the laser processing of surface textures was found to cause hardening in certain areas, and the chemical composition of elemental carbon and oxygen at the inner surface of processed bearings increased by 31.1% and 7.9%, respectively. Moreover, abrasive wear was identified as the primary form of wear. The textured surface’s antifriction mechanism primarily functioned to trap particles, which acted as a secondary lubrication source and altered the lubrication states by serving as a medium for supplied lubricants. The results confirm that a suitable selection of texture parameters can not only effectively reduce the friction coefficient without shortening the service life of the bearing pair but also facilitate the smooth start-up of the rotor–bearing system.

Avoiding lateral mode leakage in thin film lithium niobate waveguides for the generation of spectrally pure photons at telecom wavelengths

Photonic integrated optical components, notably straight waveguides, serve as pivotal elements for on-chip generation and manipulation of quantum states of light. In this work, we focus on optimizing waveguides based on lithium niobate on insulator (LNOI) to generate photon pairs at telecom wavelengths using spontaneous parametric down-conversion (SPDC). Specifically, we investigate lateral leakage for all possible SPDC processes involving type 0, type I, and type II phase matching conditions in an X-cut lithium niobate waveguide and provide a recipe to avoid leakage loss for the interacting photons. Furthermore, focusing on type II phase matching, we engineer the waveguide in the single-mode regime such that it also satisfies group index matching for generating spectrally pure single photons with high purity (99.33%). We also address fabrication imperfections of the optimized design and find that the spectral purity of the generated photons is robust to fabrication errors. This work offers guidance for the suitable selection of morphological parameters to obtain lossless, single-mode LNOI waveguides for building linear optical circuits and photon pair generation at telecom wavelengths using desired phase-matching conditions.

Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data.

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.

Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete

Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and prediction of the ultimate compressive strength of FRP-confined concrete. The modeling process was established using a dataset from open-source literature consisting of 490 circular columns. Three well-known artificial intelligence (AI) models, the multivariate adaptive regression spline (MARS), extreme learning machine (ELM), and RANdom Forest GEnRator (Ranger), were used to validate the proposed model. The results demonstrated the effectiveness of the XGBoost algorithm in the modeling process, selection of suitable parameters, and enhancement of the prediction accuracy. The algorithm achieved excellent prediction results for all input combinations with a coefficient of determination (R2) greater than 0.9, and the best performance is gained by using five input parameters with (R2 = 0.955), mean absolute percentage error (MAPE = 0.130), and root mean square error (RMSE = 0.572). The study revealed the flexibility and efficiency of capturing the nonlinear behavior of complex FRP-confined concrete using the proposed model.

Design and simulation of solid‐core octagonal photonic crystal fiber for terahertz wave propagation

AbstractA solid‐core (SC) octagonal photonic crystal fiber (O‐PCF) designed for operation in the terahertz regime aims to enhance its performance. The effective material loss, confinement loss, effective mode area, and nonlinear coefficient have been investigated and compared for two different media: silica glass and Teflon. The terahertz frequency range from 0.7 to 3.0 THz has been utilized to analyze all the results using a full‐vectorial finite element method, with COMSOL Multiphysics software employed for the simulations. With a suitable selection of parameters, the proposed SC O‐PCF demonstrates the potential to minimize losses and increase the effective mode area of the fundamental mode.

Shape memory behavior of 4D printed CF/PEKK high temperature composite under subsequent thermomechanical cycles

Shape memory effect (SME) in high temperature polymers (HTPs) has great significance in space for making self-deployable structures, needing high strength and stiffness during operation, signifying the necessity of exploring HTP composite with SME. This work investigates the SME of 4D printed carbon fiber/polyether ketone ketone (CF/PEKK) HTP composite under subsequent thermomechanical cycles for the first time. Scanning electron microscopy (SEM) revealed a uniform distribution of CFs in the developed composite, the extruded composite filament, and the composite prints with seamless raster and layer deposition, confirming a suitable selection of processing parameters. The CF/PEKK composite prints exhibited an excellent shape fixity, Rf = 90 % and 91 %, and shape recovery, Rr = 96 % and 89 % in the first and the tenth cycle, respectively. A significant loss of 7 % in Rr is observed in the tenth cycle. Glass transition (Tg) and degradation temperature (Td) of the CF/PEKK composite prints are observed to be 162 °C and 568 °C, while storage modulus is found 157.69 % and 38.69 % higher than the existing PEKK and PEEK, respectively. This study revealed an excellent SME of 4D printed CF/PEKK composite with outstanding Tg and Td, showing great potential for space applications.

Investigating boosting techniques’ efficacy in feature selection: A comparative analysis

Accurate Solar Irradiance (SI) forecasting is an important aspect of solar energy harvesting and it depends on various meteorological features. Numerous feature selection algorithms have been implemented for the selection of suitable meteorological parameters. However, boosting algorithms are not explored widely for feature selection applications. Therefore, in this study, a novel perspective is introduced by exploring the efficacy of boosting algorithms in feature selection applications. In the proposed study, we perform a comparative analysis of different boosting algorithms for feature selection applications including Extreme Gradient Boosting (XgBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Light Gradient Boosting Machine (LGBM). The novelty of this approach is in utilizing these boosting techniques for the selection of the most appropriate features that improve the predictive performance of the model. The SI data of three different geographical locations: Islamabad, Pakistan, Basel, Switzerland and Golden, Colorado, USA are attained from the National Solar Radiation Database (NSRDB) and used in the proposed study. First, the appropriate features are selected by four boosting algorithms separately. The selected features are then fed to the Bidirectional Long-Short-Term Memory (BiLSTM) network for forecasting hour-ahead Global Horizontal Irradiance (GHI). The Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators. Findings demonstrate that the BiLSTM network trained on selected features, proposed by the XgBoost model, produces better forecasting results. In the case of the Islamabad city dataset, the RMSE and MAE of BiLSTM trained with appropriate features, as compared to the conventional model, are improved by 29.92% and 14.03%, respectively. For the dataset of Basel, the RMSE and MAE of BiLSTM network improved by 14.43% and 28.72%, respectively. Moreover, for the Golden city dataset, the RMSE and MAE of the proposed approach are improved by 10.5% and 17.38%, respectively than the conventional model.

Potency assay to predict the anti-inflammatory capacity of a cell therapy product for macrophage-driven diseases: overcoming the challenges of assay development and validation

BackgroundGiven the high level of product complexity and limited regulatory guidance, designing and implementing appropriate potency assays is often the most challenging part of establishing a quality control testing matrix for a cell-based medicinal product. Among the most elusive tasks are the selection of suitable read-out parameters, the development of assay designs that most closely model the pathophysiological conditions, and the validation of the methods. Here we describe these challenges and how they were addressed in developing an assay that measures the anti-inflammatory potency of mesenchymal stromal cells (MSCs) in an M1 macrophage-dominated inflammatory environment. MethodsAn in vitro inflammation model was established by coculturing skin-derived ABCB5+ MSCs with THP-1 monocyte-derived M1-polarized macrophages. Readout was the amount of interleukin 1 receptor antagonist (IL-1RA) secreted by the MSCs in the coculture, measured by an enzyme-linked immunosorbent assay. ResultsIL-1RA was quantified with guideline-concordant selectivity, accuracy and precision over a relevant concentration range. Consistent induction of the macrophage markers CD36 and CD80 indicated successful macrophage differentiation and M1 polarization of THP-1 cells, which was functionally confirmed by release of proinflammatory tumor necrosis factor α. Testing a wide range of MSC/macrophage ratios revealed the optimal ratio for near-maximal stimulation of MSCs to secrete IL-1RA, providing absolute maximum levels per individual MSC that can be used for future comparison with clinical efficacy. Batch release testing of 71 consecutively manufactured MSC batches showed a low overall failure rate and a high comparability between donors. ConclusionsWe describe the systematic development and validation of a therapeutically relevant, straightforward, robust and reproducible potency assay to measure the immunomodulatory capacity of MSCs in M1 macrophage-driven inflammation. The insights into the challenges and how they were addressed may also be helpful to developers of potency assays related to other cellular functions and clinical indications.

DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.

Pattern Recognition with Temperature Regulation: A Single YSZ-Based Mixed Potential Sensor Classifies Multiple Mixtures of Isoprene, n-Propanol, and Acetone.

Gas sensors integrated with machine learning algorithms have aroused keen interest in pattern recognition, which ameliorates the drawback of poor selectivity on a sensor. Among various kinds of gas sensors, the yttria-stabilized zirconia (YSZ)-based mixed potential-type sensor possesses advantages of low cost, simple structure, high sensitivity, and superior stability. However, as the number of sensors increases, the increased power consumption and more complicated integration technology may impede their extensive application. Herein, we focus on the development of a single YSZ-based mixed potential sensor from sensing material to machine learning for effective detection and discrimination of unary, binary, and ternary gas mixtures. The sensor that is sensitive to isoprene, n-propanol, and acetone is manufactured with the MgSb2O6 sensing electrode prepared by a simple sol-gel method. Unique response patterns for specific gas mixtures could be generated with temperature regulation. We chose seven algorithm models to be separately trained for discrimination. In order to realize more accurate discrimination, we further discuss the selection of suitable feature parameters and its reasons. With temperature regulation coefficients which are easily available as feature input to model, a single sensor is verified to achieve elevated accuracy rates of 95 and 99% for the discrimination of seven gases (three unary gases, three binary gas mixtures, and one ternary gas mixture) and redefined six gas mixtures. This article provides a potential new approach via a mixed potential sensor instead of a sensor array that could provide a wide application prospect in the field of electronic nose and artificial olfaction.

Analysis and application research on key issues of channel coding

Channel coding plays a crucial role in enhancing the reliability and efficiency of communication systems, particularly when transmission channels are disrupted by noise and interference. This paper presents an in-depth review of various channel coding techniques, their applications, and future research directions. Key topics discussed include prevalent channel coding methods, such as repetition codes, convolutional codes, LDPC codes, turbo codes, and polar codes. The paper also delves into the selection of suitable channel coding parameters and their applications in digital TV, mobile and satellite communications, unmanned aerial vehicle data links, speech communication, and underwater acoustic channels. Moreover, the paper explores the performance analysis and comparison of different channel coding techniques, shedding light on their strengths and weaknesses. Lastly, the paper identifies emerging trends and challenges in channel coding research, providing valuable insights for researchers and practitioners in the field of communication systems. By examining these techniques and future directions, this comprehensive overview aims to contribute to the development of more robust and efficient channel coding schemes for a wide range of communication applications.

A 3D cellular automaton with inhomogeneous nucleation for simulating dynamic recrystallization of low-alloy steel with mixed-grain microstructure

Cellular automaton (CA) was widely applied in predicting the dynamic recrystallization (DRX) behavior of metals. However, the current CA models cannot accurately simulate the DRX evolution of mixed-grain microstructure. In this work, the limitations of traditional CA in simulating DRX of mixed-grain microstructure of low-alloy steel were analyzed, and the inhomogeneous nucleation based on the relationship between nucleation probability and dislocation amounts of grains was used for the first time to overcome this challenge. The initial microstructure was defined based on average grain sizes and grain size distributions of the sample. Isothermal compression experiments were conducted at different temperatures with different strain rates to validate the models’ reliability. The APRGE software was employed to investigate the most suitable orientation relationship (OR) for martensitic transformation and revealed that the Greninger-Troiano (GT) OR was the best one. The reconstructed maps from EBSD data showed that the simulation results based on inhomogeneous nucleation matched experiments better as the boundaries of coarse grains tended to act as preferential nucleation sites. What is more important, the difference in average grain sizes on different sections made it inappropriate for mixed-grain microstructure to choose deformation parameters based on 2D, which exactly reflected the advantages of 3D CA. According to experiments and 3D simulations, the initial mixed-grain microstructure became uniform during hot deformation when the natural logarithm of the Zener–Hollomon parameter was 34.59 or less, indicating that the models could help the selection of suitable deformation parameters for mixed-grain microstructure.

Remote sensing delineation of wildfire spatial extents and post-fire recovery along a semi-arid climate gradient

Understanding the wildfire extent and post-fire vegetation recovery is critical for fire and forest management. Remote sensing imagery is widely used in wildfire detection because it provides continuous and large-scale surface monitoring capability. In this study, we apply and evaluate the performance of the LandTrendr algorithm in wildfire detection across a semi-arid climate region with a marked precipitation gradient. The aims are to compare the performance of four spectral indices for the burned areas and post-fire recovery detection for different climate conditions and investigate the relationship between suitable model parameters and climate conditions. The results show that NDVI outperforms other indices, including NBR, in burned area detection for drier areas (annual precipitation <400 mm). Disturbance signal-to-noise ratio can serve as an indicator for suitable index selection for semi-arid areas. Although the performance in the detection of burned pixels varies among different indices, they are all suitable for delineating post-fire recovery except for the wet site (annual precipitation of 575 mm) where NBR displays the best performance. Parameter optimization results along the climate gradient show that climate conditions have a significant impact on suitable parameter selection. These findings provide guidance on vegetation wildfire detection in arid and semi-arid climates to support wildfire risk and forestry management.

Forest Sound Event Detection with Convolutional Recurrent Neural Network-Long Short-Term Memory

Sound event detection tackles an audio environment's complex sound analysis and recognition problem. The process involves localizing and classifying sounds mainly to estimate the start point and end points of the separate sounds and describe each sound. Sound event detection capability relies on the type of sound. Although detecting sequences of distinct temporal sounds is straightforward, the situation becomes complex when the sound is multiple overlapping of much single audio. This situation usually occurs in the forest environment. Therefore, this aim of the paper is to propose a Convolution Recurrent Neural Network-Long Short-Term Memory algorithm to detect an audio signature of intruders in the forest environment. The audio is extracted in the Mel-frequency cepstrum coefficient and fed into the algorithm as an input. Six sound categories are chainsaw, machete, car, hatchet, ambiance, and bike. They were tested using several epochs, batch size, and filter of the layer in the model. The proposed model can achieve an accuracy of 98.52 percent in detecting the audio signature with a suitable parameter selection. In the future, additional types of audio signatures of intruders and further aspects of evaluation can be added to make the algorithm better at detecting intruders in the forest environment.

Investigation on thermal behavior of TBM disc cutter under different cooling conditions

The excessive working temperature of the TBM disc cutter is one of the problems that cannot be ignored in tunnel construction. The high temperature will accelerate the disc cutter ring wear, shorten its service life and increase construction costs. Predicting its working temperature to guide the selection of suitable excavation parameters and provide cooling methods suggestions under different geological conditions can extend the excavation distance and improve efficiency. In this study, the finite element model was used to study the thermal behavior of the disc cutter from the two aspects of heating and heat dissipation. The simulation results showed that the temperature change with the excavation parameters. The maximum temperature of the disc cutter under water cooling is 81.0% lower than without cooling. The results of the orthogonal analysis showed that the cutting depth and cutterhead rotation speed had the most significant effect on temperature. According to the experiment of the similar cutter, the maximum difference between the simulation and the experiment is 18.5%. The accuracy of the simulation results was verified.

A RETROSPECTIVE ANALYSIS ON DRILLING OPERATION AND ITS PARAMETERS: A CRITICAL REVIEW

Primary manufacturing processes like casting, forming, and shaping (forging, rolling, drawing, extrusion, sheet forming, and molding) further need any of the secondary manufacturing processes like turning, drilling, boring, planing, milling, grinding, etc. In order to produce superior quality products, and to enhance productivity, the selection of desirable process parameters is significant. The selection of suitable process parameters is essential for accomplishing the desired component. Based on the existing literature, this study examines the causes, effects, and variances regarding chip formation, tool geometry, thrust force, torque, surface roughness, drilling time, and other drilling quality characteristics in the most typical machining operations such as drilling. Developing a repository on these process parameters will guide the process planning engineer for ready reckon. Therefore, this work aims at the development of a detailed repository with the study of characteristics. Further, this literature review comprehends the characteristics of a behavior with its reasoning, which was detailed in the past decade. It reveals the beneficial process parameters for achieving better production rate and superior quality.