Optimal Setting Of Process Parameters Research Articles

Improving Maraging Steel 350 Machinability via Wiper Insert-Enhanced Face Milling

Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

Process parameter optimization for laser powder directed energy deposition of Inconel 738LC

Processing nickel-based superalloys is one of the major challenges in the additive manufacturing (AM) field. Nickel-based superalloys consist of several alloying elements, which make them prone to the formation of undesired phases and cracking due to the complex thermodynamics during the metal AM processes. In order to produce defect-free parts from nickel-based superalloys via metal AM processes, optimization of process parameters is required. In this study, the effect of laser powder directed energy deposition (LP-DED) process parameters on the geometrical properties and porosity ratio of Inconel 738 LC (low carbon) is investigated. Geometrical specifications, including the track height, width, and wetting angle are the major factors that indicate the quality of the final product. The analysis of variance (ANOVA) and response surface methodology (RSM) were developed in order to predict the geometrical specifications of single tracks from the key process parameters, namely laser power, scan speed, and powder feed rate. An optimum set of process parameters was obtained through multi-objective optimization by minimizing the porosity ratio, crack formation and maximizing the deposition rate. The results were verified and a good agreement with the experimental measurements was achieved. The produced bulk sample showed less than one percent porosity content, which indicates the effectiveness of single-track optimization prior to bulk sample production. The overall microstructure of the bulk sample showed a high content of MC carbides resulting from the abundance of alloying elements.

A STUDY ON MULTI-OBJECTIVE OPTIMIZATION OF PULSED TIG WELDING PARAMETERS AND THEIR EFFECT ON JOINT PROPERTIES OF HIGH MANGANESE STEELS USING TAGUCHI-BASED GRA APPROACH

This investigation focuses on the study of the effect of process parameters like peak current ([Formula: see text], base current ([Formula: see text], pulse frequency ([Formula: see text], shielding gas flow rate ([Formula: see text] on mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and flexural strength (FS) of the welded joints during pulsed TIG welding of SAILMA 450 and EN14 B steels. Taguchi’s L[Formula: see text] orthogonal array has been used for conducting the tests. Multi-objective optimization has been performed using Grey relational analysis (GRA) in order to maximize the mechanical strength and to find out the optimal set of parameters. The optimum parametric combination is obtained at a peak current of 220[Formula: see text]Amps, base current of 120[Formula: see text]Amps, pulse frequency of 5[Formula: see text]Hz and shielding gas flow rate of 17[Formula: see text]l/min. Analysis of variance (ANOVA) is used to predict the significant process parameters. It has been observed from the ANOVA analysis that peak current and pulse frequency have more influence on the output responses than the shielding gas flow rate and base current. The results of the confirmatory test show an improvement of 0.5801 in the GRG, which is satisfactory. A microstructure study has been performed using scanning electron microscopy (SEM) for the optimal set of process parameters.

Predicting and Enhancing the Multiple Output Qualities in Curved Laser Cutting of Thin Electrical Steel Sheets Using an Artificial Intelligence Approach

This study focused on the efficacy of employing a pulsed fiber laser in the curved cutting of thin, non-oriented electrical steel sheets. Experiments were conducted in paraffinic oil by adjusting the input process parameters, including laser power, pulse frequency, cutting speed, and curvature radius. The multiple output quality metrics included kerf width, inner and outer heat-affected zones, and re-welded portions. Analyses of the Random Forest Method and Response Surface Method indicated that laser pulse frequency was the most important variable affecting the cut quality, followed by laser power, curvature radius, and cutting speed. To improve cut quality, an innovative artificial intelligence (AI) approach incorporating a deep neural network (DNN) model and a modified equilibrium optimizer (M-EO) was proposed. Initially, the DNN model established correlations between input parameters and cut quality aspects, followed by M-EO pinpointing optimal cut qualities. Such an approach successfully identified an optimal set of laser process parameters, even beyond the specified process window from the initial experiments on curved cuts, resulting in significant enhancements confirmed by validation experiments. A comparative analysis showcased the developed models’ superior performance over prior studies. Notably, while the models were initially developed based on the results from curved cuts, they proved adaptable and capable of yielding comparable outcomes for straight cuts as well.

A novel numerical modeling of microsecond laser beam percussion micro-drilling of Hastelloy X: experimental validation and multi-objective optimization

The paper investigates the characteristics of the laser beam percussion micro-drilling (LBPMD) process in aerospace nickel-based superalloy Hastelloy X using microsecond pulses. The quality of the drilled hole is crucial in laser beam micromachining, and selecting appropriate process parameters significantly impacts the hole’s quality. The objective is to achieve predefined hole dimensions with minimal taper angles. Additionally, the study focuses on the alteration of pulse width, which is a combination of laser pulse frequency and duty cycle. Laser power (P), duty cycle % (D), focal plane position (FPP), and laser frequency (f) are considered input parameters, while geometric features such as inlet and outlet diameters, hole taper angle, and inlet circularity are examined as process responses. ANOVA is employed to establish significant relationships between process parameters and response variations based on experimental tests. Creating a precise simulation model that accurately accounts for the moving boundary of the target material’s receding surface is a crucial and challenging task in formulating the laser heat conduction problem. It is necessary to simultaneously capture the material’s dynamic front movement and update the boundary conditions of the laser source. To model the micro-drilled hole with LBPMD, the UMESHMOTION and DFLUX subroutines, along with the arbitrary Lagrangian-Eulerian (ALE) adaptive remesh algorithm in the Abaqus™ software, are utilized. Notably, no previous numerical study has predicted the geometry of micro-drilled holes using this technique. The proposed procedure is validated through the predictions of inlet and outlet hole diameters. Special emphasis is placed on the validation of models. Consequently, the numerical model and statistical model are compared as well as the need to define model applicability. The study demonstrates that all input parameters significantly influence the inlet hole diameter, while the pulse width notably affects the taper angle and circularity. The interaction between high laser frequency and low duty cycle results in reduced pulse duration. Multi-objective optimization is performed to determine the optimal process parameter settings for desired quality characteristics, considering minimum hole taper angle, precise inlet diameter, and maximum inlet circularity of the hole as optimization criteria. The findings show that with the optimized predicted results obtained from the optimal input variables, a composite desirability of 92% can be achieved.

Tribological properties optimization of nylon6 composites filled with SiC and Mica

Nylon is expanding tremendously on a global scale. This paper deals with the optimization of wear behaviour of nylon-6 hybrid polymer matrix composites. The composites were prepared with Nylon 6 as the matrix and SiC and Mica as the reinforcements. Twin screw extruder and injection moulding was used to fabricate the composites. Experiments were conducted on Pin on Disc apparatus. Reinforcement %, Sliding distance, Sliding speed and Load are the parameters used for the study. Experiments were designed using Taguchi and optimization was performed using Grey Relational Analysis.L9 orthogonal array was used. It was observed that 7.5 wt% of Mica, Load 5 N, 500 m sliding distance and 6 m/s Sliding speed were the optimum process parameter settings. Using SEM micrographs, the wear mechanisms of the worn discs were examined. Additionally, it was noted that some abrasive wear and very little adhesive wear had occurred on the Nylon 6 composites.

Process parametric optimization of fused deposition modeling for manufacturing of acrylonitrile butadiene styrene parts

Additive manufacturing has been a revolution in the last decade and has led to a number of product innovations. Rapid prototyping (RP) has significantly reduced the product development cycle time and RP techniques are now replacing many conventional plastic processing techniques where production volume is not an issue. In order to ascertain the utility of additive manufacturing techniques for the development of fully functional parts, it’s important to establish the optimal set of process parameters for achieving maximum mechanical performance. In the current experimental investigation, the dogbone-shaped parts of acrylonitrile butadiene styrene (ABS) were fabricated by the fused deposition modeling (FDM) process. The process parameters were optimized for dogbone-shaped specimens fabricated by the FDM process. Among the parameters, it has been found that orientation and infill density are the most dominant factors affecting the tensile strength of FDM parts printed with ABS material.

A novel hybrid multi-criteria optimization of 3D printing process using grey relational analysis (GRA) coupled with principal component analysis (PCA)

Fused deposition modeling (FDM) is renowned as a prominent approach in the realm of 3D printing, where objects are built layer by layer using a heated nozzle to extrude melted materials. This research was conducted to identify the most effective FDM process variables to enhance tensile strength while simultaneously reducing surface roughness. Polylactic Acid (PLA) was chosen to fabricate test samples, showcasing the applications of 3D printing. In the course of this research, we conducted a series of 27 experiments to investigate the fundamental relationship between the parameters and the corresponding responses. The central aim of this study lies in optimizing the input variables viz. printing speed, layer thickness, and carbon deposition (C-deposition) for the technological manufacturing process of embossing parts in the context of Industry 4.0. To enhance both tensile strength and surface roughness simultaneously, a new hybrid method has been suggested. This approach integrates grey relational analysis (GRA) with principal component analysis (PCA) to determine the optimal combination of process parameters in the 3D printing process. Notably, the experiment trial exhibited the highest grey relational grade (GRG), indicating optimal process parameter settings at a printing speed of 100 mm s−1, layer thickness of 0.1 mm, and C-deposition of 15 mg respectively. Additionally, mathematical models are created through response surface methodology to explore the impact of FDM parameters on the grey relational grade. The findings from this study can be utilized in various industries and applications where FDM 3D printing is employed.

Statistical-experimental modeling of the effect of process parameters on geometric characteristics of laser cladding of stellite 6 on SS316 using second-order regression

Applications of 316 stainless steel can be developed by laser cladding of the Co-based superalloys, which has multiple enhancement properties and creates a small heat affected zone. Quantitive investigation of the effect of process parameters on geometrical features and providing reliable models to achieve the desirable metallurgical bond, minimal structural defects, and predicting the dimensions of the geometry without conducting excessive experimental tests are an important issue. This study evaluate the effects of the main process parameters of laser cladding on geometric characteristics of stellite 6 using the response surface methodology. A quadratic model was developed for each of the geometric characteristics. Laser power, scanning speed, and powder feed rate ranged between 400 and 600 W, 6–10 mm/s and 12–20 gr/min, respectively, were considered as the input variables and height, width, penetration depth and dilution were considered as responses. The experiments were designed using the box-behnken method. The accuracy and fitness of the models were evaluated using analysis of variance method. The results indicated the R2 factors obtained for height, width, penetration depth and dilution are equal to 0.89, 0.98, 0.98 and 0.96 respectively. After obtaining the desirability factor of 0.795, the optimal set of process parameters were selected as laser power of 510 W, scanning speed of 10 mm/s and powder feed rate of 13 gr/min. Verification of the optimization was done by performing experimental test,which maximum 13 % difference between actual values and the predicted values was obtained.

Optimization of Process Parameters to Minimize the Surface Roughness of Abrasive Water Jet Machined Jute/Epoxy Composites for Different Fiber Inclinations

Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

PurposeThis paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.Design/methodology/approachIn order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.FindingsFinally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.Practical implicationsBased on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.Originality/valueEven though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

Optimizing process parameters of fused deposition molding for 3D printing customized control of polyetheretherketone scaffolds

Polyetheretherketone (PEEK), with good biocompatibility and similar mechanical properties to natural bone, is extensively employed in the manufacture of prostheses. However, the precision and mechanical properties of current implants are major challenges for clinical applications. In this study, the effect of pore size, raster angle and printing temperature were investigated on length, width, thickness, material consumption, compressive strength and Young’s modulus. Taguchi design of experiment method was used to reduce the number of experiments and optimize the printing process parameters. Finally, predictive analysis was exploited to give the optimal set of process parameters. Experimental results indicated that the approach applied in this work provided more accurate predictions and control of the response variables. The maximum compressive strength and compressive modulus of PEEK scaffolds reached 43.4 MPa and 253.3 MPa, respectively. Therefore, the methodology of present work has the potential to meet the demand of design precision and manufacture of customized bone substitutes.

Research on the integral forming process of thin walled and thick mouth seamless gas cylinders

There is a considerable difference in wall thickness between the mouth and the cavity of thin-walled and thick-mouthed seamless gas cylinders, and the existing manufacturing processes are unable to effectively meet product requirements. To overcome such issue, a step-by-step boring-necking-spinning solution for gas cylinders was proposed, in which sufficient wall thickness is reserved for the mouth area of the cylinder blank, followed by necking-spinning to realize the overall forming of thin-walled, thick-mouthed seamless gas cylinders. The stress–strain distribution and geometric dimensional changes of gas cylinders during the spinning process were investigated by means of finite element simulation, and the effects of different process parameters on the stress and wall thickness of the bottle mouth were analyzed. Further, multi-objective optimization of the response surface model was performed using the NSGA-II algorithm to derive a set of optimal process parameters. Finally, the correctness of the simulation and optimization results was verified experimentally, and the expected geometry and optimal strain state of the gas cylinder were obtained. The newly developed processing solution represents a groundbreaking advancement in the manufacturing of thin-walled and thick-mouthed gas cylinders.

Machine learning-based multi-objective parameter optimization for indium electrorefining

A novel approach utilizing support vector regression algorithm (SVR) is presented for developing forecast models of Cu and Pb concentrations in indium electrolysis products. These models are based on a subset of process parameters and purity data. The optimization of Cu and Pb content is achieved through the integration of the forecast models with a multi-objective genetic algorithm. Consequently, a set of optimal electrolysis process parameters is identified for the electrolytic refining of high-purity indium. The determined optimal parameters are as follows: In3+ concentration of 80–90 g·L-1, NaCl concentration of 85–120 g·L-1, gelatin concentration of 0.5–0.6 g·L-1, current density of 65–70 A·m−2, pH value of 2.5, and pole pitch of 40–60 mm. To validate the effectiveness of these optimized parameters, experimental tests are conducted to confirm that the Cu and Pb contents conform to the national standard for 5 N indium. By employing this innovative approach, the study not only provides insights into the forecast modeling of Cu and Pb concentrations in indium electrolysis products but also contributes to the advancement of the electrolytic refining process for achieving high-purity indium.

A study of the particle-reinforced bonded joint performance

In this study, a systematic study has been performed to explore the influence of the adhesive particle fillers on the mechanical performance of bonded joints. Three-dimensional finite element models were established to analyse the strength of bonded joints. Based on response surface methodology, experiments were designed with three parameters and three levels. Experimental results were applied to develop response surface models between parameters (adhesive layer thickness, particle size, and particle volume fraction) and response values (shear strength and peel strength). Response surface models incorporating genetic algorithms were applied to find the optimal set of input parameters. These findings indicated that the interaction between volume fraction and adhesive layer thickness has the most significant influence on the shear strength of joints, and the interaction between particle size and adhesive layer thickness has a major effect on the peel strength of joints. The optimal set of process parameters is: adhesive layer thickness 0.54 mm, particle size 69 µm, and volume fraction 4.0%.

Multi-objective robust parameter optimization using the extended and weighted k-means (EWK-means) clustering in laser powder bed fusion (LPBF)

Metal additive manufacturing (AM) technology, especially laser powder bed fusion (LPBF), has received abundant interest from industries and the research community. Process optimization methods have thus multiplied to improve the overall quality of the final parts. However, little attention has been given to the quality repeatability issue. This paper proposes a novel multi-objective robust parameter optimization framework to explore optimal process parameters with respect to relative density and dimensional accuracy of LPBF-fabricated parts. Specifically, a modified k-means clustering, named the Extended and Weighted K-means (EWK-means), was constructed to simultaneously optimize the mean and the variance of the multiple responses. Experiments were conducted to verify the effectiveness of the proposed optimization framework. In addition, the effects of the process parameters, environment-related parameters, and physical properties on the hardness of the parts were analyzed using several machine learning models. The results showed that the proposed method achieved a set of optimal process parameters with better quality and satisfactory variability in the printed parts compared with other robust parameter optimization methods.

Dimensional Accuracy of 3D Printed Dog-bone Tensile Samples: A Case Study

Three-dimensional (3D) printing technology has revolutionized manufacturing by enabling the rapid production of complex objects. However, ensuring dimensional accuracy in 3D printed parts remains a significant challenge due to various factors, including the selection of appropriate parameters during the Fused Deposition Modeling (FDM) process. Achieving dimensional accuracy is crucial in determining the reliability of a printing machine to produce objects that meet the expected results. This study aims to investigate the influence of FDM parameters (filling percentage and layer thickness) on the final dimensions of 3D printed parts made from polylactic acid (PLA) through a systematic experimental and statistical approach. The goal is to identify the optimal process parameter settings that minimize the error percentage in the dimensions of the printed parts using the Taguchi method. Overall higher dimensional accuracy was obtained, influenced mainly by the layer thickness parameter (in the case of Y direction dimensions) and by the filling percentage (in the case of Z direction dimensions – corresponding to sample thickness). The findings of this study provide valuable insight into identifying the optimal configuration for producing PLA 3D-printed components.

Influence of welding environment on joint characteristics of friction stir lap welded AA7475-PPS polymer hybrid joints

This study explores the impact of air and underwater welding environments during friction stir lap welding (FSLW) of AA7475 and Polyphenylene-sulfide (PPS) polymer dissimilar materials. Response surface methodology-based desirability function approach was used to identify the optimal FSLW process parameter set in an underwater environment. The joint characteristics, such as microstructure, mechanical properties of the joints, and physicochemical changes in the polymer with the air and underwater FSLW, were compared. The maximum tensile lap shear strength was attained as 12 MPa and 19 MPa in the air and underwater weld environment, respectively. Microstructural examination revealed that the bonding mechanism in FSLWed hybrid joint in both weld environment conditions is macro/micromechanical interlocking. Based on the findings of a Differential Scanning Calorimetry study, the FSLW technique causes the loss of crystallinity in the polymer. The study found that the welding environment significantly impacted air voids formation, tensile characteristics, and loss of crystallinity in polymers.

Research on warping optimization of automobile mesh based on artificial intelligence algorithm

Automobile mesh is a typical large plastic part prone to warping deformation in the manufacturing process, which seriously affects the quality of products. The traditional method is to find the optimal combination parameters to reduce warping deformation in the forming process through repeated trial or mold flow analysis with high cost. In this study, An optimization method based on an artificial intelligence algorithm is proposed to optimize the warping deformation of the automobile mesh. In the optimization method, artificial intelligence regression algorithm-support vector machine regression was used to establish the nonlinear function relationship (prediction function) between the molding process parameters and the warpage of automobile mesh, and the performance of the prediction function was optimized by a genetic algorithm. The optimized prediction function was taken as the fitness function, the warping value was regarded as the fitness value, and genetic algorithm is used again to obtain a set of optimal process parameters and minimum warpage values. Finally, the reliability of the optimization method is verified by the modal flow analysis software.

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

The friction stir spot welding (FSSW) process is a novel technique that overcomes the limitation of resistance spot welding. Recently, FSSW used for welding of polymers which are difficult to be joined by traditional welding processes. The demand for Acrylonitrile Butadiene Styrene (ABS) for industrial applications has increased in recent years. However, to employ this technique the challenge is to get optimal FSSW parameters setting to achieve the best weld strength during the welding of ABS sheets. To achieve this, in the present work, full factorial experimental design layout was employed to investigate the effect of process parameters on weld strength i.e., ultimate tensile strength (UTS) and percentage elongation during FSSW of ABS-ABS sheet in butt configuration. To predict the UTS and percentage elongation, machine learning regression namely, linear, polynomial, support vector machine, and decision tree was used. Further, the study includes the identification of the fracture patterns post tensile test specimens based on the topography of the fracture surface under scanning electron microscopy. It was found that plunge depth is the most significant parameter followed by spindle speed and dwell time. The optimal setting of process parameters i.e., spindle speed of 1000 rpm, plunge depth of 1 mm, and dwell time of 40 s resulted in maximum UTS of 7.849 MPa. The maximum value of percentage elongation obtained was 5 at the parameter setting of spindle speed of 1000 rpm, plunge depth of 0.8 mm, and dwell time of 40 s. Polynomial regression outperformed in the prediction of UTS and percentage elongation with an R-square of 0.99.