Process Parameters Research Articles

Inner Modulation Controlled Process for Suppression of Chatter Vibration in Double Inserts Turning

A novel cutting manner is presented to control regenerative chatter vibration in double inserts cutting, in which the forward and the backward inserts cut workpiece simultaneously with phase difference in the modulation of finished surface. In double inserts cutting, the forward insert is clamped above the backward insert. Both the inserts are positioned symmetrically with a height offset with respect to the workpiece rotation center. The forward insert mainly removes the material; while the backward insert cuts a part of the inner modulation to lose the regular excitation. The exciting force is also reduced with the cutting thickness of the forward insert after a workpiece revolution. Because the theoretical position offset of the inserts in the feed direction is small, the side cutting edges of inserts are aligned in the same position. The process parameters are determined by estimating the removal volume of the backward insert with the phase shift. The range of the cutting speed and the height offset are given by the frequency of the chatter vibration, which is nearly the same as the natural frequency of the workpiece in single degree of freedom. The proposed cutting manner is validated using comparison between the single insert and double inserts cuttings.

Mechanism for Control of Laser‐Induced Stainless Steel Oxidation

AbstractThis paper presents the development of a model for Nd:YAG laser surface oxidation of 316L stainless steel. A full factorial design of experiments (DOE) are implemented to produce a model for the prediction of elemental composition and color. The elemental composition of the films produced where measured using an X‐ray photoelectron spectroscopic (XPS) and the color is measured using optical reflectance spectroscopy. Oxide layers produced have a Cr/Fe ratio in the range of 0.13–2.09, and highly controllable color variation via single‐pass laser process. The effect of the process parameters used such as power, frequency, and scan speed on laser‐induced oxides is examined with an aim to produce a model suitable for predictive control of the elemental composition and color of the oxide film. Surface morphology control via alteration of the laser power is an important factor for defining the resulting coloration. A new finding from this work is the discovery that within the range of laser surface processing parameters investigated, the molybdenum concentration is inversely proportional to the chromium concentration. This paper provides data required for control of 316L‐SS surface chemistry, and for the expansion of the current thermokinetic model to incorporate the mechanistic effect of molybdenum.

Process Parameter Optimization of Directed Energy Deposited QT17‐4+ Steel

AbstractThe feasibility of using argon‐atomized QT 17‐4+ stainless steel powder for directed energy deposition (DED) additive manufacturing is studied. The QT 17‐4+ steel is a novel martensitic steel designed based on the compositional modification of the standard 17‐4 precipitation‐hardened (PH) stainless steel. This modification aims to achieve better mechanical properties of as‐deposited components compared to the heat‐treated wrought 17‐4PH steel. In this study, QT 17‐4+ steel powder is used for DED, for the first time. The influence of laser power, laser scan speed, powder feed rate, and hatch overlap on the density is studied. The central composite design is used to determine the experimental matrix of these factors. The response surface methodology is used to obtain the empirical statistical prediction model. Both columnar and equiaxed parent austenite grain structures are observed. X‐ray diffraction analyses reveal a decrease in the percentage of retained austenite from 19% in the powder to 5% after DED. The microhardness of the DED processed sample in the as‐deposited state is slightly higher than that of wrought 17‐4PH steel either solution‐annealed or H900‐aged. A higher 0.2% yield strength, a lower ultimate tensile strength, and lower elongation are observed for the vertically printed test sample, when compared to the horizontal one.

Microstructural Investigation of Process Parameters Dedicated to Laser Powder Bed Fusion of AlSi7Mg0.6 Alloy

Microstructural Investigation of Process Parameters Dedicated to Laser Powder Bed Fusion of AlSi7Mg0.6 Alloy

A REVIEW ON ADDITIVE MANUFACTURING (AM) MATERIAL - STATISTICS AND COMPARISIONS

Fused deposition modeling, also known as FDM, boasts a major edge in the manufacturing industry for its remarkable capacity to craft intricate components sans pricey tooling or manual labor. As is the case with any engineering process, selecting the right process parameters plays a critical role in determining the quality of FDM products. Hence, precise calibration of these parameters is essential to elevate the durability and functionality of printed parts. Our study seeks to explore the influence of five crucial FDM parameters, including layer height and shell thickness, on the tensile strength of build parts, with the ultimate goal of optimizing overall product performance. Keywords:Fusion Deposition Modeling (FDM), Shell thickness, precise calibration, durability, functionality of printed parts.

An advanced ICMV for vibratory roller compaction

AbstractThe compaction success of vibratory roller compaction can be assessed by systems for continuous compaction control (CCC) or intelligent compaction (IC) which calculate soil stiffness-proportional quantities based on measurements of the motion behavior of the vibrating drum. However, state-of-the-art intelligent compaction meter values (ICMV) do not only depend on the stiffness of the soil but are also strongly influenced by machine and process parameters. In this paper, the methodology for determining an advanced ICMV is presented, in which the mechanical properties of the soil, the process parameters and geometric relationships in the contact area between the drum and the soil are directly included in the calculation. The methodology is explained on the example of measurement data from a compaction test conducted on sandy gravel with a heavy single-drum roller. The results of the novel ICMV are compared with those of the most widely used IC systems.

Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques

Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques

Ammonia Synthesis by Nonthermal Plasma Catalysis: A Review on Recent Research Progress

Abstract Ammonia is one of the most important industrial chemicals which is commonly used for producing fertilizers and cleaning solutions and as the refrigerant gas and precursors for making various chemicals. And with the goal of sustainable development, ammonia is also proposed as the clean fuel for decarbonized transportation. The current the Haber-Bosch process for ammonia synthesis has large footprint and operates under harsh conditions using fossil fuels as the feedstock, being recognized as the major carbon emission source. Accordingly, call for sustainable production of green ammonia using renewable energies is proposed. Ammonia synthesis assisted by nonthermal plasmas has emerged in recent years as a novel and mild electrified technology, which can potentially be coupled with intermittent renewable energies and green hydrogen. Although being promising, significant development is still needed to advance the technology towards practical applications at scales. Hence, this review comments the progression of key aspects of the plasma-assisted ammonia synthesis such as catalyst and reactor design, mechanistic understanding, and process parameters. The snapshot of the current developments and proposed perspectives hope to provide guidance for the future research efforts to drive the technology towards higher technology readiness levels.

Influence of process parameters on the occurrence of defects and dimensional properties of green part produced by material extrusion of highly filled polymer

Influence of process parameters on the occurrence of defects and dimensional properties of green part produced by material extrusion of highly filled polymer

Optimization of process parameters and predicting surface finish of PLA in additive manufacturing—a neural network approach

Optimization of process parameters and predicting surface finish of PLA in additive manufacturing—a neural network approach

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Parameters Optimization of Active Flexible Tool‐Assisted Shear Thickening Polishing Method for Concave Surface

An innovative approach, active flexible tool‐assisted shear thickening polishing (AFT‐STP), is introduced to overcome the limitations of low efficiency involved in the STP process of the concave surface. The principle and characteristics of AFT‐STP are presented, accompanied by the design of a flexible tool structure and the creation of an experimental setup. Utilizing the Taguchi method, experiments are planned and results are assessed via the signal‐to‐noise ratio to optimize process parameters. A variance analysis is then used to ascertain the relative influence of four key parameters—polishing speed, polishing gap, abrasive particle size, and abrasive concentration on the polishing performance of quartz glass in STP. Under the optimal parameters, a concave quartz glass workpiece with diameter Ø 20 mm and curvature radius 488 mm is polished, the average surface roughness Sa of the five measuring points on the surface decreases from 142.18 ± 8.63 (Avg. ± Std.) nm to 5.34 ± 1.16 nm in 45 min with MRR 57.3 nm min−1, and the surface defects of the processing area are completely removed, leaving only minor pits. The results show that the AFT‐STP is an effective method for high‐quality polishing of concave surfaces.

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Development of a Genetic Algorithm – Artificial Neural Network model to optimize the Dimensional Accuracy of parts printed by FFF

Purpose Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters. Design/methodology/approach In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model. Findings The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value. Originality/value By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

Enhanced Electroactive Phases of Poly(vinylidene Fluoride) Fibers for Tissue Engineering Applications

Nanofibrous materials generated through electrospinning have gained significant attention in tissue regeneration, particularly in the domain of bone reconstruction. There is high interest in designing a material resembling bone tissue, and many scientists are trying to create materials applicable to bone tissue engineering with piezoelectricity similar to bone. One of the prospective candidates is highly piezoelectric poly(vinylidene fluoride) (PVDF), which was used for fibrous scaffold formation by electrospinning. In this study, we focused on the effect of PVDF molecular weight (180,000 g/mol and 530,000 g/mol) and process parameters, such as the rotational speed of the collector, applied voltage, and solution flow rate on the properties of the final scaffold. Fourier Transform Infrared Spectroscopy allows for determining the effect of molecular weight and processing parameters on the content of the electroactive phases. It can be concluded that the higher molecular weight of the PVDF and higher collector rotational speed increase nanofibers’ diameter, electroactive phase content, and piezoelectric coefficient. Various electrospinning parameters showed changes in electroactive phase content with the maximum at the applied voltage of 22 kV and flow rate of 0.8 mL/h. Moreover, the cytocompatibility of the scaffolds was confirmed in the culture of human adipose-derived stromal cells with known potential for osteogenic differentiation. Based on the results obtained, it can be concluded that PVDF scaffolds may be taken into account as a tool in bone tissue engineering and are worth further investigation.

Abrasive waterjet cutting of r‐GO infused magnesium fiber metal laminates: Experimental investigations and optimization through gorilla troops algorithm

AbstractLaminated metal‐composite structures, known as fiber metal laminates (FMLs), are modern lightweight materials extensively utilized in diverse industries such as aerospace and automotive manufacturing. These materials offer enhanced impact and fatigue resistance, making them invaluable for various applications. However, machining FMLs poses a challenge due to the occurrence of delamination and heterogenous in nature during conventional methods. Therefore, this study aims to investigate the quality characteristics such as kerf width, surface roughness and kerf taper of Magnesium based FMLs processed with an unconventional machining process namely abrasive water jet cutting (AWJC). These FMLs comprise alternately stacked kevlar/carbon fibers adhesively bonded with epoxy resin matrix filled with varying wt% of reduced graphene oxide (r‐GO), combined with AZ31B alloy sheet as the skin material. AWJC experiments were performed by varying the process parameters including waterjet pressure (275–325 MPa), stand‐off distance (2.5–3.5 mm), and cutting speed (600–800 mm/min) based on the box–behnken experimental design. The findings from the statistical analysis underscore the significant influences of stand‐off distance and waterjet pressure on kerf characteristics, while the inclusion of r‐GO is observed to notably affect the surface quality. In pursuit of enhancing cut quality, a multi‐response optimization was conducted employing the Gorilla Troops Optimization (GTO) algorithm. Comparative analysis with established metaheuristics like the gray wolf algorithm, dragonfly algorithm, and harmony search algorithm reveals GTO's superior performance across various metrics, including rapid convergence, diversity, and spacing values. Further, the cutting‐induced damages such as matrix plowing, fiber‐metal debonding and composite delamination were observed through microstructure analysis.Highlights Magnesium (AZ31B) fiber metal laminate comprising with various wt% of reduced graphene oxide was fabricated. Abrasive waterjet cutting of FMLs were performed based on box–behnken design experimental approach. Statistical analysis and influence of process parameters on the kerf width, surface roughness and kerf taper were investigated. Multi‐response optimization was carried out using metaheuristic‐based gorilla troops algorithm.

Size effects of voids on the mechanical properties of 3D printed parts

AbstractAlthough the effects of additive manufacturing process parameters on the mechanical properties of 3D printed parts have been numerically investigated in the literature, less attention has been paid on the size effects of voids between deposited filaments. This study fills this gap by developing a methodology based on a combined finite element (FE) and design of experiment (DoE) technique. The development of FE methodology is based on micro-mechanical analysis of representative volume element (RVE) of 3D printed parts to predict the effective orthotropic properties. To account for the size effects of inter-bead voids, the RVE includes contribution of the multiple parameters of layer heights, layer widths and overlapping regions. To study the main and interaction effects of the above input parameters on the stiffness properties of 3D printed parts, a structured approach based on full factorial design is used. Although the size effects of voids on the constituents of elastic moduli of RVE were investigated, the main focus in the present work is to develop a regression model to predict the stiffness properties. The FE stress analysis of the RVE conducted in this study provides an insight about the potential failure modes such as delamination and filament debonding that may occur in load bearing 3D printed parts. For a case study, the results of FE-based homogenization technique in terms of stiffness properties are validated against the experimental data via three-point bending and Iosipescu shear tests which were conducted in conjunction with digital image correlation technique. The combined numerical and statistical approach proposed in this study provides a swift iterative design of 3D printed parts prior to the time-consuming computation modelling, contributing to reduce the number of tests and manufacturing costs.

Optimization of process parameters for improvement of joint strength of dissimilar aluminum alloys by friction stir welding with an activated flux of titanium diboride

Optimization of process parameters for improvement of joint strength of dissimilar aluminum alloys by friction stir welding with an activated flux of titanium diboride

Impact of Rheology-Based Optimum Parameters on Enhancing the Mechanical Properties and Fatigue of Additively Manufactured Acrylonitrile–Butadiene–Styrene/Graphene Nanoplatelet Composites

This study investigates the interaction between static and fatigue strength and the rheological properties of acrylonitrile–butadiene–styrene (ABS) polymer reinforced with graphene nanoplatelets (GNPs) in both filament and 3D-printed forms. Specifically focusing on the effects of 1.0 wt.% GNPs, the study examines their influence on static/fatigue responses. The rheological behaviour of pure ABS polymer and ABS/GNPs nanocomposite samples, fabricated through material extrusion, is evaluated. The results indicated that the addition of 1.0 wt.% GNPs to the ABS matrix improved the elastic modulus of the nanocomposite filaments by up to about 34%, while reducing their ductility by approximately 60%. Observations revealed that the static and fatigue responses of the composite filament materials and 3D-printed parts were not solely attributed to differences in mechanical properties, but were also influenced by extrusion-related process parameters. The shark-skin effect, directly related to the material’s rheological properties, had a major impact on static strength and fatigue life. The proposed method involved adjusting the temperature of the heating zones of the extruder during filament production to enhance the static response of the filament and using a higher nozzle temperature (270 °C) to improve the fatigue life of the 3D-printed samples. The findings reveal that the proposed parameter optimisation led to filaments with minimised shark-skin effects, resulting in an improvement in ultimate tensile strength compared to pure ABS. Moreover, the 3D-printed samples produced with a higher nozzle temperature exhibited increased fatigue lives compared to those manufactured under identical conditions as pure ABS.

Effect of oxygen substitution and oxycarbide formation on oxidation of Ti3AlC2 MAX phase

AbstractMAX phases, ternary transition metal carbides and nitrides, represent one of the largest families of layered materials. They also serve as precursors to MXenes, two‐dimensional (2D) carbides and nitrides. The possibility of oxygen substitution in the carbon sublattice, forming oxycarbide MAX phases and MXenes, was recently reported using secondary ion mass spectrometry. However, while the effect of oxygen substitution on the properties of MXenes was investigated, little is known about its effect on the properties of MAX phases. Here, we explore the influence of process parameters (e.g., particle size, synthesis temperature, annealing time, etc.) and oxygen presence in the lattice on the oxidation resistance of Ti3AlC2 MAX phase powders. We show that X‐ray diffraction measurements can identify oxygen substitution and assist in selecting MAX precursors to synthesize stable and highly conductive MXenes. Eliminating the substitutional oxygen from the MAX phase lattice increases the onset of oxidation by 400°C, from approximately 490 to 890°C. Finally, we discuss the impact of oxygen substitution in the MAX phases on the synthesis of MXenes and their resulting properties.