Selection Of Suitable Process Parameters Research Articles

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.

Multi-Component Evaporation and Uneven Aluminum Distribution during High-Power Vacuum Laser Welding of Ti-6Al-4V Titanium Alloy

Titanium alloy is an important material for the manufacture of key components of deep-sea submersibles. High-power vacuum laser welding is an important method for welding TC4 thick plate (40–120 mm) structures. However, due to the low melting point of aluminum, its uneven distribution in the weld caused by evaporation during welding affects the quality of joints. This paper conducted experimental and simulation studies to investigate the effect of process parameters on multi-component evaporation and uneven aluminum distribution. Based on a three-dimensional model of vacuum laser welding, the mechanism of the uneven distribution of aluminum in the weld is explained. The results show that the uneven distribution of aluminum in the weld is mainly related to the metal vapor behavior and keyhole morphology. As the welding speed rises from 1 m/min to 3 m/min, the proportion of aluminum in the metal vapor and the degree of compositional unevenness increase. When the laser power increases from 6 kW to 18 kW, the proportion of aluminum in the metal vapor and degree of unevenness increase, peak at 12 kW, and then decrease. This work facilitates the selection of suitable process parameters to reduce aluminum evaporation during the high-power vacuum welding of Ti-6Al-4V alloys. Joints with a more stable performance can be obtained by avoiding the uneven distribution of aluminum.

Finite Element Simulation and Experimental Investigation on Progressive Forming

A non-linear finite element technique is used to study the progressive forming process of the sheet, and the elastic-plastic distribution is analyzed based on Abaqus/Explict. Finite element simulations of progressive forming with different process parameters were carried out to analyse the effects of the tool head diameter, layer feed and sheet properties on forming quality. The results show that increasing the tool head diameter or decreasing the layer feed will result in better sheet forming quality, and the influence of the tool head diameter on the sheet forming quality is greater than that of the layer feed, and the selection of materials and the selection of suitable process parameters are more conducive to the improvement of forming quality and forming efficiency.

Multiphase numerical modeling and investigation of heat transfer for quenching of spherical particles in liquid pool

Quenching is a widely applied heat treatment process for metallic materials in order to adjust the microstructure and material properties by means of intensive cooling. Depending on the cooling rate attained with respect to the operating conditions as well as the cooling medium, specific material properties such as hardness can be adapted to engineering applications. One of the major constraints for intensive cooling remains with the selection of suitable process parameters. An imprudent selection will end up with sample distortion and cracks. The process being highly complex involving solidification, liquid pool immersion phenomena, rotation, the Leidenfrost effect and vapor formation emerging from liquid for these “millimeter” ranged particles, which make the complete process difficult to analyze by experimental investigations alone. In order to tackle this challenge, a multiphase numerical modeling based on a Eulerian framework is developed and experimentally validated in this work for analyzing the cooling rate of high temperature semi-solid spheres quenched in water. This model simulates particle quenching in liquids, where the source and sink terms of the conservation equations are modified to accommodate the phase transfer effects. To validate modeling results, a high temperature molten metal droplet generator is used to generate the droplets from Al 4.5-wt.% Cu with a diameter of about 1 mm which are quenched in water during the semi-solid state. The formation of dendritic structure within the solidifying droplet is highly sensitive to its cooling rate. This allows to calculate the cooling rate corresponding to the operating condition from SDAS (Secondary Dendrite Arm Spacing) and compare with numerical simulation. Finally, we investigate the influence of the particle Reynolds number (flow velocity) and pool temperature on heat transfer with this validated model.

Application of Thermal Spray Coatings in Electrolysers for Hydrogen Production: Advances, Challenges, and Opportunities

AbstractThermal spray coatings have the advantage of providing thick and functional coatings from a range of engineering materials. The associated coating processes provide good control of coating thickness, morphology, microstructure, pore size and porosity, and residual strain in the coatings through selection of suitable process parameters for any coating material of interest. This review consolidates scarce literature on thermally sprayed components which are critical and vital constituents (e. g., catalysts (anode/cathode), solid electrolyte, and transport layer, including corrosion‐prone parts such as bipolar plates) of the water splitting electrolysis process for hydrogen production. The research shows that there is a gap in thermally sprayed feedstock material selection strategy as well as in addressing modelling needs that can be crucial to advancing applications exploiting their catalytic and corrosion‐resistant properties to split water for hydrogen production. Due to readily scalable production enabled by thermal spray techniques, this manufacturing route bears potential to dominate the sustainable electrolyser technologies in the future. While the well‐established thermal spray coating variants may have certain limitations in the manner they are currently practiced, deployment of both conventional and novel thermal spray approaches (suspension, solution, hybrid) is clearly promising for targeted development of electrolysers.

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

Fabrication of high-aspect-ratio grooves with high surface quality by using femtosecond laser

PurposeThe purpose of this study is to fabricate high-aspect-ratio grooves with high surface quality by femtosecond laser (FS) to improve the machinability of silicon carbide (SiC) and optimize the process parameters in micromechanical applications.Design/methodology/approachFour contrast experiments are reported to characterize the FS laser grooving process for SiC with polarization direction, crystal orientation, multi-pass scanning and z layer feed, respectively. The effects of different experimental conditions on the groove characteristics, material removal rate (MRR), aspect ratio, heat affected zone (HAZ) and surface roughness Ra are analyzed.FindingsThe influence of increasing laser fluence and multi-scanning pass on the groove depth is greater than on the groove width. The MRR, aspect ratio, HAZ and Ra increased with the increase of laser fluence and multi-scanning pass. The direction of laser polarization affects the direction of hot electron injection but has little effect on the material characteristics. FS laser ablation is an isotropic process and there is no obvious change in different crystal orientations. The z-layer feed can significantly increase the groove width and depth and reduce HAZ and Ra. The maximum aspect ratio of 82.67% was fabricated.Originality/valueThe results contribute to the understanding of the removal mechanism and reduce the friction of the microfluidic device and improve the flowability in the FS laser ablation of SiC. This paper provides suggestions for the selection of suitable process parameters and provides a wider possibility for the application of micro-texture on SiC.

Multi-objective optimization design of die-sinking electric discharge machine (EDM) machining parameter for CNT-reinforced carbon fibre nanocomposite using grey relational analysis

In this research work, the grey relational analysis (GRA) is used as an optimization tool to find out the suitable process parameters for the machining of carbon nanotube-reinforced carbon fibre nanocomposite. In discrete space degree of relation of different data sets analysed by GRA. In this experimental work, four process parameters are selected, namely peak current (Ip), pulse on time (Ton), duty cycle (η) and gap voltage (Vg), and the significant effect on material removal rate (MRR) and tool wear rate (TWR) has been calculated. Both are considered as the main input variables for the selection of suitable process parameters. Predicted process parameters by GRA show significant increment of 0.000003 in MRR and significant reduction of 0.001904 in TWR.

Thermal energy balance control model of motorized spindle system enabling high-speed dry hobbing process

High-speed dry hobbing has been confirmed as a dominant advanced technique for gear machining due to its outstanding performance in terms of high productivity and environmental friendliness. The motorized spindle system is the key component of the high-speed dry hobbing machine, and its thermal stabilization is of significant importance for the machining precision. However, due to the integration of motor and spindle, the adoption of high cutting speed and the heavy cutting load, thermal energy will accumulate in the motorized spindle system and result in thermal deformation and stability degeneration, which eventually affects the consistency of hobbing accuracy. In this paper, an analytical model is proposed to control the thermal energy balance of a high-speed dry hobbing spindle system by optimizing the thermal energy accumulation with the hob rotation speed, hob axial feed rate, cooling water flow rate and the compressed air temperature. The optimal solution is obtained by solving the proposed optimization model with the simulated annealing method. A motorized spindle system is introduced as a case study and the experimental results indicate that the maximum value of the average temperature of the motorized spindle system is reduced from 39.6 °C to 35.0 °C, an 11.6% margin, and the material removal time is reduced from 2.07 min to 1.06 min, a 48.8% margin, under the optimal condition. Moreover, the average runout value of a hobbed workpiece is effectively controlled, by a 15.7% margin. Therefore, the presented optimization model can give a reference for the selection of suitable process parameters for thermal stability control and higher machining productivity and accuracy.

Experimental Study on Surface Integrity, Dimensional Accuracy, and Micro-Hardness in Thin-Wall Machining of Aluminum Alloy

This article presents an experimental investigation into the influence of process parameters viz. feed per tooth, axial depth of cut on milling force, surface finish, wall deflection and micro-hardness during thin-wall machining of an aerospace grade aluminum alloy 2024-T351. Results revealed that the process parameters significantly influence the surface finish and dimensional accuracy of machined thin-walls. High feed rate promoted the formation of built-up-edge (BUE). Combination of high feed and axial depth of cut aided in catastrophic failure of tools. Surface damages such as material plucking, material shearing, material adhesion and deformed feed mark layer formation were observed. Axial depth of cut negatively influenced the wall deflection leading to loss of dimensional accuracy. Interestingly, the micro-hardness at the machined surface was found to be lower than that of the bulk material hardness. These results will be useful in selection of suitable process parameters for quality and precise machining of thin-wall parts.

Chatter stability prediction for five-axis ball end milling with precise integration method

The differential dynamic equation (DDE) considering regenerative chatter effect is derived from an ideal cutting model. At the same time, the ball end mill-workpiece engagement (BWE) in five-axis milling is efficiently extracted by the semi-analytical method, where the machining residual left on the finished surface is taken into account. The explicit precise integration method (PIM) is adopted to translate the DDE into a time series expression, ensuring stability prediction without any discretization error loss. With the relationship of cutter edge and BWE at different instants, the stability lobe diagram (SLD) is constructed by Floquet theory, which is subsequently testified in the five-axis NC machine tool. The experimental results keep good agreement with estimations, validating the proposed method. Compared with traditional method in dynamic cutting forces modeling, the proposed approach is more close to actual processing. In contrast to Runge-Kutta based complete discretization method, PIM shows higher superiority no matter in convergence rate or calculation accuracy. Lastly, the influences of different inclination angles on stability are revealed, which can give a reference to selection of suitable process parameters for higher productivity.

Specific and Programmable Surface Structuring by Electrochemical Jet Processing

Component surface optimisation through the application of surface structuring is understood to be beneficial to performance and longevity of a component in service. However, the multi-functional requirements of a surface defined at the design stage can be difficult to realise. Utilisation of electrochemical jet processing (EJP) methods in surface processing allows a wide-range of variation in surface structures over a multitude of length scales. Surface characteristics such as roughness and depth of cut can be controlled by selection of suitable process parameters. With an optimised control structure, it is possible to directly impart a design intent to a component to create a specific programmed surface.EJP-specific computer-aided manufacturing (CAM) software has been developed and is reported here. This is demonstrated to control processes parameter variation as a function of part local co-ordinates to allow complete CAD to CAM translation of surface design intent. In this way both meso- and micro-scale features can be specifically located on a surface. Alongside this, functionally graded surfaces are created in stainless steel by application of a mathematical functions allowing automated variation of current and feed rate to achieve the desired surface roughness distribution. Using a greyscale image as a process parameters map with current, therefore surface roughness, being proportional to grey tone value, practical application of the software is further demonstrated by direct translation of digitised imagery to surface replication. This functionality allows for fast creation of repeatable, programmed surfaces.

Online Learning of Stability Lobe Diagrams in Milling

The productivity of milling machines is limited by chatter vibrations. Stability lobe diagrams (SLD) allow the selection of suitable process parameters to maximize the productivity. However, the calculation of SLDs is very time-consuming and requires complex experiments. In this article a new online learning method is presented, which allows the calculation of SLDs during the production process. The algorithm is a combination of reinforcement learning and nearest-neighbor-classification and allows the estimation of the stability border based on measured vibration signals during machining. The proposed algorithm is capable of being continuously trained with sorted input data. A trust criterion is introduced, which allows judging the prediction quality of the algorithm. The algorithm is validated with analytical benchmark functions and with a 2-DOF milling stability simulation.

SELECTION OF APPROPRIATE PROCESS PARAMETERS FOR GAS METAL ARC WELDING OF MEDIUM CARBON STEEL SPECIMENS

Gas Metal Arc Welding (GMAW) is a semi-automated process used widely for accurate welding in the fabrication industry. The selection of appropriate process parameters of GMAW is essential to obtain the desired weld quality. Â In the past, much work has been done investigating a variety of workpiece and electrode material combinations. In the present work, an Analytical Hierarchy Process (AHP) based parametric optimization is tried in gas metal arc welding of C45 medium carbon steel specimens using carbon dioxide as the gas shield. The experiments were performed by varying three process parameters, weld speed, weld voltage and weld current. The AHP facilitates the selection of suitable process parameters to obtain a sound weld. In the present experimental domain, optimal conditions are evaluated to be at a weld voltage of 30 V, weld current of 160 A with a weld speed of 475.75 mm/min. http://dx.doi.org/10.13033/ijahp.v5i2.184

The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling

Face milling is a process predominantly affected by dynamic variation of cutting forces, thermo-mechanical shocks and vibration leading to catastrophic tool failure along with gradual wear of the inserts. Keeping in view the industrial importance of this process, it is necessary to devise suitable methods to predict in advance the onset of tool failure without grossly impairing the machining set-up and the job. Hence, the applicability of back propagation neural network with delta bar delta learning rule for faster convergence has been studied with the above objective. The multi sensor based tool condition monitoring strategy shows that the learning rate adaptation scheme combined with the selection of suitable process parameters drastically reduces the training time of the artificial neural network without dispensing with the prediction accuracy.