Thermal Friction Drilling Process Parametric Optimization for AISI 304 Stainless Steel Using an Integrated Taguchi-Pareto–Grey Wolf-Desirability Function Analysis Optimization Technique

Thermal friction estimations are presently essential on steel for manufacturing applications as they predict the aggregated energy required for the required process. However, the current thermal friction estimates are inaccurate as they exclude the optimized thresholds of both the input and output quantities. In this article, the optimization of the drilling operation process is accounted for by introducing a new method of combined Taguchi-Pareto–grey wolf-desirability function analysis applied on the AISI 304 stainless steel. An objective function was formulated using the delta values developed from the average signal-to-noise into the response table of the Taguchi method. Besides, the ranks of the parameters through the response table are taken in the reciprocal mode to evaluate the values of the linear program formulated according to the objective function and some constraints taken from the system. Six input parameters were considered tool cylindrical region diameter, friction angle, friction contact area ratio, mouthpiece thickness, feed rate and reciprocal speed. The outputs are the axial force, radial force, hole diameter dimensional error, roundness error and bushing length. These inputs and outputs were analyzed for the optimization process. Based on the results, which were solved using the C++ software, the best value converges in iteration 8 with the starting value of 1699.2. Iteration 1 drops to 11016.3 in six iterations (iterations 2 to 7) and finally converges at 11015.9 in iterations 8 through 20. The usefulness of the effort is to help process engineers to execute cost-effective energy conservation decisions in optimization that could be obtained using optimized thermal friction values

Temperature Optimization by Using Response Surface Methodology and Desirability Analysis of Aluminium 6061

Because aluminium is a lightweight and low-density material, its alloys, such as Al 6061 alloy, are extensively used in numerous automobile, defense, and aviation components. This study aims to develop a predictive model to investigate the impact of tool nose radius on the CNC turning process of Al 6061 alloy and better recognize the implications of operating machining considering cutting speed, rate of feed, cutting depth, and tool nose radius. The trials were carried out by using the response surface methodology (RSM), with an Al2O3 coated carbide tool as the cutter and an Al 6061 workpiece as the material. A mathematical model of the second-order was created. The analysis of variance (ANOVA) approach was used to analyze the performance characteristics of the turning operation. Individual desirability values from the desirability function analysis for the multi-responses are used to construct a composite desirability value. The ideal parameter levels were determined by using the composite desirability value, and the significant impact of parameters was assessed by using the analysis of variance. The minimum temperature attained at the machining parameters are 98.0 m/min cutting speed, 0.26 mm/rev rate of feed, 0.893 mm cutting depth, and 0.84 mm tool nose radius. The best total desirability value is 23.615 °C, indicating that the experimental results are close to the predicted values.publishedVersio

Milling of Inconel 718: an experimental and integrated modeling approach for surface roughness

Inconel 718, a hard-to-cut superalloy is reputed for having poor machining performance due to its low thermal conductivity. Consequently, the surface quality of the machined parts suffers. The surface roughness value must fall within the stringent limits to ensure the functional performance of the components used in aerospace and bioimplant applications. One doable way to enhance its machinability is the adequate dissipation of heat from the machining zone through efficient and ecofriendly cooling environment. With this perspective, an experimental and integrated green-response surface machiningbased- evolutionary optimization (G-RSM-EO) approach is presented during this investigation. The results are compared with two base-line techniques: the traditional flooded approach with Hocut WS 8065 mineral oil, and the dry green approach. A Box-Behnken response surface methodology (RSM) is employed to design the milling tests considering three control parameters, i.e., cutting speed (vs), feed/flute (fz), and axial depth of cut (ap). These control parameters are used in the various experiments conducted during this research work. The parametric analysis is then accomplished through surface plots, and the analysis of variance (ANOVA) is presented to assess the effects of these control parameters. Afterwards, a multiple regression model is developed to identify the parametric relevance of vs, fz, and ap, with surface roughness (SR) as the response attribute. A residual analysis is performed to validate the statistical adequacy of the predicted model. Lastly, the surface roughness regression model is considered as the objective function of the particle swarm optimization (PSO) model to minimize the surface roughness of the machined parts. The optimized SR results are compared to the widely employed genetic algorithm (GA) and RSM-based desirability function approach (DF). The confirmatory machining tests proved that the integrated optimization approach with PSO being an evolutionary technique is more effective compared to GA and DF with respect to accuracy (0.05% error), adequacy, and processing time (3.19 min). Furthermore, the study reveals that the Mecagreen 450 biodegradable oil-enriched flooded strategy has significantly improved the milling of Inconel 718 in terms of eco-sustainability and productivity, i.e., 42.9% cost reduction in cutting fluid consumption and 73.5% improvement in surface quality compared to the traditional flooded approach and the dry green approach. Moreover, the G-RSM-EO approach presents a sustainable alternative by achieving a Ra of 0.3942 μm that is finer than a post-finishing operation used to produce close tolerance reliable components for aerospace industry

Grey Relational Analysis-Based Optimisation of Input Parameters of Incremental Forming Process Applied to the AA6061 Alloy

Incremental forming is mainly based on the deformation occurring locally on sheet metal. A hemispherical head tool deforms the sheet metal progressively, and its path is controlled by means of a CNC machine. Even though the process is a time-consuming one, the cost reduction connected with punches and dies makes this process attractive for rapid prototyping. In single point incremental forming, the quality of formability mainly depends on the forming parameters and the tool path generated. In this study, cone-shaped components were formed from AA6061 alloy sheets with a thickness of 1.15 mm. Factors, such as feed rate, spindle speed, and step depth are considered as input parameters to determine the output response in terms of surface roughness and wall thickness. Taguchi’s technique was used for conducting the experiments with the smallest number of repetitions to evaluate the output. Grey relational analysis was introduced to determine the optimal forming parameters with respect to the output response. The experiments conducted with the optimised parameters show a minimal deviation of 0.15% in the grade value compared to the predicted grade which is acceptable

A Study on Parametric Appraisal of Drilling on Bio-compatible Materials

Titanium alloy and stainless steel finds widespread applications in different fields such as bio-medical, aerospace and electronics due to their superior physical properties (high strength,toughness,corrosion resistance and durability and low density). Due to their bio-compatibility in nature,they are used in the field of bio-medical engineering such as artificial bone joints, artificial knee joints and in dental fields. Drilling is one of the important machining processes involved in most of the application fields including bio-medical engineering. This study investigates the effect of drilling parameters on performance measures through development of numerical model using finite element approach.The numerical model is validated by experimental study. The parameters included for investigation are spindle speed, feed rate and drill diameter.The experimental plan is made using design of experiment approach, specifically a face centered central composite design of response surface methodology in order to reduce the experimental runs and reduce cost of experimentation. The output performance characteristics considered are burr height at entry, burr height at exit, surface roughness, circularity at entry and circularity at exit.In order to optimize multiple responses simultaneously, the responses are converted into single response using superiority and inferiority ranking (SIR) method. Empirical model relating machining parameters with output responses have been developed using non-linear regression analysis. An improved version of latest evolutionary approach known as Harmony Search (HS) algorithm has been is used to find out best parametric condition subjected to constraints such as circularity at entry and circularity at exit.The study also investigates the effect of high speed laser drilling process of Ti6Al4V and AISI 316 stainless steel during laser drilling.Laser machining is carried out using assistant gas as nitrogen environment using a 2.5 kW CO2 laser.The experimental planning has been done using Taguchi L9 orthogonal design. The machining parameters used for investigation in the study are flushing pressure, laser power and pulse frequency.The output responses investigated are heat affected zone (HAZ), spatter area, circularity and taper of hole. Parametric study on drilling process helps in providing guidelines to the practitioners for securing the implant material in an adequate manner

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al4V)

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters

Characterization of shrinkage effects in micro-injection moulding (µ-IM)

This thesis characterizes the effects on shrinkage in microinjection moulding. The literature review considers four branches of investigation (material properties, processing parameters, mould design and specimen design). Two research gaps rise from the analysis of the literature review: the absence of a standardized methodology for measuring shrinkage of moulded parts at the micro-scale, and the absence of optimization stage that implements multiple quality criteria. Adequate research routes are set in order to address these gaps. The conventional standard for determining shrinkage at the macro scale is adapted to the micro-scale and this bridges the first gap. The micro-mould replicates the same design of the standard, and a preliminary stage solves some mouldability problems: the implemented mould extended the mouldability range of processing parameters for improving the reliability of results. After the micro-mould validation, the study of shrinkage at the micro-scale considers the influence of five processing parameters: the mould and melt temperature, the holding time and pressure, then the injection pressure. The design of experiment approach identifies the critical parameters that affect moulding, post-moulding and total shrinkage in parallel to and normal to the flow direction within an interval of confidence of 95% for POM and 90% for 316L feedstock. Statistical tools analyse the results, and the trends of critical factors found confirmation in the literature. This methodology at the micro-scale can fill the first gap because it is on purpose designed for the micro-scale. Moreover, the binder of feedstock is a mixture of POM based polymers, and the use of a common platform permits to compare directly the two materials and highlight the influence of powder loading. The optimization stage adopts desirability functions for achieving optimized values that simultaneously fulfil two requests: minimize shrinkage and maximize moulded part mass. The analysis of the literature review shows that few papers adopt multiple quality criteria approach as methodology for optimizing the results, and none consider jointly part mass and shrinkage. The optimized processing parameters allow moulding “optimized specimens”, and results demonstrate that their total shrinkage and part mass achieve the requests. Even if the use of desirability functions produce results thatrepresents a compromise between the requests, the results show that overall shrinkage decreases and part mass increases. This approach demonstrates its reliability and bridges the second gap. The last part of the thesis investigates the 316L feedstock behaviour for filling micro-features parallel to and normal to the flow oriented. The moulded features are investigated for studying the replication quality and the effect of the orientation of channels with dimension close to the feedstock lower mouldability value. These informations are available in the literature only for polymers, and the contribution of this part of thesis is to fill this gap by analysing a feedstock. The statistical approach permits to identify the critical factors that affect the feature replication quality. Optical investigations allow to identify the 316L feedstock lower mouldability value and to observe the influence of the orientation of features with dimensions near the lower limit

Advanced fibre reinforced material : non-crimp composites

Abstract: Non-crimp fabric (NCF) composites combine the superior in-plane properties of unidirectional pre-impregnated tape (UDPT) and excellent out-of-plane properties of woven fabrics without their associated drawbacks of high manufacturing cost and crimping respectively. Research on such novel composite materials have mostly been parochial and focused on improving either the matrix or the reinforcement. The aim of this thesis is therefore to present a holistic and multifaceted study (in a life cycle vision of the composite) addressing the critical factors of matrix modification, dispersion quantification, testing optimisation and fibre reclamation from waste...D.Phil. (Mechanical Engineering

Precision Machining

The work included in this book focuses on precision machining and grinding processes, including milling, laser machining and polishing on various materials for high-end applications. These processes are in the forefront of contemporary technology, with significant industrial applications. Their importance is also made clear by the important works that are included in the research that is presented in the book. Some important aspects of these processes are investigated, and process parameters are optimized. This is performed in the presented works with significant experimental and modelling work, incorporating modern tools of analysis and measurements

Modeling and Optimization of Micro-EDM Operation for Fabrication of Micro Holes

Based on the experimental results, an analysis was made to identify the performance of various electrodes during fabrication of micro holes considering Inconel 718 as well as titanium as workpiece materials. It was found that that platinum followed by graphite and copper as electrode material exhibited higher MRR for both the workpiece materials but on the other hand platinum showed higher values of OC, RCL and TA respectively when compared to graphite and copper. The variation of temperature distribution in radial and depth direction with different process parameters has been determined for Inconel 718 and Titanium 5. Theoretical cavity volume was calculated for different process parameter settings for both workpiece materials and it was found that Titanium 5 exhibited higher cavity volume then Inconel 718. This research work offers new insights into the performance of micro-µ-EDM of Inconel 718 and Titanium5 using different electrodes. The optimum process parameters have been identified to determine multi-objective machinability criteria such as MRR, angle of taper of micro-hole, the thickness of recast-layer and overcut for fabrication of micro-holes