Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM

Machining of Titanium alloys (Ti6Al4V) becomes more vital due to its essential role in biomedical, aerospace, and many other industries owing to the enhanced engineering properties. In the current study, a Box–Behnken design of the response surface methodology (RSM) was used to investigate the performance of the abrasive water jet machining (AWJM) of Ti6Al4V. For process parameter optimization, a systematic strategy combining RSM and a heat-transfer search (HTS) algorithm was investigated. The nozzle traverse speed (Tv), abrasive mass flow rate (Af), and stand-off distance (Sd) were selected as AWJM variables, whereas the material removal rate (MRR), surface roughness (SR), and kerf taper angle (θ) were considered as output responses. Statistical models were developed for the response, and Analysis of variance (ANOVA) was executed for determining the robustness of responses. The single objective optimization result yielded a maximum MRR of 0.2304 g/min (at Tv of 250 mm/min, Af of 500 g/min, and Sd of 1.5 mm), a minimum SR of 2.99 µm, and a minimum θ of 1.72 (both responses at Tv of 150 mm/min, Af of 500 g/min, and Sd of 1.5 mm). A multi-objective HTS algorithm was implemented, and Pareto optimal points were produced. 3D and 2D plots were plotted using Pareto optimal points, which highlighted the non-dominant feasible solutions. The effectiveness of the suggested model was proved in predicting and optimizing the AWJM variables. The surface morphology of the machined surfaces was investigated using the scanning electron microscope. The confirmation test was performed using optimized cutting parameters to validate the results

Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

Electrical discharge machining (EDM) is a non-traditional machining process widely used to machine geometrically complex and hard materials. In EDM, selection of optimal EDM parameters is important to have high quality products and increase productivity. However, one of the major issues is to obtain better machining performance at optimal value of these machining parameters. Modelling and optimization of EDM parameters have been considered to identify optimal EDM parameters that would lead to better EDM performance. Due to the complexity and uncertainty of the machining process, computational approaches have been implemented to solve the EDM problem. Thus, this study conducted a comprehensive investigation concerning the influence of EDM parameters on material removal rate (MRR), surface roughness (Ra) and dimensional accuracy (DA) through an experimental design. The experiment was performed based on full factorial design of experiment (DOE) with added center points of pulse on time (Ton), pulse off time (Toff), peak current (Ip) and servo voltage (Sv). In the EDM optimization, glowworm swarm optimization (GSO) algorithm was implemented. However, previous works indicated that GSO algorithm has always been trapped in the local optima solution and is slow in convergence. Therefore, this study developed a new hybrid artificial fish and glowworm swarm optimization (AF-GSO) algorithm to overcome the weaknesses of GSO algorithm in order to have a better EDM performance. For the modeling process, four types of regression models, namely multiple linear regression (MLR), two factor interaction (2FI), multiple polynomial regression (MPR) and stepwise regression (SR) were developed. These regression models were implemented in the optimization process as an objective function equation. Analysis of the optimization proved that AF-GSO algorithm has successfully outperformed the standard GSO algorithm. 2FI model of AF-GSO optimization for MRR and DA gave optimal solutions of 0.0042g/min and 0.00129%, respectively. On the other hand, the SR model for Ra of AF-GSO optimization gave the optimal solution of 1.8216p,s. Overall, it can be concluded that AF-GSO algorithm has successfully improved the quality and productivity of the EDM problems

Development of tri-hybrid nanofluids as cutting fluids to enhance performance of end milling process of aluminium alloy 6061-T6

Machining of aluminium alloys is extensively complex due to the adherence tendency of aluminium to the tool surface. During machining, the tool wear is mainly affected by forming an adhesive layer and built-up-edge, significantly affecting the machined surface's quality. Several studies are carried out to restrict the heat generated in machining. Among the various alternatives available, the cutting fluids remain to be the choice. Therefore, different techniques are explored to replace the use of cutting fluids. Nowadays, using nanotechnology in science and industry improves the yield of different processes. Hence, machining operations are used as nanofluid and coated cutting tools with nanoparticles. However, their usage in machining is a comparatively primary stage and deserves much attention. The hybrid nanofluids are potential fluids to offer better heat transfer performance and thermophysical properties than single nanofluids. The machining process using hybrid nanofluids requires further research to better understand the mechanism of tool wear and the fundamental aspects are not yet ventured. This study aims to develop tri-hybrid nanofluids as cutting fluids to enhance the performance of the end milling process of AA6061-T6. The tri-hybrid SiO2-Al2O3-ZrO2 nanoparticles were dispersed in 60:40 vol.% of deionized water to ethylene glycol, and concentrations 0.08 and 0.12 wt.% were selected to mix with dispersing agent CTAB at a 1:3 weight ratio. After two weeks of daily visual and UV-Vis spectral examination, the tri-hybrid nanofluids were stable. The zeta potential is higher than 30 mV, suggesting well-dispersed nanoparticles. The uncoated tungsten carbide, single-layered CVD TiCN-Al2O3 and dual-layered PVD TiAlTaN tungsten carbide inserts were used. The study was conducted using cutting speed, feed rate, depth of cut, MQL flow rate and concentration and machining responses of surface roughness, cutting temperature, cutting force, flank wear. Response surface methodology with central composite rotatable design approach is used, and experimental data were validated statistically. SEM micrographs and EDX patterns characterized tool damage. At 0.1 wt.% and 70°C, tri-hybrid nanofluids were 41.1, 10.5 and 20.3 % better thermal conductivity than base fluid, SiO2-Al2O3 and SiO2-ZrO2, respectively. The tri-hybrid nanofluids exhibited 50.5% viscosity enhancement at higher concentrations and lower temperatures. At a higher feed rate, uncoated demonstrated lower surface roughness of AA6061-T6, reflecting the effectiveness of tri-hybrid nanofluids. The cutting temperature below 38 °C improved 84% over the conventional technique. The cutting force was below 30 N, indicating a 35% improvement in process performance. Coated tool exhibited higher cutting force due to coated hardness and various tool failures mechanism. Adhesion, attrition and edge fracture were among of tool failures observed. The absence of BUE is attributed to reduced chip adhesion with higher MQL flow rates and concentrations. Due to coating delamination, coated inserts had worse flank wear than uncoated inserts. However, uncoated tool dominated attrition wear. The optimal end milling parameters were established. The optimum uncoated tungsten carbide cutting conditions were 8440 rpm, 50.1 mm/min feed rate, 0.336 mm cut depth, 1.8 mL/min MQL flow rate, and 0.112 wt% concentration using multi-criteria decision making on parallel coordinates. The application of tri-hybrid nanofluids is the first attempt to enhance single and dual-hybrid thermal-physical properties as well as end milling process performance under high-speed machining. It is strongly recommended to use a newly created tri-hybrid nanofluid with MQL technique in various applications of machining industries

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multi-criteria decision making respectively. This integrated approach, to best of the authors’ knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design

Modelling and Prediction of Effect of Machining Parameters on Surface Roughness in Turning Operations

In this study, effects of different machining parameters on surface roughness in turning of St-37 material are presented. The machining experiments were carried out on the CNC lathe. In order to minimize the number of experiments, the experimental design was set up using Taguchi\u27s L27 orthogonal array. Cutting speed (150 m/min, 200 m/min, and 250 m/min), feed rate (0,1 mm /rev, 0,2 mm/rev, and 0,3 mm/rev), depth of cut (0,5 mm, 1 mm, and 1,5 mm), and tool nose radius (0,4 mm, 0,8 mm and 1,2 mm) were used as control factors. The analysis of variance (ANOVA) was performed in order to determine the impact of the control factors on surface roughness. Signal/noise (S/N) ratios were determined in the Taguchi design. The results of the regression models and Taguchi Analysis revealed that the most effective parameters on surface roughness (Ra and Rz) were the feed rate (f) and tool nose radius (R)

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Genetic Based Experimental Investigation on Finishing Characteristics of AlSiCp-MMC by Abrasive Flow Machining

Implementing non-conventional finishing methods in the aircraft industry by the abrasive flow machining (AFM) process depends on the production quality at optimal conditions. The optimal set of the process variables in  metal-matrix-composite (MMC) for a varying reinforcement percentage removes the obstructions and errors in the AFM process. In order to achieve this objective, the resultant output functions of the overall process using every clustering level of variables in a model are configured by using genetic programming (GP). These functions forecast the data to vary the percent of silicon carbide particles (particles without experimentation obtaining the output functions for material removing rates and surface roughness changes of Al-MMCs machined with the AFM process by using GP. The obtained genetic optimal global models are simulated and, the results show a higher degree of accuracy up to 99.97% as compared to the other modeling techniques.   &nbsp

Estudo do micro-corte no acabamento de um biomaterial de difícil usinabilidade

Doutoramento em Engenharia MecânicaA microusinagem está se tornando um processo de usinagem amplamente usado em indústrias ou pesquisas acadêmicas, pois este processo é uma opção para a miniaturização que apresenta bons resultados. Embora o processo de microusinagem apresente grandes vantagens, existem ainda lacunas há serem preenchidas ou o desenvolvimento de novas aplicações, principalmente para área médica. Este estudo investigou o uso do micro-corte com altas velocidades de corte no acabamento da liga de titânio Ti-6Al-7Nb, para fins de aplicações dentárias. A eficiência desse processo foi analisada através da análise da corrosão dos componentes em in vitro testes. Os resultados indicaram que essa técnica pode beneficiar a eficiência dos componentes dentários.The micromachining is becoming a machining process widely used in the industries or academic researchers, because this process is an option to miniaturization that presents good results. In spite of micromachining process presents great advantages, there are still gaps to be filled or discovery of new applications, mainly for the medical applications. This study investigated the use of the micro-cutting with high speed machining in the finishing of the Ti-6Al-7Nb titanium alloy, for purposes of dental applications. The efficiency of this process was analyses through the corrosion analysis of the components in in vitro test. The results of experiments indicated that this technique can benefit for the dental components

Development of the Analysis and Optimization Strategies for Prediction of Residual Stresses Induced by Turning Processes

Difficult-to-machine materials are widely used in the aerospace and automotive industries including landing gears of aircrafts, drive-shafts of automobiles, and high strength bolts and frame parts of airplanes and motorsports due to their high toughness, less sensitivity to heat, and high resistance to fatigue and corrosion. Machining these materials is accompanied by high cutting temperatures and forces, which cause high residual stresses. It is known that high temperature leads to inaccuracies in component dimensions and causes phase transformation in the material. High cutting forces also raise the power consumption of turning machines and result in an excessive deflection and consequently breakage of the tool. Also, both large cutting temperatures and forces cause high tool wear. Most importantly, machining-induced tensile residual stresses have detrimental effects on the performance of components due to having the tendency to open tiny cracks and speed up crack propagation, which subsequently results in decreasing the resistance to fatigue and corrosion. In contrast, high compressive residual stresses have beneficial effects as they tend to close cracks and slow down crack propagation, which consequently increases the fatigue life considerably. The machining process is required to be efficient by removing as large amount of material as possible, meaning to have a high material removal rate. Machining forces, temperature, residual stresses, and material removal rate depend highly on machining parameters including cutting conditions and tool geometry. Therefore, a thorough optimization study is required to be conducted to identify optimal machining parameters including cutting speed, feed rate, edge radius, rake angle, and clearance angle to improve response variables specially residual stresses, which will be highly desirable and of paramount importance to the industry. More particularly, when the optimization is carried out based on Finite Element Method (FEM), by which the expensive, time-consuming process of experimental tests is avoided, the outcome will be more economical for the industry. Finite Element (FE) modeling of orthogonal turning is considered as an open-ended subject as most of the phenomena involved in the orthogonal turning, which also exist in other machining operations, are not fully understood. In the present research work, first, a predictive high-fidelity finite element model is developed using Abaqus software to obtain response variables of cutting temperature, machining forces, and residual stresses induced by orthogonal turning 300M Steel. The validity of the developed FE model is then verified by comparing the predicted machining forces, chip thickness, and residual stresses with those of experimental tests obtained in turn using a piezoelectric dynamometer, a digital micrometer, and ‘X-Ray diffraction apparatus, electropolishing equipment, and a profilometer machine’. The FE model is then utilized to systematically derive response functions (Meta or surrogate models) for desired FE outputs using D-optimal Design of Experiment (DoE) and Response Surface Method (RSM). The derived response functions explicitly relate the desired responses to identified design parameters, and therefore, can be effectively utilized for design optimization problems without using the FE model. Finally, multi-criteria optimization problems are formulated to reduce superficial residual stresses individually and improve a combination of residual stresses, cutting temperature, cutting and thrust forces, and material removal rate by obtaining optimum values of machining parameters including cutting speed, feed rate, edge radius, rake angle, and clearance angle. Special attention is devoted to minimizing the machining-induced residual stresses. Optimization is conducted using a hybrid method of Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) technique in order to accurately capture the global optimum values of machining parameters and response variables. The optimization results show considerable improvement in the total objective function and especially residual stresses. Since there are no research studies on the finite element simulation, experimental test, and most importantly, constrained and unconstrained multi-performance optimization of machining characteristics and residual stresses for radial turning of 300M steel, the results of the present research can be utilized as a reference for future works along this field

Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni55.8Ti Shape Memory Alloy

In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi’s L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm3/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions