Modern optimization techniques for advanced manufacturing: Heuristic and Metaheuristic Techniques (original) (raw)
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optimization of machining parameters in Aluminum alloy
The study aims at optimization of cutting parameters in CNC End milling of Aluminium Alloy 6082. CNC milling is a versatile and most widely used operation in present industry. Surface quality affects fatigue life of components and influence various mechanical properties and has received serious attention for many years. In this work, experiments are conducted to analyze the surface roughness using various machining parameters such as Spindle speed, feed rate and depth of cut . The data was used to develop surface roughness prediction models as a function of the machining parameters. In the present study, CNC machining centre with Cemented carbide end mill of 12mm diameter and 30° helix angle was used. A multiple regression analysis is used to correlate the relationship between the machining parameters and surface roughness. RS methodology was selected to optimize the surface roughness resulting minimum values of surface roughness and their respective optimal conditions. An attempt has been made to compare the results of Response surface methodology(RSM)with the Genetic Algorithm(GA).
IOP Conference Series: Materials Science and Engineering
In the aerospace industry, the milling of aluminum alloy parts is a machining process with the primary purpose of removing high volumes of material. Aluminum alloys are materials that have relatively good machinability, which helps the process because many of the components of the aircraft are of high dimensions. These parts have many pockets more or less deep, and the removal by cutting off about 90% of the initial volume of the workpiece is a matter of consideration. The manufacturing process is protracted and involves long semi-finishing and finishing operations, so it is recommended that any researcher who begins and finishes an experimental study should do it base on a specific experimental plan. Mathematical statistics techniques and methods are used, but also optimization methods that lead to a rational choice of process parameters, process input data and objective functions that need to be improved. This scientific paper presents applied research based on an extremely pertin...
Experimental modeling of the milling process of aluminum alloys used in the aerospace industry
Bulletin of the Polish Academy of Sciences Technical Sciences
This research presents an experimental study carried out for the modeling and optimization of some technological parameters for the machining of metallic materials. Certain controllable factors were analyzed such as cutting speed, depth of cut, and feed per tooth. A dedicated research methodology was used to obtain a model which subsequently led to a process optimization by performing a required number of experiments utilizing the Minitab software application. The methodology was followed, and the optimal value of the surface roughness was obtained by the milling process for an aluminum alloy type 7136-T76511. A SECO cutting tool was used, which is standard in aluminum machining by milling. Experiments led to defining a cutting regime that was optimal and which shows that the cutting speed has a significant influence on the quality of the machined surface and the depth of cut and feed per tooth has a relatively small impact on the chosen ranges of process parameters.
International Journal of Engineering Research and, 2015
This experimental investigation was conducted to determine the effects of machining parameters on surface roughness and cutting forces in slot milling of A1uminium 2014-T6 under different lubrication conditions such as dry , MQL and also an external minimum quantity lubrication system was developed. Here the experiments are designed using Taguchi orthogonal array and nine experiments each under different lubrications .Then Taguchi based grey relation analysis is used to optimize the cutting parameters to have lowest surface roughness and cutting force among different combinations of speed ,feed and depth of cut. After that the results are analyzed using analysis of variance which is used for identifying the factors significantly affecting the performance measures and developed a mathematical model using regression technique to predict performance measures (surface roughness, cutting force).And finally the results shows that MQL system has better surface finish and low cutting force than dry lubrication systems. It is true that this small reduction has enabled significant improvement in machinability indices , so we can say that MQL machining is an alternative for dry systems.
Tool Performance Optimization While Machining Aluminium-Based Metal Matrix Composite
Metals
Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cut...
Experimental modelling and optimisation of electrical arc machining of Al-B4C metal matrix composite
Australian journal of mechanical engineering, 2019
Machining of present days superior engineering materials is still a challenging task before industries, as conventional machining processes have proven to be inefficient to process these materials. In order to meet challenges, numerous processes with innovative mechanism of material removal have come into existence. Electrical discharge machining (EDM) is one among many such processes that has got wide attention. However, EDM results in very poor material removal and requires very high specific energy as compared to conventional machining processes. Electrical arc machining (EAM) is a process, which is very similar to EDM but results in very high material removal rate (MRR). In the present research, an innovative process known as vibration-assisted electrical arc machining has been developed. The process has been used to machine aluminium-boron carbide metal matrix composite. Peak current, frequency of vibration and dielectric flushing velocity has been considered as input control factors to evaluate MRR and surface roughness (SR). An artificial intelligence (AI)-based approach has been applied for single objective optimisation for MRR and SR. The AI-based approach results in an improvement of approximately 230 and 50% in MRR and SR, respectively.
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/experimental-investigation-optimization-of-machining-parameter-in-milling-of-aluminium-2014-t6-alloy-under-different-lubrication-conditions https://www.ijert.org/research/experimental-investigation-optimization-of-machining-parameter-in-milling-of-aluminium-2014-t6-alloy-under-different-lubrication-conditions-IJERTV4IS080268.pdf This experimental investigation was conducted to determine the effects of machining parameters on surface roughness and cutting forces in slot milling of A1uminium 2014-T6 under different lubrication conditions such as dry , MQL and also an external minimum quantity lubrication system was developed. Here the experiments are designed using Taguchi orthogonal array and nine experiments each under different lubrications .Then Taguchi based grey relation analysis is used to optimize the cutting parameters to have lowest surface roughness and cutting force among different combinations of speed ,feed and depth of cut. After that the results are analyzed using analysis of variance which is used for identifying the factors significantly affecting the performance measures and developed a mathematical model using regression technique to predict performance measures (surface roughness, cutting force).And finally the results shows that MQL system has better surface finish and low cutting force than dry lubrication systems. It is true that this small reduction has enabled significant improvement in machinability indices , so we can say that MQL machining is an alternative for dry systems.
Progress in Additive Manufacturing, 2021
The art of nano-additive manufacturing in developing advanced mechanical components via machining cannot be overemphasized when developing mechanical parts for aerospace, automobile, and structural application through the end-milling of aluminum alloys. However, the end-milling process generates heat and friction due to the machining parameter that initiated the contact between the cutting tool and the workpiece. This excess heat leads to high surface roughness (SR), low material removal rate (MRR), and high cutting force (CF). This study aimed to resolve the machining parameters and the material adhesion by carrying out an experimental evaluation with multiobjective optimization of the machining factors on end-milling of AL8112 alloy using copra oil-based multi-walled carbon nanotube (MWCNTs) nanolubricant. The nano-lubricant preparation was done using the two-step method, and nano-lubricants were implemented via the minimum quantity brought to you by CORE View metadata, citation and similar papers at core.ac.uk provided by Covenant University Repository lubrication (MQL) method with the five machining factors. Additionally, the multi-objective optimization and prediction study was achieved using the ramp and desirability bar plot for the three responses, i.e., SR, MRR, and CF under the quadratic rotatable central composite design (QRCCD). The multiobjective optimization result shows that the minimum SR of 1.16 µm, maximum MRR of 52.1 mm 3 /min, and minimum CF of 33.75 N was obtained at the optimized machining factors. Furthermore, the models predicted the experimental results accurately. In conclusion, the multi-objective optimization with copra oil-based MWCNT's-nano-lubricant enhanced machine parts' production for sustainable additive manufacturing. This is a preview of subscription content, access via your institution. Article Google Scholar 2. 2. Okokpujie I, Okonkwo U, Okwudibe C (2015) Cutting parameters effects on surface roughness during end milling of aluminum 6061 alloy under dry machining operation. Internat J Sci Res 4(7):2030-2036 Google Scholar 3. 3. Sharma S, Kumar R, Gaur A (2015) A model for magnetic nanoparticle transport in a channel for targeted drug delivery. Proc Mater Sci 10:44-49 Article Google Scholar 4. 4.
The continuous introduction of new materials and the endless demands for engineers to produce complicated shapes within tighter tolerances in many industrial applications are gradually increasing. From this point of view, machining special materials is present great importance and also the Electrical Discharge Machining (EDM) is a relatively modern machining process. In this study, a powerful tool to design optimization for quality is used to find the optimal parameters for machining operations. Process parameter optimization is essential for exploiting their potentials and capabilities to the fullest extent economically.An orthogonal array, the signal-to-noise ratio, and the analysis of variance are employed to investigate the cutting characteristics of alloys using tungsten carbide cutting tools. Through this study, not only can the optimal parameters for machining operations be obtained, but also the main machining parameters that affect the cutting performance in machining operations can be found. Here an optimization machining parameters of the Electrical Discharge Machining (EDM) process on aerospace alloys with multiple performance characteristics has been carried out.
Ingenieria y Universidad, 2022
Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products