Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

© 2019 by SAGE Publications Ltd.Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.Peer reviewedFinal Published versio

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

Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 μm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC

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

Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based on Neural Networks and Search Algorithms

This book chapter presents an Adaptive Control with Optimization (ACO) system for optimising a multi-objective function based on material removal rate, quality loss function related to surface roughness, and cutting-tool life subjected to surface roughness specifications constraint

Experimentation and Prediction of Temperature Rise in Turning Process using Response Surface Methodology

Reducing the temperature rise during turning operation improves the quality of the product and reduces tool wear. Experiments are conducted as per the Design of Experiments (DoE) of Response Surface Methodology (RSM) to predict the temperature rise by varying the cutting parameters such as cutting speed, feed rate and depth of cut. In the present study, the experiment was conducted on Aluminium Al 6061 by coated carbide tool. A second order mathematical model in terms of machining parameters was developed for temperature rise prediction using RSM. This model gives the factor effects of the individual process parameters. Values of Prob> F less than 0.05 indicate model terms are significant. The cutting speed is the most important parameter that cause the temperature of the turning process compared to the other factors such as feed rate and depth of cut. Validation results show good agreement between the actual process output and the predicted temperature rise

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso- firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. Afte

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. After the predicted SR has been obtained by using the two methods, average percentage deviation was calculated, the result obtained using least square approximation method (that is the mathematical expression) show the accuracy of 99% and Response Surface Methodology (RSM) mathematical model shows accuracy of 99.6% which is viable and appropriate in prediction of SR. When either of these models are applied this will enhance the rate of production

Optimization of cnc operating parameters to minimize surface roughness of Pinus sylvestris using integrated artificial neural network and genetic algorithm

The surface roughness of wood is affected by the processing conditions and the material structure. So, optimization of operation parameters is very crucial to have minimum surface roughness. In this study, modeling and optimization of surface roughness (Ra) of Scotch pine (Pinus sylvestris) was investigated. Firstly, the samples were cut under different conditions 8 mm, 9 mm and 11mm depth of cut and 12 mm, 14 mm and 16 mm axial depth of cut) in computer numerical control (CNC) machine, and then surface roughness (Ra) values of samples were calculated. Then a prediction model of surface roughness was developed using artificial neural networks (ANN). Optimization process was carried out to reach minimum surface roughness of wood samples by the genetic algorithm (GA) method. MAPE value of the ANN model was found lower than 4,0 %. The optimum CNC operation parameters were 1874,5 rad/s, 3,0 m/min feed rate, 9,7 mm depth of cut and 12 mm for axial depth of cut for minimum surface roughness. As a result of study, surface roughness of Scotch pine wood can be modeled and optimized using integrated ANN and GA methods by saving time and cost

Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented