Multi-response optimization in machining: exploration of TOPSIS and Deng’s similarity based approach

Machining deals with removal of unwanted material from the work piece in the form of chips in order to get required dimension. Consumption of energy, wastage of material, requirement of skilled person etc. make the process expensive. Hence, machining industries have to face the inevitable challenge to reduce cost as well as to machine material within the tolerance limit which can be accepted by the customers. The output characteristics like Material Removal Rate (MRR), surface roughness, tool wear, tool life, cutting temperature, cutting force etc. are greatly influenced by the input cutting parameters like speed, feed rate, depth of cut etc. Therefore, selection of cutting parameter plays an important role for a sound production. Optimization techniques are quite helpful for selection of appropriate cutting parameters through offline check. The industries have to concern about a number of performance characteristics simultaneously because focus on a single objective may appear as loss for rest of the objectives, and, hence, multi-objective optimization techniques may be suitable. In the present work, turning operation of aluminum was carried out using a HSS tool on a lathe machine. Cutting parameters: speed, feed rate, and depth of cut was varied at five different levels; Taguchi method was employed for designing a L25 orthogonal array. The output performances viz. MRR, surface roughness, cutting temperature, and cutting forces were recorded for each run. Deng’s similarity based method and TOPSIS (integrated with Taguchi method) were explored for determining appropriate process environment (parameter setting) for simultaneous optimization of multiple process-performance-yields

Pokročilé modelování povrchové drsnosti pomocí neuronových sítí, Taguchiho metody a genetického algoritmu

Modern manufacturing requires reliable and accurate models for the prediction of machining performance. Predicting surface roughness before actual machining plays a very important role in machining practice. This paper presents the modeling methodology for predicting the surface roughness in turning of unreinforced polyamide based on artificial neural networks (ANNs), Taguchi method and genetic algorithm (GA). The machining experiment was conducted based on Taguchi’s experimental design using L27 orthogonal array. Input variables consisted of cutting speed, feed rate, depth of cut and tool nose radius, while surface roughness (Ra) was considered as output variable. To systematically identify optimum settings of ANN design and training parameters, Taguchi method was applied. Furthermore, a simple procedure based on GA for enhancing the ANN model prediction accuracy was applied. Statistically assessed as an accurate model, ANN model equation was graphically presented in the form of contour plots to study the effect of the cutting parameters on the surface roughness.Moderní výroba vyžaduje spolehlivé a přesné modely pro predikci výkonu zpracování. Předvídání drsnost povrchu před vlastním zpracováním hraje velmi důležitou roli v obráběcí praxi. Tato práce představuje modelovou metodiku pro odhad drsnosti povrchu při soustružení z prostého polyamidu na bázi umělé inteligence neuronových sítí (ANNs), Taguchiho metodě a genetických algoritmů (GA). Obráběcí experiment byl proveden na základě experimentálního Taguchiho návrhu pomocí L27 ortogonální pole. Vstupními proměnnými jsou řezná rychlost, posuv, hloubka řezu a poloměru břitu, zatímco drsnost povrchu (Ra) je považována za výstupní proměnnou. Pro systematickou identifikaci optimálního nastavení ANN návrhu a odborné přípravy parametrů byla použita metoda Taguchi. Dále byl použit jednoduchý postup, založený na GA pro zvýšení přesnosti modelu ANN predikce. Statisticky vyhodnocený přesný model ANN rovnice byl graficky prezentován ve formě obrysů pro studium vlivu řezných parametrů na drsnost povrchu

Modelado de las fuerzas de corte en el torneado de alta velocidad utilizando redes neuronales artificiales

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.Las fuerzas de corte son variables muy importantes para el rendimiento del mecanizado, ya que afectan la rugosidad de la superficie, la vida útil de la herramienta de corte y el consumo de energía. La reducción del consumo de energía eléctrica de los procesos de fabricación no solo beneficia económicamente a los fabricantes, sino que también mejora su comportamiento medioambiental. Muchos factores, como el material de la herramienta de corte, la velocidad de corte y el tiempo de mecanizado, afectan la fuerza de corte y el consumo de energía de la máquina. En la actualidad, muchas investigaciones se han realizado sobre el consumo energético de las máquinas herramienta. Sin embargo, la investigación sobre torneado de acero al carbono a alta velocidad es escasa. En este trabajo se estudiaron los efectos de los materiales de las herramientas de corte y su velocidad sobre las fuerzas de corte y el consumo específico de energía en el torneado en seco de alta velocidad de acero AISI 1045. Las fuerzas de corte se determinaron experimentalmente y se compararon con las estimaciones de los modelos predictivos desarrollados mediante regresión polinomial y redes neuronales artificiales. Los modelos obtenidos fueron evaluados según métricas de desempeño como el coeficiente de determinación y la raíz del error cuadrático medio, donde los modelos polinomiales no superaron el 70% en la representación de la variabilidad de los datos. Se determinó la velocidad de corte y el tiempo de mecanizado relacionados con el mayor y menor consumo de energía de las plaquitas CT5015-P10 y GC4225-P25. Los valores más bajos de consumo de energía de estas herramientas se alcanzaron para la velocidad de corte intermedia. Además, la plaquita GC4225 presentó un mayor consumo que la herramienta CT5015

Prediction of Surface Quality Using Artificial Neural Network for the Green Machining of Inconel 718

Inconel 718 is a nickel-based heat resistant super-alloy (HRSA) that is widely used in many aerospace and automotive applications. It possesses good properties like corrosion resistance, high strength, and exceptional weld-ability but it is considered as one of the most difficult alloys to cut. Recently researchers have focused on employing many machining strategies to improve machinability of Inconel 718. This research work presents the experimentation of wet milling of Inconel 718 using a carbide tool with biodegradable oil. Surface quality is the major aspect of machinability. Hence input parameters such as depth of cut, cutting speed, and feed rate are considered to study their effect on surface quality. Nine experimental runs based on an L9 orthogonal array are performed. Additionally, analysis of variance (ANOVA) is applied to identify the most significant factors among cutting speed, feed rate, and depth of cut. Moreover, this research work presents the Artificial Neural Network (ANN) model for predicting the surface roughness based on experimental results. The ANN based-decision-making model is trained by using acquired experimental values. Visual Gene Developer 2.0 software package is used to study the efficiency of ANN. The presented ANN model demonstrates a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of surface roughness and tool wear

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process

MODELING AND OPTIMIZATION OF MACHINING PERFORMANCE MEASURES IN FACE MILLING OF AUTOMOTIVE ALUMINUM ALLOY A380 UNDER DIFFERENT LUBRICATION/COOLING CONDITIONS FOR SUSTAINABLE MANUFACTURING

The use of cutting fluids in machining process is very essential for achieving desired machining performance. Due to the strict environmental protection laws now in effect, there is a wide-scale evaluation of the use of cutting fluids in machining. Consequently, minimal quantity lubrication (MQL), which uses very small quantity of cutting fluids and still offers the same functionality as flood cooling, can be considered as an alternative solution. This thesis presents an experimental study of face milling of automotive aluminum alloy A380 under four different lubrication/cooling conditions: dry cutting, flood cooling, MQL (Oil), and MQL (Water). Experiments were design using Taguchi method for design of experiments. Empirical models for predicting surface roughness and cutting forces were developed for these four conditions in terms of cutting speed, feed and depth of cut. Optimization technique using Genetic Algorithms (GA) was used to optimize performance measures under different lubrication/cooling conditions, based on a comprehensive optimization criterion integrating the effects of all major machining performance measures. Case studies are also presented for two pass face milling operation comparing flood cooling condition with MQL. The comparison of the results predicted by the models developed in this work shows that the cutting force for MQL (Oil) is either lower or equal to flood cooling. The surface roughness for MQL (Oil) is comparable to flood cooling for higher range of feed and depth of cut. A comparison of the optimized results from the case studies, based on value of utility function, shows that the optimum point for two pass face milling operation having MQL (Oil) as finish pass has highest utility function value

Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear

Surfaces functionality of precision machined components: Modelling, simulation, optimization and control

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 23/07/2008.This research develops an analytical scientific approach for investigating the high precision surface generation and the quantitative analysis of the effects of direct factors in precision machining. The research focuses on 3D surface characterization with particular reference to the turning process and associated surface generation. The most important issue for this research is surface functionality which is becoming important in the current engineering industry. The surface functionality should match with the characterization parameters of the machined surface, which can be expressed in formula form as proposed in chapter 4. Modelling and simulation are extensively used in the research. The modelling approach integrates the cutting forces model, thermal mode% vibration model, tool wear model, machining system response model and surface topography model. All of those models are integrated as a whole model. The physical model with such as direct inputs is formed. The major inputs to the model are tooling geometry and the process variables. The outputs from the modelling approach are cutting force, surface texture parameters, dimensional errors, residual stress and material removal rate. MATLAB and Simulink are used as tools to implement the modelling and simulation. According to the simulation results, it is found that the feed rate has the most profound effect on in surface generation. The influence of the vibrations between the cutting tool and the workpiece on the surface roughness may be minimised by the small feed rate and large tool nose radius. Surface functionality simulation has been developed to model and simulate the surface generation in precision turning. The surface functionality simulation model covers the material and tool wear as well. It shows that chip formation is resulted from cutting forces. Cutting trials are conducted to validate the modelling and simulation developed. There are positive results that show the agreement between the simulation and experimental results. The analysis of the results of turning trials and simulations are conducted in order to find out the effects of process variables and tooling characteristics on surface texture and topography and machining instability. From the research, it can be concluded that the investigation on modelling and simulation of precision surfaces generation in precision turning is performed well against the research objectives as proposed. Recommendations for future work are to improve the model parameters identification, including comprehensive tool wear, chip formation and using Neural Networks modelling in the engineering surface construction system.Universiti Teknologi Tun Hussien Orin (UTHM

Diamond turning of contact lens polymers

Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost