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

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

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

Prediction of Surface Roughness and Power in Turning Process Using Response Surface Method and ANN

This paper examines the influence of three cutting parameters (cutting speed, cutting depth and feed rate) on surface roughness and power in the longitudinal turning process of aluminium alloy. For the analysis of data gathered by experiments, two methods for prediction of responses were employed, namely Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The research has shown that the ANN gives a better prediction of surface roughness than the RSM. In the modelling of the power, the average error value obtained by the ANN does not differ significantly from its value obtained by the RSM. This research is conducted to reveal the rigidity of the machine tool in order to select an appropriate spindle motor for retrofit purpose. The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit. Therefore, the servo motor with smaller power than the original motor is selected which is cost-effective and it will not cause inappropriate strong vibrations that lead to the unexpected surface roughness and excessive noise inside the Learning Factory environment in which the machine tool is used

Development of Sustainable Methodologies in Product Design, Manufacturing and Education

The influence of sustainability in product design and manufacturing processes can be considered from two different points of view: the design of sustainable products and the sustainable manufacturing of those products. Of course, a basic assumption for the aforementioned elements to be realized is the appropriate training and education for sustainability of the young designers and engineers. In this research, sustainability has been applied to many fields, including design, manufacturing and education acting as an umbrella which covers all the three elements and has as the main target to promote sustainability. In today’s world, in which a considerable number of contrasting signs reveal that our society is currently contributing to the planet’s collapse, a new kind of engineer is needed, an engineer who is fully aware of what is going on in society and who has the skills to deal with aspects of sustainability. According to the literature review on the state-of-the-art associated to the subject, in the current research were developed tools and methodologies for the promotion of sustainability aspects that are related to product design, manufacturing and education. Product DesignThe research work was based on a framework, which was built according to the direct communication between users and designers. There is a need for a cultural transformation, which can be focused on consumers and promote the needed behavioural change. Moreover there is a need for a cultural transformation on the role of designers and engineers to the product design process, with an aim to address sustainability as well as emerging priorities from societal to environmental challenges. New tools and methodologies were generated, in order to promote sustainability to the users/citizens bringing them inside to the product design process, giving them the opportunity to be a vital part of it. ManufacturingSustainable manufacturing faces new challenges for developing predictive models and optimization techniques in order to produce more products. The first part of the current is related to the drilling process and cutting tool technology. The creation of mathematical models focused on maximization of productivity and cost reduction by identifying crucial parameters and processes influencing manufacturing effectiveness. The second part of the current research is associated to the development of models used by CAD/ CAM that allow a rapid improvement and an efficient design and manufacture.EducationThe third aspect of the research is associated with the education related to sustainability. The engineering students should develop sustainability competences such as critical thinking, systemic thinking, obtaining values consistent with the sustainability paradigm, except of just taking a course on sustainability, focus on the technological role of sustainability. Focus on that the current research was based on sustainable characteristics such as a) remote control freeware applications, b) share of valuable resources, c) distance learning methodology and d) active participation of the students.<br /

Predictive Modeling for Power Consumption in Machining Using Artificial Intelligence Techniques

AbstractThe objective of this work is to highlight the modeling capabilities of artificial intelligence techniques for predicting the power requirements in machining process. The present scenario demands such types of models so that the acceptability of power prediction models can be raised and can be applied in sustainable process planning. This paper presents two artificial intelligence modeling techniques - artificial neural network and support vector regression - used for predicting the power consumed in machining process. In order to investigate the capability of these techniques for predicting the value of power, a real machining experiment is performed. Experiments are designed using Taguchi method so that effect of all the parameters could be studied with minimum possible number of experiments. A L16 (43) 4-level 3-factor Taguchi design is used to elaborate the plan of experiments. The power predicted by both techniques are compared and evaluated against each other and it has been found that ANN slightly performs better as compare to SVR. To check the goodness of models, some representative hypothesis tests t-test to test the means, f-test and Leven's test to test variance are conducted. Results indicate that the models proposed in the research are suitable for predicting the power

Optimization of machining characteristics during helical milling of AISI D2 steel considering chip geometry

Helical milling is one of the high-performance and high-quality hole manufacturing activities with strong prospects for the automotive and aerospace industries. Literature suggests chip geometry plays a significant role in optimizing machining operations. In the present study, a mechanistic approach is used to estimate the chip geometry, cutting force and power/energy consumption concerning the tool rotation angle. Experiments are conducted at different levels of spindle rotational speed, cutter orbital speed and axial depth of cuts using 8 and 10 mm diameter mill cutters. Experimental results for cutting speed in X, Y and Z directions are measured. A hybrid approach, which combines the Taguchi method and Graph theory and matrix approach (GTMA) technique is used and optimized process parameters. The highest aggregate utility process parameters are met by 2000 rpm spindle speed, 50 rpm orbital speed and 0.2 mm axial cutting depth during helical milling of AISI D2 steel. FEM simulation is used for predicting the chip thickness, cutting forces and power consumption and also validated the optimization