Using MACBETH method for supplier selection in manufacturing environment

Supplier selection is always found to be a complex decision-making problem in manufacturing environment. The presence of several independent and conflicting evaluation criteria, either qualitative or quantitative, makes the supplier selection problem a candidate to be solved by multi-criteria decision-making (MCDM) methods. Even several MCDM methods have already been proposed for solving the supplier selection problems, the need for an efficient method that can deal with qualitative judgments related to supplier selection still persists. In this paper, the applicability and usefulness of measuring attractiveness by a categorical-based evaluation technique (MACBETH) is demonstrated to act as a decision support tool while solving two real time supplier selection problems having qualitative performance measures. The ability of MACBETH method to quantify the qualitative performance measures helps to provide a numerical judgment scale for ranking the alternative suppliers and selecting the best one. The results obtained from MACBETH method exactly corroborate with those derived by the past researchers employing different mathematical approaches

A DEA-TOPSIS-based approach for performance evaluation of Indian technical institutes

Since independence, India has been one of the few developing countries to invest extensively in both science and technical education. In India, technical education plays a pivotal role in human resource development while creating skilled manpower, increasing industrial productivity and enhancing the quality of life. If a technical institute means to be effective in developing learned and qualified engineers, then it would be useful to know the performance of that technical institution. However, measuring the performance of a technical institution has received very little attention because it is very difficult to measure its output. Thus, this paper focuses on assessing the performance of eight Indian Institutes of Technology (IITs) using a combined approach of data envelopment analysis (DEA) and technique of order preference by similarity to ideal solution (TOPSIS). In the first phase, DEA is applied to shortlist the efficient IITs having the desired characteristics from the stakeholders’ point of view, and TOPSIS method is then employed to rank those efficient IITs while also identifying the best performing IIT. It is observed that IIT Kharagpur outperforms all the considered IITs which exactly corroborates with the findings of the recently published surveys/reports

PREDICTION OF RESPONSES IN A CNC MILLING OPERATION USING RANDOM FOREST REGRESSOR

In the present-day manufacturing environment, the modeling of a machining process with the help of statistical and machine learning techniques in order to understand the material removal mechanism and study the influences of the input parameters on the responses has become essential for cost optimization and effective resource utilization. In this paper, using a past CNC face milling dataset with 27 experimental observations, a random forest (RF) regressor is employed to effectively predict the response values of the said process for given sets of input parameters. The considered milling dataset consists of four input parameters, i.e. cutting speed, feed rate, depth of cut and width of cut, and three responses, i.e. material removal rate, surface roughness and active energy consumption. The RF regressor is an ensemble learning method where multiple decision trees are combined together to provide better prediction results with minimum variance and overfitting of data. Its prediction performance is validated using five statistical metrics, i.e. mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error, correlation coefficient and root relative squared error. It is observed that the RF regressor can be deployed as an effective prediction tool with minimum feature selection for any of the machining processes

A Quality Function Deployment-Based Model for Cutting Fluid Selection

Cutting fluid is applied for numerous reasons while machining a workpiece, like increasing tool life, minimizing workpiece thermal deformation, enhancing surface finish, flushing away chips from cutting surface, and so on. Hence, choosing a proper cutting fluid for a specific machining application becomes important for enhanced efficiency and effectiveness of a manufacturing process. Cutting fluid selection is a complex procedure as the decision depends on many complicated interactions, including work material’s machinability, rigorousness of operation, cutting tool material, metallurgical, chemical, and human compatibility, reliability and stability of fluid, and cost. In this paper, a decision making model is developed based on quality function deployment technique with a view to respond to the complex character of cutting fluid selection problem and facilitate judicious selection of cutting fluid from a comprehensive list of available alternatives. In the first example, HD-CUTSOL is recognized as the most suitable cutting fluid for drilling holes in titanium alloy with tungsten carbide tool and in the second example, for performing honing operation on stainless steel alloy with cubic boron nitride tool, CF5 emerges out as the best honing fluid. Implementation of this model would result in cost reduction through decreased manpower requirement, enhanced workforce efficiency, and efficient information exploitation

OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives

PARAMETRIC STUDY OF A CNC TURNING PROCESS USING DISCRIMINANT ANALYSIS

In the present day manufacturing scenario, computer numerical control (CNC) technology has evolved out as a cost effective process to perform repetitive, difficult and unsafe machining tasks while fulfilling the dynamic requirements of high dimensional accuracy and low surface finish. Adoption of CNC technology would help an organization in achieving enhanced productivity, better product quality and higher flexibility. In this paper, an endeavor is put forward to apply discriminant analysis as a multivariate statistical tool to investigate the effects of speed, feed, depth of cut, nose radius and type of the machining environment of a CNC turning center on surface roughness, tool life, cutting force and power consumption. Simultaneous discrimination analysis develops the corresponding discriminant function for each of the responses taking into account all the input parameters together. On the contrary, step-wise discriminant analysis develops the same functions while considering only those significant input parameters influencing the responses. Higher values of hit ratio and cross-validation percentage prove the application of both the discriminant functions as effective prediction tools for achieving enhanced performance of the considered CNC turning operation

Two-Layered Pulsatile Blood Flow in a Stenosed Artery with Body Acceleration and Slip at Wall

Pulsatile flow of blood through an artery in presence of a mild stenosis has been investigated in this paper assuming the body fluid blood as a two-fluid model with the suspension of all the erythrocytes in the core region as Bingham Plastic and the peripheral region of plasma as a Newtonian fluid. This model has been used to study the influence of body acceleration, non- Newtonian nature of blood and a velocity slip at wall, in blood flow through stenosed arteries. By employing a perturbation analysis, analytic expressions for the velocity profile, Plug-core radius, flow rate, wall shear stress and effective viscosity, are derived. The variations of flow variables with different parameters are shown diagrammatically and discussed. It is noticed that velocity and flow rate increase but effective viscosity decreases, due to a wall slip. Flow rates and speed are enhanced further due to the influence of body acceleration

Pulsatile Flow of Blood in a Constricted Artery with Body Acceleration

Pulsatile flow of blood through a uniform artery in the presence of a mild stenosis has been investigated in this paper. Blood has been represented by a Newtonian fluid. This model has been used to study the influence of body acceleration and a velocity slip at wall, in blood flow through stenosed arteries. By employing a perturbation analysis, analytic expressions for the velocity profile, flow rate, wall shear stress and effective viscosity, are derived. The variations of flow variables with different parameters are shown diagrammatically and discussed. It is noticed that velocity and flow rate increase but effective viscosity decreases, due to a wall slip. Flow rate and speed enhance further due to the influence of body acceleration. Biological implications of this modeling are briefly discussed