Mohammed T. Hayajneh

A fuzzy multi-criteria decision making model for supplier selection

This paper presents a hybrid fuzzy model for group Multi Criteria Decision Making (MCDM). A modified fuzzy DEMATEL model is presented to deal with the influential relationship between the evaluation criteria. The modified DEMATEL captures such relationship and divides the criteria into two groups, particularly, the cause group and the effect group. The cause group has an influence on the effect group where such influence is used to estimate the criteria weights. In addition, a modified TOPSIS model is proposed to evaluate the criteria against each alternative. Here, a fuzzy distance measure is used in which the distance from the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated. The resulted distances were used to calculate the similarity to Ideal and Anti-ideal points. Later, an optimal membership degree (closeness coefficient) of each alternative is computed to estimate to which extent an alternative belongs to both FPIS and FNIS. The closer the degree of membership to FPIS and the farther from FNIS the more preferred the alternative. The membership degree is obtained by the optimization of a defined objective function that measures the degree to which an alternative is similar/dissimilar to the Ideal/Anti-Ideal solutions. The closeness coefficient is used to rank the alternatives. To better have a high contrast between the ranks of alternatives an optimization problem was introduced and solved to maximize the contrast. The presented hybrid model was applied on an industrial case study for the selection of cans supplier/suppliers at Nutridar Factory in Amman-Jordan to demonstrate the proposed model. Finally a sensitivity analysis is introduced to verify the resulting ranks of the available suppliers via testing different values of the used parameters. The sensitivity analysis has shown robust and valid results that are close to real preferences of the consulted experts.

HOLE QUALITY IN DEEP HOLE DRILLING

Deep hole drilling represents the most economical method of hole producing with length-to-diameter ratios ≥5. The objective of this study was to ascertain the effect of machining parameters on hole quality produced by the deep hole machining process and to develop a better understanding of the effect of these process parameters on the hole quality. Such an understanding can provide insight into the quality control problems of the holes when the process parameters are adjusted to obtain certain characteristics. This study deals with the experimental results obtained during boring trepanning association (BTA) drilling on medium carbon steel (AISI 1060). The surface roughness, out-of-roundness, and hole size are influenced by cutting speed and feed rate of the deep hole drilling.

Fuzzy logic controller for overhead cranes

Purpose – To propose a new method for controlling the overhead crane systems based on the theory of fuzzy logic with a reduced number of rules than has appeared before in the literature. The proposed fuzzy logic controller (FLC) can be implemented to move the overhead crane along a desired path while ensuring that the payload is swing free at the end of the motion.Design/methodology/approach – In this study, a FLC that includes two rule bases, one for displacement control, the other for swing control, was designed and successfully implemented to move the overhead crane along a desired path while ensuring that the payload is swing free at the end of the motion.Findings – Control simulation results demonstrate that by using the proposed FLC, the overhead traveling crane smoothly moves to the destination in short time with small swing angle and almost no overshoot.Originality/value – This paper offers practical help to whom are working in controlling the transporting the payloads to the required position as ...

Eco-material selection using fuzzy TOPSIS method

Abstract In the classical multiple attribute decision-making or MADM methods, the ratings and the weights of the criteria are known precisely. However, in eco-material selection exercises, the available data are typically inadequate because of the selection dual quantitative and qualitative natures. Some of the qualitative selection criteria can be rated in several classes rather being expressed by exact numerical values; hence the application of fuzzy concepts in decision-making seems attractive to deal with such kind of ratings. Thusly, the presented study attempts to propose an eco-material selection approach specific to the automobile body panels using a fuzzy technique for order preference by similarity to ideal solution (TOPSIS), to incorporate both numerical and rating-based criteria into one holistic sustainability model. TOPSIS and fuzzy logic can aid the material selection process in translating the design goals and parameters into usable numbers that in turn can be used to rank candidate materials in their closeness to the ideal solution. An additional uniqueness of this study stems from using the fuzzy-TOPSIS as a scoring tool without any assigned weights for the different selection attributes, in order to avoid the bias that is typically associated with other classical MADM, such as quality function deployment, analytical hierarchy process and digital logic.

Modeling Surface Finish in End Milling Using Fuzzy Subtractive Clustering-Based System Identification Method

Abstract In this article, fuzzy subtractive clustering-based system identification and a Sugeno-type fuzzy inference system are used to model the surface finish of the machined surfaces in end milling and to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. The surface finish model is identified by using spindle speed, feed rate; and depth of cut as input data. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering-based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (spindle speed, feed rate, and depth of cut). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by employing different sets of inputs. This study deals with the experimental results obtained during end milling on aluminum alloy 390.

Drilling of Al—Mg—Cu alloys and Al—Mg—Cu/SiC composites

Metal matrix composites are widely used in engineering applications including automotive, aircraft, and military industries. In this study, different Al—4 wt%Mg—Cu alloys, and Al—4 wt%Mg—Cu/SiC composites were drilled on a vertical drilling machine using moderate speed and general purpose high-speed steel tools. The machinability parameters studied in this research were drilling forces (torque and thrust force) and surface roughness of the drilled holes. The results showed that the effect of the addition of copper as alloying element (up to 5 wt%) to Al—4 wt%Mg tends to decrease torque and thrust force. Also, it was found that the addition of SiCP (up to 10 vol.%) to the Al—4 wt%Mg—Cu alloys had little effect on the drilling torque and thrust force, but tends to improve the surface roughness of the drilled surfaces. Moreover, the analysis of the produced chips indicates that most of the produced chips by dry drilling of aluminum-based materials were of continuous type.

Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method

In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity defect and surface defects as the output data. The process of model building is carried out by using subtractive clustering in both the input and output spaces. A minimum error model is developed through exhaustive search of clustering parameters. The fuzzy model obtained is capable of predicting the tile defects for a given set of inputs as mentioned above. The fuzzy model is verified experimentally using different sets of inputs. This study intends to examine and deal with the experimental results obtained during various stages of ceramic tile production during 90-day period. It is believed, that the results obtained from the present study could be considered in other ceramic tiles industries, which experienced similar forms of defects.

A modified PID controller (PIIσβD)

Abstract The purpose of this study is to modify the traditional PID controller in order to improve its performance (stability and tracking) by changing the length of integration interval. The performance of the traditional PID controller was improved by changing the length of integration interval to make the most of the returns of the PID and PI σ D controllers. The asymptotic stability domain, in terms of the feedback gains, is derived for systems of second order using the modified controller which will be identified as PII σ β D . Comparing this controller with the traditional PID controller and PI σ D controller proposed in [1] , it proves that it is more accurate and more stable. For illustration and comparison, two examples have been simulated to evaluate the performance of the modified controller. All simulation results indicate that the modified controller is better than the traditional PID controller and the PI σ D controller from the accuracy and stability point of view.

The effect of the increase in graphite volumetric percentage on the strength and hardness of Al-4 weight percent Mg-graphite composites

In this study, the fabrication of an Al-4 wt.% Mg-graphite particle composite is described. Composites of Al-4 wt.% Mg alloys containing 1–10 volume percentages of graphite particles were prepared using the compocasting technique. A pitched-blade stirrer was used to stir the graphite particles in the semi-solid melt, and the slurry was then poured into a metallic mould to obtain the cast bars. The emphasis of the investigation was on the important features of the castings obtained by this method, specifically the distribution of graphite particles along the cast bars and porosities. Then we studied the effect of the addition of the graphite on the strength and hardness of these cast bars. The results show that both the tensile strength and the hardness decrease with the increase in graphite content.

The effect of graphite particles addition on the surface finish of machined Al-4 Wt.% Mg alloys

Metal matrix composites (MMCs) are a new class of materials finding various applications, especially in transport industries. Compocasting technique was used in the present study to produce aluminum, magnesium, and graphite (MMCs) cast bars. These cast bars were machined to investigate the effect of graphite addition on the surface finish. The obtained results show that the addition of graphite particles to the aluminum and magnesium alloys produce slight improvement on the machined surface finish, for all the percentage of graphite additions considered.