Issam S. Jalham

Modeling capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base metal matrix composites

Abstract The aim of this paper is to investigate the capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base Metal Matrix Composites by comparing the results of the ANN predictions to the results of predictions by the RBF approach in our previous work. The experimental results of the hot deformation of 12 vol.% Al 2 O 3 Aluminum matrix composites under a range of temperatures and a range of strain rates were used in this investigation to be able to recognize the powerfulness of ANN against the RBF approach. The results showed that the filtrated ANN approach gives better results than RBF approach.

Joining of sialon ceramics by a stainless steel interlayer

In direct diffusion bonding of sialon to stainless steel, thermal residual stresses arise due to the difference in coefficient of thermal expansion of the two materials. These stresses frequently lead to failure of the bond. This behaviour is further influenced by the formation of interfacial reaction layers between ceramic and metal and the problem is essentially one of asymmetry of stresses in the interface between dissimilar materials. The present study demonstrates that a thin layer of austenitic stainless steel can be used as an interlayer to join two sialon components. In such a case the distribution of residual stresses is symmetrical across the composite join and provided that the thickness of the steel layer is less than a critical value, then fracture on cooling from joining temperature does not occur. The development of this process is described and a finite-element model has been used to predict the properties of the interfacial reaction layer between steel and ceramic which are consistent with the experimental observations.

Decision-making integrated information technology (IIT) approach for material selection

In this investigation, a decision-making IIT approach for materials selection has been proposed. This approach consists of three phases which are the preselection, selection, and postselection and techno-economic phases. This approach differs from other approaches in taking into consideration the economic and business issues factors. Going from one phase to another, a number of the candidate materials are eliminated step by step until the optimum material is reached. This approach was also translated into a computer programme which consists of the complementary work of the modules that have screening properties. A case study was presented to test the proposed model and good results have been obtained.

Factors influencing the repurchase intention and customer satisfaction: a case of Jordanian telecom companies

Todays harsh competition has forced telecom companies to continually enhance customer satisfaction, which leads to repurchases intention and consequently larger market share. Determination the key success factors in gaining competitive advantage within telecom industry has received significant research attention in different countries. This research examines the relationships between service quality, customer satisfaction, corporate image, perceived value, perceived price, loyalty, trust and checking their effects on repurchase intentions in Jordanian telecom companies using structural equation modelling. A structural model of 20 hypotheses is built. The data was collected from 550 users of telecom service. Results reveal that customer satisfaction is positively affected by service quality, perceived value, perceived price and image. Moreover, repurchases intention is positively affected by customer satisfaction, loyalty and perceived value. In conclusion, customer satisfaction is the key factor to be considered if telecom companies are willing to survive in todays harsh competition.

A structural equation model and a statistical investigation of lean-based quality and productivity improvement

In response to rapid expansion rate of technological change and information technology, and increasing customer demands, Next Generation Manufacturing Systems (NGMS) and Fit Production Systems (FPS) are emerged. Lean Production (LP) covers a wide range of production concepts and philosophies that can be classified as NGMS and/or FPS. In this paper, LP is introduced, defined, and reviewed, important aspects when implementing such production strategy is addressed, and practices of LP are mapped into different impact areas of production, among these “Quality and Productivity Improvement (QPI)” impact area. QPI may involve eight lean practices these are: Variability Reduction (VR), Visual Control (VC), Poka Yoke (PY), Quality at the Source (QS), Kaizen (K), Five S (5S), Root Cause Analysis (RCA), and Total Quality Management (TQM). The purpose of this article is to provide bottomless insights into the adoption of these eight constructs and their effect on lean based QPI model. A Structural Equation Model (SME) is constructed. Hypotheses are proposed, Five-Points-Likert scale questionnaire is distributed by hand to 450 different firms. Feedback from 300 firms is collected then statistically analyzed using SPSS 15.0, AMOS 19.0.0, and LISREL 8.8 software. Implementation level (I %) of the eight lean practices in the selected sample is evaluated, multicollinearity, reliability and internal correlations matrix to insure content validity, consistency, and construct validity respectively are applied, fit indices are derived, one way ANOVA analysis is performed, and finally hypothesized causal relationships between model constructs are concluded, the results provide strong evidence that all the considered lean practices has a positive effect on lean-based QPI model, these findings can be fruitfully utilized to cope over the challenges facing the implementations of LP production strategy in industry.

Testing and Evaluation of Rubber-base Composites Reinforced with Silica Sand

In this study, natural rubber (NR) was reinforced with the unique composition of Jordanian silica sand at different loadings (10, 30, 50, and 75 pphr) and different particle sizes (10, 25, 45, and 75 μm). Several tests, such as hardness, tensile strength, elongation at break, compression set, compression stress-strain, and density are conducted to evaluate this composite according to the international standards. The results showed that, although the particle size for each loading is directly proportional to the hardness, the worst results are observed for the products of 75 pphr loading and 75 μm particle size. The tensile strength is inversely related to the particle size of the filler and gives the highest strength when filling with 10 pphr among other filling loadings. The elongation at break of natural rubber reduced by increasing the loading and decreasing the particle size. The results of compression set showed that the best behavior was for the product of 10 pphr and 10 μm particle size. The compression stress is directly related to the loading, especially for small particle sizes. The elastic modulus increased with the increase of loading and the increase of particle size. The density test showed that the silica filler has no significant increase in the density of rubber especially for small particle size fillers. Comparing the studies of properties of the manufactured rubber-base composites in this investigation with those achieved in other research works, it is found that they are superior especially for hardness, tensile strength, and compression set properties and density.

Application of the Taguchi approach to improve performance of the alignment-layer printing process in liquid crystal displays

The uniformity of the alignment-layer thickness on indium tin oxide glass plays an important role in producing liquid crystal display panels of quality images. However, thickness non-uniformity results in producing liquid crystal display panel of poor color image, which increases rework cost and scrap. The Taguchi method is utilized in the define–measure–analyze–improve–control approach to improve the capability of this process. In the define phase, process mapping and quality characteristic definition are discussed. The x̄ and R control charts and process capability analysis are employed in the measure phase. The L18 array and signal-to-noise ratio, followed by analysis of variance, are utilized in the analyze phase. In the improve phase, two-step optimization is conducted to reduce thickness variations and adjust process mean on target. The control charts for the thickness are regularly constructed in the control phase. Initially, the estimates of the capability indices for the alignment-layer printing process are 0.731 and 0.69, respectively, which indicate that this process is incapable. Utilizing the Taguchi method, confirmation experiments showed that the potential and actual capability indices are respectively improved to 3.48 and 3.47. Hence, the process becomes highly capable. This proves the effectiveness of the Taguchi method in improving the performance of the alignment-layer printing process.

Improvement of organizational efficiency and effectiveness by developing a manufacturing strategy decision support system

Purpose – To develop a model for manufacturing strategy formulation and implementation to help the managerial body of a corporation improve the efficiency and effectiveness of their company.Design/methodology/approach – The quality function deployment (QFD) technique is used in the formulation process to provide the basis for selection between options in each of the formulation stages. The proposed model consists of six stages starting from the business strategy formulation, functional strategy formulation, manufacturing priorities formulation, the generation of action plans and the suggestions of the detailed tasks of each action plan, and ending up with the evaluation of the developed strategy. QFD capture software is used to form the matrices needed for the proposed model.Findings – It was found that the proposed QFD approach is easy to apprehend, clear to be applied and leads to dependable manufacturing strategy. Accordingly, this model is recommended to be used in other functional areas in addition t...

Computer-aided quality function deployment method for material selection

A computing algorithm is suggested to computerise the quality function deployment (QFD) method, which is used for product planning development and manipulated so that it would be suitable for selecting the optimum material for a certain application. The need for this computerised method is to avoid the complexity when there is a large number of candidate materials. It can be claimed that this method can substitute the existing weighted properties method, which has a misleading approach and lacks the systematic sequence. A simple example is presented to demonstrate this method. It was found that this computerised version of the QFD method is a good and helpful tool to select the optimum material.

A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The statistical regression is formulated for the signal to noise S/N ratios of each response replicate. Then, the optimal factor levels for each replicate are utilized in building fuzzy regression model. The desirability function, pay-off matrix, and the deviation function are finally used for formulating the optimization models for the lower, mean, and upper limits. Two case studies investigated in previous literature are employed for illustration; where in both case studies the proposed approach efficiently optimized processes performance.