Mohd Idris Shah Ismail

Bio: Mohd Idris Shah Ismail is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Welding & Tool wear. The author has an hindex of 13, co-authored 69 publications receiving 484 citations. Previous affiliations of Mohd Idris Shah Ismail include Okayama University & Universiti Tun Hussein Onn Malaysia.

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Abstract: Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multi-response, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview of topics with brief systematic review and concise discussions about the recent development of comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors’ policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

Analyzing the effect of nozzle diameter in fused deposition modeling for extruding polylactic acid using open source 3D printing

Abstract: Fused deposition modeling (FDM) is one of the Rapid Prototyping (RP) technologies. The 3D Printer has been widely used in the fabrication of 3D products. One of the main issues has been to obtain a high quality for the finished parts. The present study focuses on the effect of nozzle diameter in terms of pressure drop, geometrical error as well as extrusion time. While using polylactic acid (PLA) as a material, the research was conducted using Finite Element Analysis (FEA) by manipulating the nozzle diameter, and the pressure drop along the liquefier was observed. The geometrical error and printing time were also calculated by using different nozzle diameters. Analysis shows that the diameter of the nozzle significantly affects the pressure drop along the liquefier which influences the consistency of the road width thus affecting the quality of the product’s finish. The vital aspect is minimizing the pressure drop to be as low as possible, which will lead to a good quality final product. The results from the analysis demonstrate that a 0.2 mm nozzle diameter contributes the highest pressure drop, which is not within the optimum range. In this study, by considering several factors including pressure drop, geometrical error and printing time, a 0.3 mm nozzle diameter has been suggested as being in the optimum range for extruding PLA material using open-source 3D printing. The implication of this result is valuable for a better understanding of the melt flow behavior of the PLA material and for choosing the optimum nozzle diameter for 3D printing.

Recent developments in metamodel based robust black-box simulation optimization: An overview

Abstract: In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

Experimental Investigation on Micro-Welding of Thin Stainless Steel Sheet by Fiber Laser

Abstract: Problem statement: The miniaturization of components plays an important role for manufacturing in electrical and electronic industries. The joining technology of thin metal sheets has been strongly required. Laser welding with micro-beam and high-speed scanning is a promising solution in micro-welding, because it has high-potential advantages in welding heat sensitive components with precise control of heat input and minimal thermal distortion. Approach: In this study, the characteristics of laser micro-welding of thin stainless steel sheets by using a single-mode CW fiber laser with high-speed scanning system were experimentally investigated. Results: It was clarified that the welding bead width and depth increased with increasing the scanning velocity under a constant energy density condition and high efficient welding was expected by using high-speed laser scanning with Galvano scanner. The utilization of shielding gas was very effective to obtain smooth fusion bead and the combination of micro beam spot and high-speed laser scanning made it possible to obtain good overlap welding of ultra-thin stainless steel sheets. Conclusion: A faster and high quality welding could be achieved by using a single-mode fiber laser with micro-beam and high-speed scanning.

Neural Network Modeling for Prediction of Weld Bead Geometry in Laser Microwelding

Abstract: Laser microwelding has been an essential tool with a reputation of rapidity and precision for joining miniaturized metal parts. In industrial applications, an accurate prediction of weld bead geometry is required in automation systems to enhance productivity of laser microwelding. The present work was conducted to establish an intelligent algorithm to build a simplified relationship between process parameters and weld bead geometry that can be easily used to predict the weld bead geometry with a wide range of process parameters through an artificial neural network (ANN) in laser microwelding of thin steel sheet. The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. The accuracy of neural network model has been tested by comparing the simulated data with actual data from the laser microwelding experiments. The predictions of the neural network model showed excellent agreement with the experimental results, indicating that the neural network model is a viable means for predicting weld bead geometry. Furthermore, a comparison was made between the neural network and mathematical model. It was found that the developed neural network model has better prediction capability compared to the regression analysis model.

Global optimization and simulated annealing

Abstract: In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of Rn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematic formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature. Keywords: global optimization, continuous variables, simulated annealing.

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete.

Abstract: Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model's performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R² = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R² = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

Laser Material Processing

Abstract: Processing of Laser Materials have a variety of industrial operations in which work piece is modified by laser operation, for example, by melting it or removing material from it. In recent years, laser based technologies became important or even dominant in industrial applications such as welding, cutting or drilling. Manufacturing technology will rely on lasers and laser-based material processing for the development of new material processing methodologies and multi-functional device integration solutions. Further possibilities of processing, innovation, and advancement of laser material treatments are still in progress and very challenging. The very broad field of laser materials processing is still very fast developing. To extend this developing field of material processing, a well-organised database can give a noble guideline and great reference. That can be used as fundamentals for any research or it may help industry to choose right type of laser for specific techniques.