Papers by Professor Mohamed Hassan Mehany H . Gadallah
20th Design Automation Conference: Volume 1 — Dynamic Mechanical Systems; Geometric Modeling and Features; Concurrent Engineering, 1994
A new algorithm for manufacturing tolerance synthesis is proposed. In complex mechanical assembli... more A new algorithm for manufacturing tolerance synthesis is proposed. In complex mechanical assemblies, most tolerance analysis and synthesis methods tend to be impractical. In this article, a methodology is proposed to minimize manufacturing cost by tightening important dimensional and/or geometrical tolerances. Analysis of variance and experimental design techniques are used to discriminate among various design dimensions to the overall functional requirement of the mechanical assembly. In this case, some, but not all, design dimensions will be controlled. This paper reviews the state-of-the art in the area of tolerance analysis and synthesis and highlights the contribution of this work.
World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, Mar 30, 2016
Transactions of The Canadian Society for Mechanical Engineering, Jun 1, 2002
Design, modeling, analysis, and optimization are steps necessary in production engineering today.... more Design, modeling, analysis, and optimization are steps necessary in production engineering today. In this paper, the flat end milling process is studied and modeled using a new approach. An Artificial Neural Network (ANN) Based model is developed that takes inputs in the form of cutting conditions and tool geometry parameters. Initially, three cutting parameters are varied in an arbitrary way; these are feed rate, spindle speed, and radial depth of cut. The inclusion of all cutting parameters can be done but would result in a dramatic increase in the number of experiments and cost of training. Accordingly, statistical design of experiments (D.E.O.) is employed to allow the change of variables using fractional factorial design (FFE), Several ANN models are developed from L90A, L270A, L27 OA with extended parameters ranges and L36 OA (9, 27, 27 (extended) and 36 experiments) arrays and the corresponding ANN models are compared with respect to accuracy of prediction. Nine confirmation experiments are carried...
<jats:title>Abstract</jats:title> <jats:p>In this paper, a new statistical opti... more <jats:title>Abstract</jats:title> <jats:p>In this paper, a new statistical optimization technique is proposed. The technique employs new variance reduction schemes (VRTs). The performance of three standard designs: L27/L27 OA, L54/L27 OA and L243 / L27 OA are studied. These designs, although both orthogonal and balanced, exhibit high variance reduction properties with questionable convergence in very short number of iterations. Four new composite designs are developed, implemented and compared with the standard ones. These designs are known as: 5-, 7-, 9- and 11-point composite L27 OA. The problem of tolerance allocation with optimal process selection is revisited as a case study for simulation. Results indicate the efficiency of these new designs to reduce variances to lower levels than standard designs and better convergence in fraction of experiments. These designs are then integrated in an optimization algorithm previously developed (Gadallah, M.H., 2000). The algorithm is then modified to deal with the least sensitive optimal solutions for standard and composite designs. Particularly, the parameters that affect the algorithm are varied and their effects on performance of algorithm are studied. A standard manufacturing case study is used for analysis and simulation results for the composite designs are also given.</jats:p>
Transactions of the Canadian Society for Mechanical Engineering, 2002
In this study, a new idealization to the exhaustive search optimization problem is given, Three f... more In this study, a new idealization to the exhaustive search optimization problem is given, Three formulations are given to a design-engineering problem using standard arrays: these are L243/ L27 OA, L27/ L108 OA and L81/ L108 OA and their sub-families. We found that certain idealizations, though realistic are still considered NP hard problems. As a remedy, a new exhaustive sequential algorithm is developed that solves NP hard problems in n sequential stages. Both the deterministic and statistical solutions are given and conclusions are drawn regarding their convergence properties. Composite arrays are developed out of the standard ones and simulations results indicate that certain composite arrays have better variance and convergence properties than other standard ones. These results are considered as part of the authors’ work to develop efficient statistical optimization techniques. Review of optimization related literatures indicates the strong need for development of global optimization techniques. A st...
Volume 11: Systems, Design, and Complexity, 2016
Tolerance allocation is a necessary and important step in product design and development. It invo... more Tolerance allocation is a necessary and important step in product design and development. It involves the assignment of tolerances to different dimensions such that the manufacturing cost is minimum, while maintaining the tolerance stack-up conditions satisfied. Considering the design functional requirements, manufacturing processes, and dimensional and/or geometrical tolerances, the tolerance allocation problem requires intensive computational effort and time. An approach is proposed to reduce the size of the tolerance allocation problem using design of experiments (DOE). Instead of solving the optimization problem for all dimensional tolerances, it is solved for the significant dimensions only and the insignificant dimensional tolerances are set at lower control levels. A Genetic Algorithm is developed and employed to optimize the synthesis problem. A set of benchmark problems are used to test the proposed approach, and results are compared with some standard problems in literature.
Ahmad et al.-2007-Opt imizat ion of Mult ipass Turning Paramet ers by GA-ANN based Hybrid … Nafis... more Ahmad et al.-2007-Opt imizat ion of Mult ipass Turning Paramet ers by GA-ANN based Hybrid … Nafis Ahamd Art ificial neural net work based on genet ic learning for machining of polyet heret herket one composit e … carlos ant onio
This Paper provides a new approach to handle constrained multi objective problems using a modific... more This Paper provides a new approach to handle constrained multi objective problems using a modification to the fast elitist multi objective Genetic Algorithm, NSGA II (Non-dominated Sorting Genetic Algorithm II). The modification overcomes shortcomings of some previous approaches to handle constrained multi objective problems like Penalty Function Approach, Jimenez-Verdegay-Gomez-Skarmeta&#39;s approach, and Ray-Tai-Seow&#39;s Method. Comparative studies are conducted using a group of quality indices [2,19] to compare among three different algorithms: regular NSGA, NSGA-II with penalty function and M-NSGA-II with constraint handling techniques. Results show that the new modification is the best among the three approaches. Due to limited space, only recent studies on the subject of multi objective optimization are reviewed for brevity.
Volume 11: Systems, Design, and Complexity, 2016
Tolerance allocation is a necessary and important step in product design and development. It invo... more Tolerance allocation is a necessary and important step in product design and development. It involves the assignment of tolerances to different dimensions such that the manufacturing cost is minimum, while maintaining the tolerance stack-up conditions satisfied. Considering the design functional requirements, manufacturing processes, and dimensional and/or geometrical tolerances, the tolerance allocation problem requires intensive computational effort and time. An approach is proposed to reduce the size of the tolerance allocation problem using design of experiments (DOE). Instead of solving the optimization problem for all dimensional tolerances, it is solved for the significant dimensions only and the insignificant dimensional tolerances are set at lower control levels. A Genetic Algorithm is developed and employed to optimize the synthesis problem. A set of benchmark problems are used to test the proposed approach, and results are compared with some standard problems in literature.
Development of involved optimization algorithms is not an easy task for several reasons: First, e... more Development of involved optimization algorithms is not an easy task for several reasons: First, every analyst is interested in a specific problem; Second, the capabilities of these methods may not be fully understood a priori; Third, coding of multi-purpose and more involved algorithms is not an easy job. In this paper, the optimization problem employing the near to global optimum algorithm is studied (Gadallah, M.H., 2000). The focus is to exploit 2 ideas: First, the algorithm can be modified to act as a variance reduction technique; Second, the algorithm can be modified to tackle the problem of system decomposition. Both ideas are novel within the context of statistical design of experiments. The first, if fully proved experimentally could yield the simultaneous integration of nominal and variance optimization possible. T he second, can be extended to deal with multi-dimensional highly constrained systems with ease. These two ideas are explained with the use of a simple example to...
The International Journal of Advanced Manufacturing Technology
Titanium alloys are the primary candidates in several applications due to its promising character... more Titanium alloys are the primary candidates in several applications due to its promising characteristics, such as high strength to weight ratio, high yield strength, and high wear resistance. Despite its superior performance, some inherent properties, such as low thermal conductivity and high chemical reactivity lead to poor machinability and result in premature tool failure. In order to overcome the heat dissipation challenge during machining of titanium alloys, nano-cutting fluids are utilized as they offer higher observed thermal conductivity values compared to the base oil. The objective of this work is to investigate the effects of multiwalled-carbon nanotubes (MWCNTs) cutting fluid during cutting of Ti-6Al-4V. The investigations are carried out to study the induced surface quality under different cutting design variables including cutting speed, feed rate, and added nano-additive percentage (wt%). The novelty here lies on enhancing the MQL heat capacity using nanotubes-based fluid in order to improve Ti-6Al-4V machinability. Analysis of variance (ANOVA) has been implemented to study the effects of the studied design variables on the machining performance. It was found that 4 wt% MWCNTs nano-fluid decreases the surface roughness by 38% compared to the tests performed without nano-additives, while 2 wt% MWCNTs nano-fluids improve the surface quality by 50%.
Manufacturing Review, 2015
Geometric Design Tolerancing: Theories, Standards and Applications, 1998
Proceedings of the Fourth International Conference on Computer Integrated Manufacturing and Automation Technology, 1994
deviations determine the parts acceptance ar rejection. In A new algorithm for form tolerance eva... more deviations determine the parts acceptance ar rejection. In A new algorithm for form tolerance evaluatwn has been developed. Evaluation of the minimum tolerance wne is formulared as an optimizatwn pmblem following the definitions of geometric tolerances in the current ANSI srandanls. The algorithm utilizes the experimental optimization techniques and the combinatorial nature of orthogonal arrays to plan the experimentation and evaluate the minimum tolerance wnt. The approach is applied to 2-dimemwnal features tolerances such as straightness and circular@ (roundness) and 3-dimensional features such asjlutness. The obtained results are compared with other approaches wing the Luast Square method, the constrained optimization techniques and the Convex Hull approach,
International Journal of Machining and Machinability of Materials, 2010
Int. J. Machining and Machinability of Materials, Vol. 8, Nos. 1/2, 2010 ... Intelligent process ... more Int. J. Machining and Machinability of Materials, Vol. 8, Nos. 1/2, 2010 ... Intelligent process modelling using radial basis neural network ... Department of Operations Research, Institute of Statistical Studies and Research, Cairo University, 5 Tharwat Street, Orman, Dokki, Giza, 12613 ...
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Papers by Professor Mohamed Hassan Mehany H . Gadallah
He is interested to develop Technical Education based on Competencies. Accreditation of new Educational Programs is another interest.
The tolerance analysis and allocation problem can be reduced. This concept is based on the hypothesis that not all dimensions have the same importance (significance) to the overall functional requirements. Design of experiments (DOE) is performed and the responses are analyzed using Analysis of Means (ANOM) to signify the important dimensions (parameters), and such dimensions are controlled. Thus, each tolerance problem is reduced by the number of controlled dimensions.
Tolerance analysis methods predict the yield (percentage of accepted assemblies) of the proposed tolerances. When the yield is below a desired value, dimensional and/or geometric tolerances are tightened in order to achieve higher yield values; thus, increasing the manufacturing cost significantly. Instead of tightening all dimensional and/or geometrical tolerances, only the significant tolerances are tightened, while insignificant tolerances are relaxed. A set of benchmark problems are used to test the proposed approach. Monte Carlo simulation is employed to predict the yield of these benchmark problems. The proposed approach produced higher yield values.
Tolerance allocation is a necessary and important step in product design and development. It involves the assignment of tolerances to different dimensions such that the manufacturing cost is minimized, while maintaining the tolerance stack-up conditions satisfied. Considering the design functional requirements, manufacturing processes, and dimensional and/or geometrical tolerances, the tolerance allocation problem requires intensive computational effort and time. Two approaches are proposed and investigated to reduce the size of the tolerance allocation problem using design of experiments (DOE). In the first approach, the insignificant dimensions are assigned tolerances and their corresponding manufacturing processes are selected; thus, reducing the tolerance allocation problem. In the second approach, tolerances for the significant dimensions are assigned and their suitable manufacturing processes are selected; hence, the problem is reduced for the insignificant dimensions only. In these two approaches, the controlled dimensions are set at different control levels and the reduced tolerance allocation problem is solved using Genetic Algorithms (GA). A set of benchmark problems are used to test the proposed approaches, and results are compared with some standard problems in literature. The first approach showed superior performance than the second approach, especially, at low control levels. This is due to the fact that tightening tolerances of the insignificant dimensions will avail the functional requirement allowance for further relaxation of the significant dimensions. This in return reduces the total manufacturing cost.
Taguchi's parameter design is a systematic approach to optimize process performance, quality and cost. Laser beam cutting (LBM) is a non-traditional machining process widely used for cutting, drilling, marking, welding, sintering,
and heat treatment. The objective of this study is to apply Taguchi optimization methodology to optimize Laser beam cutting parameters of Stainless steel (316L) to achieve
optimal Average Kerf Taper (Ta), Surface Roughness (Ra) and Heat affected zone (HAZ). A series of experiments are conducted using (LBM) to relate machining parameters to several quality responses. Analysis of variance
(ANOVA), Analysis of mean (ANOM), Orthogonal array (L27OA) and signal to noise ratio are employed to analyze the influence of process parameters. The machining parameters are machining on power (Watt), oxygen pressure (MPa),
pulse frequency (Hz) and cutting speed (cm/min). Another objective is to build mathematical models for average kerf taper and average surface roughness as function of significant process parameters using Response Surface Methodology (RSM).
Experimental results for both S/N ratio and mean response values show that power, oxygen pressure, and cutting speed are the most significant parameters that influence Kerf taper at confidence levels 99%, 95%, and 90% respectively.
On the other hand, power and oxygen pressure are the significant parameters that influence average surface roughness at confidence levels 99%95%, and 90% respectively, consequently both the power and pressure of oxygen are the criteria that affect the impact of the heat affected zone at confidence levels 99%, 95%, and 90% respectively. RSM models are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective problem subject to process constraints. Models are formulated based on
Analysis of Variance (ANOVA) via optimization toolbox MATLAB. Optimum solutions are compared with Taguchi Methodology results.
Further validation experiments are carried to verify developed models with success.