Use In Design Optimization Research Articles

Positioning accuracy prediction of precision cycloid reducer based on a BDDTE model and use in design optimization

As users' expectations for precision cycloid reducer positioning accuracy rise, accurate positioning accuracy prediction of products before assembly and design solutions to improve product qualification rates are very important. Considering the actual manufacturing conditions of components, a new multi-source error (MSE) equivalence modeling assumption and pin gear's MSE measurement method are proposed, and the rationality of the assumption is verified through experiments. Based on the geometric characteristics of each component's MSEs, a new analytical model of bi-directional drive transmission error (BDDTE) is established, which organically unifies the prediction of transmission error (TE) and lost motion (LM). The influence of the output mechanism on the positioning accuracy of the entire reducer is found to be non-negligible through Monte Carlo simulation; meanwhile, the general tolerance allocation scheme makes it difficult to ensure that each prototype meets the performance requirements. For this reason, a new design solution for the positioning accuracy of the reducer with the pin gear and output mechanism as the optimization targets is proposed. Finally, the prototype test results show that the BDDTE model based on the new assumption has a higher accuracy in predicting positioning accuracy compared with the model based on the conventional assumptions. Meanwhile, the effectiveness of the proposed improvement design solution for positioning accuracy improvement is verified. The BDDTE model reveals the intrinsic connection between the TE and LM of the cycloid reducer, and the improvement solution has guiding effects on improving the product qualification rate.

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review.

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

Multibranch Inductance Extraction Procedure for Multichip Power Modules

This article presents a measurement-based technique for estimating the parasitic inductances associated with the interconnection structures of a multichip power module (MCPM) at a finer granularity than has been previously demonstrated. The technique introduced here makes it possible to determine an estimate of the interconnect inductances at each individual die position within the module geometry. For this purpose, this technique leverages the measured input impedance of an MCPM in the frequency domain, which has been carefully shaped by employing discrete capacitors in place of a semiconductor die. An example of the proposed technique is provided for an MCPM with four die positions per switch position. The provided theoretical formulation is verified by its application to a physical test subject, which is designed to represent a generic MCPM. Owing to the efficiency of this extraction technique and the possibility of providing direct empirical validation for FEA-obtained estimates, the proposed approach is expected to be of particular interest to MCPM designers for use in design optimization.

The Use of Genetic Optimization for Design of Small Diesel-Battery Submarine

Design of a Small Diesel-Battery Submarine (SDBS) has been carried out using Multi-Objectives Genetic Algorithm optimization combined with Analytical Hierarchy Process to meet the operation requirements. The objective attributes for this optimization are cost and effectiveness. The product of this optimization is a series of cost-effectiveness frontiers in the scattered graph which are used to select alternative designs based on the Indonesian Navy's preference. It is found that the Length of the submarine is 28 m with Diameter 4 m. The sprint power of 20 knots determines the motor power while the endurance power of 5 knots determines the battery capacity.

Design optimization of an accumulator for reducing rotary compressor noise

This article deals with the optimization of an accumulator to reduce rotary compressor noise by maximizing the total transmission loss of an accumulator subjected to constraints on lower bounds of local transmission loss values. Transmission loss of an accumulator is described using the Helmholtz resonator theory to obtain the compressor noise characteristics. An experimental analysis of an accumulator is performed to identify the noise characteristics, and numerical data for use in design optimization are then obtained based on the Helmholtz resonator theory. Dominant design variables are selected via analyses of means, and are verified by back-propagation neural network based causality analysis. The back-propagation neural network is used to establish global approximate meta-models to accommodate the inherent non-linearity and multi-modality in the accumulator model. The genetic algorithm is employed as a global optimizer to find optimized accumulator designs in the context of approximation optimization.

The epistemic implications of engineering rhetoric

The texts (and talk) of engineers take different forms. In this essay, I present and critique several texts written for different purposes and audiences but all intended to convey to the reader the technical details of whatever they are about—whether a textbook passage describing the fundamental behavior of an electrical component, a journal article about a mathematical technique intended for use in design optimization, a memo to co-workers within a firm about a heat transfer analysis of a remotely sited building, or a general introduction to the field of? ergonomics? My aim is to explore how the ways in which engineers describe and document their problems and projects frame what they accept, display and profess as useful knowledge. In this I am particularly interested in how engineers envision the ‘users’ of, or participants in, their productions. Like science, engineering texts are written as if they were timeless and untainted by socio-cultural features. However, a technical treatise is not devoid of metaphor or creative rendering of events; there is always a narrative within which worldly data and instrumental logic is embedded—but it is a story in which the passive voice prevails, history is irrelevant, and the human actor or agent is painted in quantitative parameters fitting the occasion. Whether this rhetoric can be sustained in the face of challenges to traditional ways of doing engineering is an open question.

Shock wave and detonation propagation through U-bend tubes

The objective of the research outlined in this paper is to provide experimental and computational data on initiation, propagation, and stability of gaseous fuel–air detonations in tubes with U-bends implying their use for design optimization of pulse detonation engines (PDEs). The experimental results with the U-bends of two curvatures indicate that, on the one hand, the U-bend of the tube promotes the shock-induced detonation initiation. On the other hand, the detonation wave propagating through the U-bend is subjected to complete decay or temporary attenuation followed by the complete recovery in the straight tube section downstream from the U-bend. Numerical simulation of the process reveals some salient features of transient phenomena in U-tubes.

Applications of soft computing techniques in response surface based approximate optimization

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

Optimal design of a composite leaf spring using genetic algorithms

A formulation and solution technique using genetic algorithms (GA) for design optimization of composite leaf springs is presented here. The suspension system in an automobile significantly affects the behaviour of vehicle, i.e. vibrational characteristics including ride comfort, directional stability, etc. Leaf springs are commonly used in the suspension system of automobiles and are subjected to millions of varying stress cycles leading to fatigue failure. If the unsprung weight (the weight, which is not supported by the suspension system) is reduced, then the fatigue stress induced in the leaf spring is also reduced. Leaf spring contributes for about 10–20% of unsprung weight. Hence, even a small amount of weight reduction in the leaf spring will lead to improvements in passenger comfort as well as reduction in vehicle cost. In this context, the replacement of steel by composite material along with an optimum design will be a good contribution in the process of weight reduction of leaf springs. Different methods are in use for design optimization, most of which use mathematical programming techniques. This paper presents an artificial genetics approach for the design optimization of composite leaf spring. On applying the GA, the optimum dimensions of a composite leaf spring have been obtained, which contributes towards achieving the minimum weight with adequate strength and stiffness. A reduction of 75.6% weight is achieved when a seven-leaf steel spring is replaced with a mono-leaf composite spring under identical conditions of design parameters and optimization.

Application of classifier systems in improving response surface based approximations for design optimization

Emergent computing paradigms, such as genetic algorithms and neural networks have found increased use in problems of engineering design. These computational tools have been shown to be applicable in providing fast function approximations, in identifying causality in numerical data, and in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the broad subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert systems, and machine learning. The present paper explores the use of a machine learning paradigm, the central building blocks of which are tools, such as genetic algorithms and neural networks. Such learning systems have received some attention in the field of computer science, where they have been referred to as classifier systems; the paper discusses the significance of this approach in the problem of constructing high-quality global approximations for subsequent use in design optimization.

Integrated Product and Process Development: Methods, Tools and Technologies

From the Publisher: Emphasizing applications, this extremely user-friendly guide covers everything from basic principles to cutting-edge research. It addresses ideas and methods in product design as well as issues related to process design and manufacturing. Case studies illustrate the application of various tools and techniques of IPPD in manufacturing for the defense industry, making the most of product planning, applications of quality function deployment (QFD), the effective use of design optimization, and integrating design and process planning.

Recent progress in aerodynamic design optimization

Recent emphasis on reduction of design cycle time and cost in the design of commercial aircraft (P.E. Rubbert, ‘CFD and the changing world of airplane design’, AIAA Wright Brothers Lecture, September, 1994) has sparked a renewed interest in design optimization in aerodynamics, structures and aeroelastics. In this paper, recent developments in the use of design optimization in aerodynamics using the TRANAIR code are considered. Globalization techniques and the extension of the methodology to multipoint design will be discussed. Copyright © 1999 John Wiley & Sons, Ltd.

Analytical investigation of active control of radiated inlet fan noise

An analytical model has been developed to study the potential of active noise control techniques for reducing radiated tonal inlet noise from turbofan engines. The analytical model consists of multiple control sources placed in the periphery of the engine inlet duct which inject antinoise into the duct to destructively interfere with the tonal sound field generated by the fan. The development of the analytical expressions of the radiated sound fields due to both the disturbance and the control sources is based on duct modal expansion. Attenuation of the radiated sound field is accomplished using a multiple-channel feedforward control approach. Control signals are calculated to minimize the pressure disturbance at desired error sensor locations and are then used to determine the controlled radiated sound field. The model is used to evaluate the performance of the control system for single and multiple circumferential arrays of control sources. The model can be extended for use in design optimization. Sample results are presented to illustrate the predicted sound attenuations.

Thermal modeling of zone-melting-recrystallization processing of silicon-on-insulator film structures

A numerical simulation model that considers changes in thermal and radiation properties during zone-melting-recrystallization processing of silicon-on-insulator film structures with a graphite strip heater was developed. A series of numerical experiments were conducted to investigate the conductive, convective, and radiative parameters affecting the process. The variability of the thermal conductivity of silicon as a function of temperature at the premelting stage and the variation in thermal conductivity due to the multilayer nature of the structure were found not to affect of the temperature distribution during processing. The convective heat losses induced by the inert gas in the processing chamber does not affect significantly the temperature profiles in the film structure, but can reduce the melt zone width by 10%–25%. The study of radiative effects focused on the effects of varying the width and height of the graphite strip and the distance between the graphite strip and the film structure. These parameters significantly affect the temperature distribution and the width of the melt zone. The relationships between the convection and radiation parameters and the resultant melt zone width are presented in graphical form for use in design optimization.

The Center Design Optimization System

CENTER is a software system based on the UNIX® operating system used to optimize integrated circuit designs and semiconductor device technologies. Its novel architecture and interface mechanisms allow rapid linking to both arbitrary simulators that define a particular design problem and to existing optimization software. This significantly reduces the time and effort required to solve individual design optimization problems. The architecture allows easy integration of sampling, approximation, and statistical design modules, and enables high-level tasks to be developed using the low-level modules as building blocks. In this article, we detail the functionality and architecture of CENTER and give examples of its use for design optimization.

A Production System for Use of Global Optimization Knowledge

A method employing ideas from applied artificial intelligence is proposed for use in design optimization. Knowledge of global information is incorporated in several rule types concerning constraint activity, redundancy, and dominance. The rules are organized in a production system that makes deductions about possible (globally) active sets. Two design examples demonstrate the strategy. Similar ideas may be used in other areas of the optimization process, particularly in design applications.

The anticipated impact of supercomputers on finite-element analysis

The supercomputers of the 1980's have already impacted large-scale computation. This paper discusses the status and anticipated impact of supercomputers on finite-element analysis which is the primary tool for structural analysis and is also very useful in other areas of engineering analysis. The initial impact has been the significant reduction in turnaround time for large problems and the corresponding opportunity to solve heretofore unsolvable problems. In these cases, emphasis has been placed on employing already-proven computing software which was modifed to take advantage of vector processing and other forms of parallel operations. This trend is expected to continue because the established usage base of commercially available programs is not likely to be quickly dislodged. The near term will see the further use of design optimization, broader use of nonlinear mechanics, and a closer link between designers and analysts because of improved computer turnaround. The economy of scale suggests that solution techniques will be performed not only faster but cheaper than is possible with scalar processors which will further encourage the analysis of larger, more complex structures. The supercomputers of the future are expected to offer additional challenges to today's application systems. A primary factor in this will be the effective use of multiprocessors. Additional influence is expected as Artificial Intelligence matures to the point where Expert Systems become a reality for selected engineering and scientific disciplines. In order to effectively compete, today's software companies must address the possibility of significant changes in the architecture and methodology currently embodied in today's systems. Improved packaging, most likely in the form of pre- and postprocessors, will be necessary to provide industry- or technology-specific systems solutions.