离散变量结构优化设计的拟满应力遗传算法

以力学准则法为基础,提出了一种求解离散变量结构优化设计的拟满应力方法;这种方法能直接求解具有应力约束和几何约束的离散变量结构优化设计问题。通过在遗传算法中定义拟满应力算子,建立了一种离散变量结构优化设计的混合遗传算法拟满应力遗传算法。算例表明:这种混合遗传算法对于离散变量结构优化设计问题具有较高的计算效率。     

Genetic algorithm attributes for component selection

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.     

Robust optimal-parameter design approach for tolerance design problems

A robust optimal-parameter design method, henceforth called the TGAOA, to solve tolerance design problems has been investigated. Tolerance affects system performance and leads to violation of design constraints. The TGAOA approach conducts global exploration by using a genetic algorithm and exploits optimal offspring via the Taguchi method. It is able to effectively reduce the impact of parameter variations in reaching robust optimal-solutions as allowed by the tolerance. Two design examples are employed to evaluate the performance of the new method. The first is for a mixed H₂/H_∞ optimal PID controller under varying PID component specifications, plant uncertainty, and other external unknown disturbance. The second involves a 13-variable test function, which includes quadratic, linear, and polynomial forms to illustrate the general robustness and computational efficiency, for which comparisons are also made with its predecessors of genetic algorithm and hybrid Taguchi-genetic algorithm.     

A unified method of design for fatigue crack growth resistance in structural materials

It is well established that there are two fatigue crack tip driving forces – the cyclic, ΔK, and the static, K_max. In this study, the effects of each crack tip driving force on crack growth were evaluated for various structural materials. A unified method of design that allows for predicting the response of long and physically small fatigue cracks at positive stress ratios is introduced. Good agreement between predicted and experimental long and physically small fatigue crack growth data was obtained. The importance of this method in material and component design is discussed as part of a contemporary design philosophy.     

Evolutionary design of a generalized polynomial neural network for modelling sediment transport in clean pipes

To determine the minimum velocity required to prevent sedimentation, six different models were proposed to estimate the densimetric Froude number (Fr). The dimensionless parameters of the models were applied along with a combination of the group method of data handling (GMDH) and the multi-target genetic algorithm. Therefore, an evolutionary design of the generalized GMDH was developed using a genetic algorithm with a specific coding scheme so as not to restrict connectivity configurations to abutting layers only. In addition, a new preserving mechanism by the multi-target genetic algorithm was utilized for the Pareto optimization of GMDH. The results indicated that the most accurate model was the one that used the volumetric concentration of sediment (C_V), relative hydraulic radius (d/R), dimensionless particle number (D_gr) and overall sediment friction factor (λ_s) in estimating Fr. Furthermore, the comparison between the proposed method and traditional equations indicated that GMDH is more accurate than existing equations.     

A new push‐pull sample design for microscale mode 1 fracture toughness measurements under uniaxial tension

A sample geometry is proposed for performing microscale tensile experiments based on a push‐pull design. It allows measuring mode 1 fracture toughness under uniform far‐field loading. Finite element simulations were performed to determine the geometry factor, which was nearly constant for Young's moduli spanning 2 orders of magnitude. It was further verified that mode 1 stress intensity factor K_I is nearly constant over the width of the tension rods and an order of magnitude higher than K_II and K_III. Notched samples with different a/w ratios were prepared in (100)‐oriented Si by a combination of reactive ion etching and focused ion beam milling. The mode 1 fracture toughness K_I,q was constant with a/w and in average 1.02 ± 0.06 MPa√m in good agreement with existing literature. The geometry was characterized and experimentally validated and may be used for fracture toughness measurements of all material classes. It is especially interesting when a uniaxial, homogeneous stress field is desired, if crack tip plasticity is important, or when positioning of the indenter is difficult.     

Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization

The optimal truss design using problem-oriented evolutionary algorithm is presented in the paper. The minimum weight structures subjected to stress and displacement constraints are searched. The discrete design variables are areas of members, selected from catalogues of available sections. The integration of the problem specific knowledge into the optimization procedure is proposed. The heuristic rules based on the concept of fully stressed design are introduced through special genetic operators, which use the information concerning the stress distribution of structural members. Moreover, approximated solutions obtained by deterministic, sequential discrete optimization methods are inserted into the initial population. The obtained hybrid evolutionary algorithm is specialized for truss design. Benchmark problems are calculated in numerical examples. The knowledge about the problem integrated into the evolutionary algorithm can enhance considerably the effectiveness of the approach and improve significantly the convergence rate and the quality of the results. The advantages and drawbacks of the proposed method are discussed.     

TRANSIENT FATIGUE CRACK GROWTH BEHAVIOUR FOLLOWING SINGLE OVERLOADS AT HIGH STRESS RATIOS

Fatigue crack growth tests with constant amplitude loading and single overload have been performed on a long mode I crack in 2017-T3 aluminium alloy at various stress ratios from 0 to 0.7. Two crack tip parameters of σ_op and σ_tt were evaluated using a finite element analysis for a growing crack under these loading conditions. The former is the crack opening stress and the latter is the applied stress level at which the stress at the crack tip becomes tensile. It was found that transient crack growth behaviour following single overloads at high stress ratios was significantly different from that at a low stress ratio: at higher stress ratios, following the application of the overload, there was a rapid retardation which was followed by an acceleration in growth rate and then a faster return to the steady state level at baseline loading. The experimentally observed transient post-overload behaviour is discussed in terms of the two effective stress range ratios of U_op and U_tt, which are determined from σ_op and σ_tt, respectively. For the stress ratios and overload ratios studied, the results indicate that the changes in U_tt with crack extension after the overload are reasonably consistent with the crack growth rate trends. The stress distribution at minimum applied stress would account for the transient changes in U_tt.     

Conceptual robustness in simultaneous engineering: An extension of Taguchi's parameter design

Simultaneous engineering processes involve multifunctional teams; team members simultaneously make decisions about many parts of the product-production system and aspects of the product life cycle. This paper argues that such simultaneous distributed decisions should be based on communications about sets of possibilities rather than single solutions. By extending Taguchi's parameter design concepts, we develop a robust and distributed decision-making procedure based on such communications. The procedure shows how a member of a design team can make appropriate decisions based on incomplete information from the other members of the team. More specifically, it (1) treats variations among the designs considered by other members of the design team asconceptual noise; (2) shows how to incorporate such noises into decisions that are robust against these variations; (3) describes a method for using the same data to provide preference information back to the other team members; and (4) provides a procedure for determining whether to release theconceptually robust design or to wait for further decisions by others. The method is demonstrated by part of a distributed design process for a rotary CNC milling machine. While Taguchi's approach is used as a starting point because it is widely known, these results can be generalized to use other robust decision techniques.     

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a _p), radial immersion (a _e), feed rate (f _t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.     

Hybrid algorithm based on improved extended shifting bottleneck procedure and GA for assembly job shop scheduling problem

With the makespan as the optimisation goal, we propose a hybrid solving method that combines improved extended shifting bottleneck procedure (i-ESB) and genetic algorithm (GA) for the assembly job shop scheduling problem (AJSSP). Hybrid genetic algorithm (HGA) uses a GA based on operation constraint chain coding to achieve global search and a local search based on an i-ESB. In the design of i-ESB, an extended disjunctive graph model (EDG) corresponding to AJSSP is presented. The calculation method of the operation head and tail length based on EDG is studied, as well as the searching method of key operations. The Schrage algorithm with disturbance is used to solve the single-machine scheduling subproblem. The selection criterion for bottleneck machines is increased. A greedy bottleneck machine re-optimisation process is designed. The effectiveness and superiority of the proposed algorithm are verified by testing and analysing the relevant examples in the literature.     

Novel method to construct large-scale design space in lubrication process utilizing Bayesian estimation based on a small-scale design-of-experiment and small sets of large-scale manufacturing data

A large-scale design space was constructed using a Bayesian estimation method with a small-scale design of experiments (DoE) and small sets of large-scale manufacturing data without enforcing a large-scale DoE. The small-scale DoE was conducted using various Froude numbers (X₁) and blending times (X₂) in the lubricant blending process for theophylline tablets. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y₁), tablet hardness (Y₂), and dissolution rate (Y₃) on a small scale were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. The constant Froude number was applied as a scale-up rule. Three experiments under an optimal condition and two experiments under other conditions were performed on a large scale. The response surfaces on the small scale were corrected to those on a large scale by Bayesian estimation using the large-scale results. Large-scale experiments under three additional sets of conditions showed that the corrected design space was more reliable than that on the small scale, even if there was some discrepancy in the pharmaceutical quality between the manufacturing scales. This approach is useful for setting up a design space in pharmaceutical development when a DoE cannot be performed at a commercial large manufacturing scale.     

Economic and Economic‐Statistical Designs of Phase II Profile Monitoring

In economic design of profiles, parameters of a profile are determined such that the total implementation cost is minimized. These parameters consist of the number of set points, n, the interval between two successive sampling, h, and the parameters of a control chart used for monitoring. In this paper, the Lorenzen–Vance cost function is extended to model the costs associated with implementing profiles. The in‐control and the out‐of‐control average run lengths, ARL₀ and ARL₁, respectively, are used as two statistical measures to evaluate the statistical performances of the proposed model. A genetic algorithm (GA) is developed for solving both the economic and the economic‐statistical models, where response surface methodology is employed to tune the GA parameters. Results indicate satisfactory statistical performance without much increase in the cost of implementation. Copyright © 2013 John Wiley & Sons, Ltd.     

Thermal optimization in transient thermoelasticity using response surface approximations

Response surface methodology is used to construct approximations to temperature and stress in transient thermoelastic analysis of non-linear systems. The analysis forms the core component of a heating/cooling rate maximization problem in which the ordinates of the ambient temperature at equally spaced time intervals are chosen as the design variables. Polynomials or cubic splines are fitted through the ordinates to describe the ambient temperature profile required for the convective heat transfer analysis. An experimental design method based on D-optimality and a genetic algorithm was used to select the design points used to create the approximations. Linear response surfaces were found to be sufficiently accurate, thereby minimizing the number of finite element analyses. Two examples of which one is a thick-walled pressure vessel are used to illustrate the methodology. © 1998 John Wiley & Sons, Ltd.     

EURAXLES – A global approach for design,production and maintenance of railway axles: WP1 – Advances in fatigue load analysis and reliability assessment of railway axles

The work presented in this paper was led within the collaborative project “Euraxles” of the FP7 program of the European Commission. It aimed at developing processes and methods that contribute to the minimization of the risk of fatigue failure of railway axles in service. This paper focuses on the development of a method to assess the reliability of axles according to fatigue damage. The proposed approach is mainly based on the stress strength interference analysis (SSIA) and the fatigue‐equivalent‐load (FEL) methods. It aims at calculating the axles’ probability of fatigue failure, by characterizing the variability of real in‐service loads and the scatter of the axles fatigue strength, and at evaluating more accurately the actual design margins. First of all, the main lines of the stress strength interference analysis method are recalled. This method aims at evaluating the in‐service reliability of components for their design or their homologation. It is used in many industries for various applications (mechanical components or systems, electronic elements, etc.). In the second part, the fatigue load analysis method that is proposed for railway axles is described. It starts with a post‐processing of an axle load measurement: from a time signal of forces applied to both wheels fitted on the axle, fatigue cycles of bending moment applied to the axle are identified and transformed into a cyclic equivalent load, the M_eq, which is a measurement of the severity of the initial variable load. Then, virtual but realistic load spectra are generated, thanks to a classification operation followed by a random draw of elementary load data that considers the operation and maintenance conditions of the axle. All the spectra are then analysed thanks to the fatigue‐equivalent‐load method in order to build the distribution of in‐service load severities that gives a picture of the stress to which the axles are submitted. In the third and last part of the paper, the methods are applied to real data of “Société nationale des chemins de fer français” (SNCF), the French national railway operator. Sensitivity analyses are performed in order to quantify the effect on the M_eq of variations of parameters and to verify the convergence and robustness of the process. Finally, results obtained for a passenger coach are given. The comparison between the distribution of load severities and the normative load, defined as according to european standards EN13103, shows that, for the studied axle, the normative load is very conservative. Using the axles fatigue limits identified on full‐scale tests, a stress strength interference analysis is performed to calculate the probability of failure of the axle.     

D-optimal experimental design for production models in nonstandard experiments

Robust design is an effective Quality by Design method to reduce product variation by selecting levels of design factors. For a number of situations, a nonstandard design region with linearly limited resources is needed to conduct an experiment. In the literature, little attention has been given to the development of robust design models for the nonstandard design region with a combination of linearly limited resources and a limited number of design points. In this paper, a selection scheme of D-optimal experimental design points is proposed to generate design points using the modified exchange algorithm for the nonstandard design region while specifying linearly limited resources and the limited number of design points. The modified exchange algorithm is able to generate global design points with less time complexity than the improved Fedorov algorithm. In addition, robust design models linking a D-optimal experimental design with quality considerations are proposed in order to obtain optimum settings of design factors for the product. Comparative studies are also presented. Finally, a real-life experimental study shows that the proposed models with the desirability function and the sequential quadratic programming technique achieve greater variance reduction than the traditional counterparts.     

Stress curves and mechanical properties of high performance concrete

Abstract

In this research, high performance concrete (HPC) was designed by the minimum void ratio method, and slag and silica fumes partially replaced cement, as well as fly ash replacing about 15% of sand. Stress curves for compressive, splitting and flexure strengths of HPC specimens were measured and indicated the experimental concretes had better pastes to void ratios than control batches ratio N=V_p /V_v =1.3. The result indicates that pozzolanic materials provide not only a chemical strength effect, but also a physical packing effect. The compressive stress curves may keep growing as the concrete ages.     

Design of flexible manpower line walk cycles for a fixed number of operators

Flexible manpower lines (FMLs) are a form of flow process line in which operators are allocated ‘walk cycles’, i.e. a repetitive sequence in which to load and unload machine tools. The effective design of such lines is normally achieved with the expectation that operators without full walk cycles, i.e. those that do not require a full Takt time to accomplish, can complete their walk cycles at an adjoining FML. However, an alternative FML design strategy is possible in cases where no adjoining FML exists or it is not possible for operators to move between work areas. This strategy involves determining the minimum Takt time and the associated operator walk cycles at which the FML can operate under a fixed number of operators. To solve this type of problem, a genetic algorithm that make use of a novel crossover operator has been developed that can design FMLs. The genetic algorithm is capable of generating, for a specific Takt time and fixed number of operators, FMLs with high-quality, near-optimal operator walk cycles. Solutions for the fixed manpower case were then identified by performing multiple genetic algorithm runs to find the best walk cycles at various Takt times.     

The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps

In the present study, multi-objective optimization of centrifugal pumps is performed in three steps. In the first step, efficiency (η) and the required net positive suction head (NPSHr) in a set of centrifugal pumps are numerically investigated using commercial software. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained in the second step for modeling of η and NPSHr with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, a multi-objective particle swarm optimization method (MOPSO) is used for Pareto-based optimization of centrifugal pumps considering two conflicting objectives, η and NPSHr. The Pareto results of the MOPSO method are also compared with those of a multi-objective genetic algorithm (NSGA II). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of centrifugal pumps can be discovered by Pareto-based multi-objective optimization of the obtained polynomial metamodels representing η and NPSHr characteristics.     

Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers

A CFD-based model is applied to study emission formation in a bubbling fluidized bed boiler burning biomass. After the model is validated to a certain extent, it is used for optimization. There are nine design variables (nine distinct NH₃ injections in the selective non-catalytic reduction process) and two objective functions (which minimize NO and NH₃ emissions in flue gas). The multiobjective optimization problem is solved using the reference-point method involving an achievement scalarizing function. The interactive reference-point method is applied to generate Pareto optimal solutions. Two inherently different optimization algorithms, viz. a genetic algorithm and Powell's conjugate-direction method, are applied in the solution of the resulting optimization problem. It is shown that optimization connected with CFD is a promising design tool for combustion optimization. The strengths and weaknesses of the proposed approach and of the methods applied are discussed from the point of view of a complex real-world optimization problem.