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In this study, response surface methodology was used to examine the effects of temperature and time on the development of niobium carbide coatings on AISI D3 steel. The effect of niobizing temperature (900–1100^∘C) and period (2–6 hours) on coating thickness, hardness, fracture toughness, coefficient of friction and wear rates was investigated. ANOVA was conducted to analyze the experimental data, and it was observed that the coating thickness and microhardness increased with temperature and time. The response surfaces developed for fracture toughness, coefficient of friction and wear rates were found to exhibit a complex structure that is significantly influenced by temperature, time and their interactions. The correlation coefficients of the developed regression models range between 0.82 and 0.99. Using the empirical formulas obtained with these mathematical models, it is predicted that niobium carbide coatings can be obtained with the targeted properties more economically and practically with the thermochemical method.

Under low-speed collision conditions, the thin-walled structures located at the forepart of passenger car act as a significant energy absorber, which can reduce the injury degree of passengers in collision accidents. Therefore, the design of thin-walled structures is particularly important in automobile body development. High stiffness and strength contribute to structural integrity, but the resulting large collision force will be detrimental to occupant safety. To address this issue, the paper proposed a novel negative Poisson ratio thin-walled tube (NPR-TWT). In addition, the second-order sensitivity-based response surface models (SB-RSMs) were established to improve the efficiency of design optimization. Furthermore, the NPR-TWT was optimized by using the non-dominated sorting genetic algorithm (NSGA-II) to enhance its comprehensive performance. The results showed that the optimized NPR-TWT could absorb collision energy by sufficient displacement deformation and reduce the peak impact force. It can provide theoretical and practical guidance for the application of thin-walled structures in automobile body safety.

This paper proposes a novel structural damage identification approach coupling the Mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

The geometry of high-dimensional spaces is very different from low dimensional spaces and possesses some counter-intuitive features. It is shown that, for high dimensions, the sampling points fall far away from the origin and concentrate within an intersection between a very thin shell and a suitable equatorial slab. The well-known First-Order Reliability Method (FORM), originally formulated for low dimensions, may work well in many engineering problems of high dimension. But it is not able to reveal the level of achieved accuracy. Considering the features of high-dimensional geometry, a novel linear response surface based on Support Vector Method (SVM) is proposed for structural reliability problems of high dimension. The method is shown to outperform FORM for structural reliability problems of high dimension in terms of robustness and accuracy.

National and international regulatory standards require industrial risk assessment, taking into account natural hazards including earthquakes, in the framework of Quantitative Risk Analysis (QRA). Seismic fragility analysis of industrial components may be carried out similarly as what has been done for buildings, even though some peculiar aspects require the development of specific tools. In the present paper a contribution to the definition of a rational procedure for seismic vulnerability assessment of standardised industrial constructions in a probabilistic framework is given. The method covers a range of components of the same structural type. Seismic reliability formulation for structures is used. Both seismic capacity and demand are considered probabilistic with the latter assessed by dynamic analyses. The application example refers to shell elephant foot buckling of unanchored sliding tanks. A regression-based method is applied to relate fragility curves to parameters varying in the domain of variables for structural design.

A dual-stage sampling (DSS) is proposed to reduce training samples of artificial neural network for establishing the analytical response surface model. The procedure of the DSS-based artificial neural network response surface (ANNRS) methodology is divided into two stages. At the rough stage, new sample is generated in the entire space by maximizing the minimum distance to existed samples. Then at the refine stage, new sample is produced in the same way, but is limited in the partial space between two samples with larger gradient and larger distance. And the efficiency of the DSS method is proved by a test function. Considering design variables about slot width and depth, permanent magnet (PM) width and height, and end tooth width, tubular permanent-magnet linear synchronous motor (TPMLSM) modeling problem is represented, whose thrust and ripple are calculated time-consumingly by finite element analysis. Then, The DSS-based ANNRS is applied to establish the TPMLSM model in good accuracy with fewer samples, lower consumption.

A superior performance is the primary target to which the hot-stamping process engages. Resistance heating is an effective method to higher quality that has been proved by researches. Generally, finding the available domain for the match of factors (ADMF) is the key step to obtain a superior performance. However, the ADMF is not clear about a special superior set-up system. The ADMF of principle operational factors was primarily detected by the full factorial design and the response surface methodology (RSM). These statistic models were confirmed by experiments. A heating specification was prepared based on the statistic models.