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Rotary-draw tube bending operation is one of the most universal methods used for the tube forming processes. Similar to the other forming methods, some problems such as wall thinning, cross-section distortion, wrinkling, and springback can also be seen on the tubes formed by rotary-draw bending operations. Springback is a very common problem and its prediction plays a crucial role in increasing the efficiency of the tube bending operations and also to overcome the difficulties in the assembly processes. Tube diameter, wall thickness, bend radius, bend angle, and coefficient of friction can be considered as the most effective parameters that cause the variation of springback magnitude. In this study, not a simple one-at-a-time sensitivity analysis, but a thorough investigation of the springback phenomena involving interactions between the geometrical and mechanical parameters is done and surrogate models are developed via the data obtained from finite element analysis using a multi-purpose explicit and implicit finite element software LS-DYNA to analyze the non-linear response of structures. The constructed surrogate models can be utilized to perform fast prediction of springback for a given combination of parameters. Three different surrogate modeling techniques are exploited and it is found that the linear polynomial response surface approximations can provide acceptable accuracy. Finally, experiments are conducted to validate the accuracy of surrogate models. It is observed that the cross-validation error predictions are close to the errors observed in the experiments., Scientific and Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [MAG-109M078], This work was supported by the Scientific and Research Council of Turkey (TUBITAK) [grant number MAG-109M078].

Radial basis functions (RBFs) are approximate mathematical models that can mimic the behaviour of rapidly changing and computationally expensive simulations, such as finite element simulations for predicting automobile crash responses. The most popular way of selecting optimal RBF shape parameters is based on minimizing the global cross-validation error (CVE). Solving this optimization problem may lead to the construction of globally accurate RBF models, but the shape parameters are assumed to be constant over the entire design space. On the other hand, having flexible shape parameters that can change over the design space may allow the local behaviour to be captured better, thereby improving the accuracy. Thus, optimizing the RBF shape parameters based on minimization of the pointwise CVE rather than the global CVE is proposed in this paper. Three benchmark mathematical functions followed by an automobile crash problem are used to evaluate the effectiveness of the proposed method. It is found that the RBF models based on the minimum pointwise CVE outperform the RBF models based on the minimum global CVE.

Experimental structural response of equipment mounted in store carried externally by jet type aircraft is investigated, predicted and compared with responses suggested by military standards in this study. A representative store which is similar to Mark-83 warhead with guidance units in terms of mass and geometry is used in this study. The main scope of this study is to evaluate the structural response proposed by military standards with real test conditions and also suggest a new method with an artificial neural network to predict the maximum response. Seventy-five different flight conditions are used to train the network for low and high frequency components. Also, eight flight conditions apart from the training set of flight conditions are used to test the approach. Acceleration levels are collected in real flight conditions by the data storage system. In signal processing, vibration response is expressed as power spectral density functions in the frequency domain. Procedures to predict the maximum response from measurements are determined with statistical limits in the literature. Besides the well-known limits in literature, third-order polynomial normal and logarithmic transform is used, and the performance of the different limits is compared. It is found that the military standard vibration spectrum is conservative. Distribution-free and normal tolerance limits predicted low frequency acceleration spectral density magnitudes more accurately. Their prediction performances were better than those of the other tolerance limits and that of the military standard. Third-order polynomial transform predictions are found to be reasonable with respect to normal prediction limit and envelope approach. Finally, it can be concluded that the response prediction method proposed in this article works well for Mark-83 warheads with guidance unit carried externally by jet fighter. © IMechE 2021.

Many automobile companies are actively exploring the use of high-strength dual-phase steels as an alternative to aluminum and magnesium alloys owing to their light weight, low cost and durability. However, dual-phase steels have a tendency to springback more than other structural steels in a forming operation owing to their high tensile strength. In addition, variations in manufacturing process parameters and material properties cause springback variation over different manufactured parts. Therefore, it is an important task to reduce the magnitude of springback, as well as its variation within, to produce robust and cost-effective parts. This article investigates minimization of the magnitude and variation of springback of DP600 steels in U-channel forming within a robust optimization framework. The computational cost was reduced by utilizing metamodels for prediction of the springback and its variation during optimization. Three different allowable sheet thinning levels were considered in solving the robust optimization problem, and it was found that, as the allowable thinning increased, the die radius decreased, thereby the magnitude and variation of springback reduced. A simple sensitivity analysis was performed and the yield stress was found to be the most important random variable. Finally, a double-loop Monte Carlo simulation method was proposed to calculate part-to-part and batch-to-batch springback variations. It was found that, as the batch-to-batch variation of yield stress increased, the batch-to-batch springback variation increased, while the part-to-part springback variation remained unchanged., TUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [MAG 109M078], This research is supported by TUBITAK (The Scientific and Technological Research Council of Turkey), under award MAG 109M078.

The high hardness and wear resistance levels of Alumina (Al2O3)-based ceramics make them excellent choices for incorporation into cutting tools that can be used to machine hardened steels. However, their poor fracture behaviour usually leads to catastrophic failures when they are used in these machining tasks. Thus, there is a need for an investigation that probes the effect of cutting parameters on the machining of hardened steels with Al2O3-based ceramic cutting tools and isolates a set of optimal values of cutting parameters. This paper is such a study. An experimental study that is guided by Taguchi's techniques is performed to analyse the effects of three cutting parameters: cutting speed; feed rate; and depth of cut on two performance measures, flank wear and surface roughness. The robust optimal cutting parameters for each performance measure are reported.

The high hardness and wear resistance levels of Alumina (Al2O3)-based ceramics make them excellent choices for incorporation into cutting tools that can be used to machine hardened steels. However, their poor fracture behaviour usually leads to catastrophic failures when they are used in these machining tasks. Thus, there is a need for an investigation that probes the effect of cutting parameters on the machining of hardened steels with Al2O3-based ceramic cutting tools and isolates a set of optimal values of cutting parameters. This paper is such a study. An experimental study that is guided by Taguchi's techniques is performed to analyse the effects of three cutting parameters: cutting speed; feed rate; and depth of cut on two performance measures, flank wear and surface roughness. The robust optimal cutting parameters for each performance measure are reported.

Reliability prediction of highly safe mechanical systems can be performed using classical tail modeling. Classical tail modeling is based on performing a relatively small number of limit-state evaluations through a sampling scheme and then fitting a tail model to the tail part of the data. However, the limit-state calculations that do not belong to the tail part are discarded, so majority of limit-state evaluations are wasted. Guided tail modeling, proposed earlier by the author, can provide a remedy through guidance of the limit-state function calculations toward the tail region. In the original guided tail modeling, the guidance is achieved through a procedure based on threshold estimation using univariate dimension reduction and extended generalized lambda distribution and tail region approximation using univariate dimension reduction. This article proposes a new guided tail modeling technique that utilizes support vector machines. In the proposed method, named guided tail modeling with support vector machines (GTM-SVM), the threshold estimation is still performed using univariate dimension reduction and extended generalized lambda distribution, while the tail region approximation is based on support vector machines. The performance of guided tail modeling with support vector machines is tested with mathematical example problems as well as structural mechanics problems with varying number of variables. GTM-SVM is found to be more accurate than both guided tail modeling and classical tail modeling for low-dimensional problems. For high-dimensional problems, on the other hand, the original guided tail modeling is found to be more accurate than guided tail modeling with support vector machines, which is superior to classical tail modeling., This work was supported by The Scientific and Technological Research Council of Turkey TUBITAK [grant number MAG-109M537].

In recent years, optimization started to become popular in several engineering disciplines such as aerospace, automotive and turbomachinery. Optimization is also a powerful tool in hydraulic turbine industry to find the best performance of turbines and their sub-elements. However, direct application of the optimization techniques in design of hydraulic turbines is impractical due to the requirement of performing computationally expensive analysis of turbines many times during optimization. Metamodels (or surrogate models) that can provide fast response predictions and mimic the behavior of nonlinear simulation models provide a remedy. In this study, simulation-based design of Francis type turbine runner is performed by following a metamodel-based optimization approach that uses multiple types of metamodels. A previously developed computational fluid dynamics-based methodology is integrated to the optimization process, and the results are compared to the results obtained from on-going computational fluid dynamics studies. The results show that, compared to the conventional methods such as computational fluid dynamics-based methods, metamodel-based optimization can shorten the design process time by a factor of 9.2. In addition, with the help of optimization, turbine performance is increased while cavitation on the turbine blades, which can be harmful for the turbine and reduce its lifespan, is reduced., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is financially supported by the Ministry of Development of Turkey. The computations are performed at TOBB ETU Center for Hydro Energy Research (ETU Hydro), CFD Laboratory.

In recent years, optimization started to become popular in several engineering disciplines such as aerospace, automotive and turbomachinery. Optimization is also a powerful tool in hydraulic turbine industry to find the best performance of turbines and their sub-elements. However, direct application of the optimization techniques in design of hydraulic turbines is impractical due to the requirement of performing computationally expensive analysis of turbines many times during optimization. Metamodels (or surrogate models) that can provide fast response predictions and mimic the behavior of nonlinear simulation models provide a remedy. In this study, simulation-based design of Francis type turbine runner is performed by following a metamodel-based optimization approach that uses multiple types of metamodels. A previously developed computational fluid dynamics-based methodology is integrated to the optimization process, and the results are compared to the results obtained from on-going computational fluid dynamics studies. The results show that, compared to the conventional methods such as computational fluid dynamics-based methods, metamodel-based optimization can shorten the design process time by a factor of 9.2. In addition, with the help of optimization, turbine performance is increased while cavitation on the turbine blades, which can be harmful for the turbine and reduce its lifespan, is reduced., The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is financially supported by the Ministry of Development of Turkey. The computations are performed at TOBB ETU Center for Hydro Energy Research (ETU Hydro), CFD Laboratory.