Cem Celal Tutum

Reliability Estimation of the Pultrusion Process Using the First-Order Reliability Method (FORM)

In the present study the reliability estimation of the pultrusion process of a flat plate is analyzed by using the first order reliability method (FORM). The implementation of the numerical process model is validated by comparing the deterministic temperature and cure degree profiles with corresponding analyses in the literature. The centerline degree of cure at the exit (CDOCE) being less than a critical value and the maximum composite temperature (Tmax) during the process being greater than a critical temperature are selected as the limit state functions (LSFs) for the FORM. The cumulative distribution functions of the CDOCE and Tmax as well as the correlation coefficients are obtained by using the FORM and the results are compared with corresponding Monte-Carlo simulations (MCS). According to the results obtained from the FORM, an increase in the pulling speed yields an increase in the probability of Tmax being greater than the resin degradation temperature. A similar trend is also seen for the probability of the CDOCE being less than 0.8.

Optimization of the Thermosetting Pultrusion Process by Using Hybrid and Mixed Integer Genetic Algorithms

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

Optimisation of process parameters in friction stir welding based on residual stress analysis: a feasibility study

Abstract The present paper considers the optimisation of process parameters in friction stir welding (FSW). More specifically, the choices of rotational speed and traverse welding speed have been investigated using genetic algorithms. The welding process is simulated in a transient, two-dimensional sequentially coupled thermomechanical model in ANSYS. This model is then used in an optimisation case where the two objectives are the minimisation of the peak residual stresses and the maximisation of the welding speed. The results indicate that the objectives for the considered case are conflicting, and this is presented as a Pareto optimal front. Moreover, a higher welding speed for a fixed rotational speed results, in general, in slightly higher stress levels in the tension zone, whereas a higher rotational speed for a fixed welding speed yields somewhat lower peak residual stress, however, a wider tension zone, leading to a substantially higher residual tensile force.

Numerical optimisation of friction stir welding: review of future challenges

Abstract During the last decade, the combination of increasingly more advanced numerical simulation software with high computational power has resulted in models for friction stir welding (FSW), which have improved the understanding of the determining physical phenomena behind the process substantially. This has made optimisation of certain process parameters possible and has in turn led to better performing friction stir welded products, thus contributing to a general increase in the popularity of the process and its applications. However, most of these optimisation studies do not go well beyond manual iterations or limited automation. The present paper thus attempts to give a brief overview of some of the successful autonomous optimisation applications of FSW in combination with what determines the state of the art in the field. Finally, this is followed by a discussion of some of the trends and future challenges that we foresee in the rapidly expanding area of autonomous optimisation of FSW.

The Internal Stress Evaluation of Pultruded Blades for a Darrieus Wind Turbine

This paper investigates the integrated modeling of a pultruded NACA0018 blade profile which is a part of the FP7 EU project DeepWind. The pultrusion process simulation is combined with the preliminary subsequent in-service load scenario. In particular, the process induced residual stresses and distortions are predicted by using a new approach combining a 3D Eulerian thermo-chemical analysis, in which the temperature and the cure degree distributions are obtained, and a 2D quasi-static plane strain mechanical analysis. The post-die region where convective cooling prevails is also included in the process model. The bending into shape of the pultruded blade profile is simulated with and without taking the residual stresses into account. The internal stress distribution in the profile is evaluated after the bending analysis and it is found that the process induced residual stresses have the potential to promote or to demote the internal stresses in the structural analysis.

Utilizing multiple objectives for the optimization of the pultrusion process based on a thermo-chemical simulation

Pultrusion is one of the most effective manufacturing processes for producing composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the resin injection chamber, etc., to provide better understanding of the process, consequently to improve the efficiency of the process as well the product quality. Numerous simulation approaches have been presented until now. However, optimization studies had been limited with either experimental cases or determining only one objective to improve one aspect of the performance of the process. This objective is either augmented by other process related criteria or subjected to constraints which might have had the same importance of being treated as objectives. In essence, these approaches convert a true multi-objective optimization problem (MOP) into a single-objective optimization problem (SOP). This transformation obviously results in only one optimum solution and it does not support the efforts to get more out of an optimization study, such as relations between variables and objectives or constraints. In this study, an MOP considering thermo-chemical aspects of the pultrusion process (e.g. cure degree, temperatures), in which the pulling speed is maximized and the heating power is minimized simultaneously (without defining any preference between them), has been formulated. An evolutionary multi-objective optimization (EMO) algorithm, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), has been used to solve this MOP in an ideal way where the outcome is the set of multiple solutions (i.e. Pareto-optimal solutions) and each solution is theoretically an optimal solution corresponding to a particular trade-off among objectives. Following the solution process, in other words obtaining the Pareto-optimal front, a further postprocessing study has been performed to unveil some common principles existing between the variables, the objectives and the constraints either along the whole front or in some portion of it. These relationships will reveal a design philosophy not only for the improvement of the process efficiency, but also a methodology to design a pultrusion die for different operating conditions.

Multi-Criteria Optimization in Friction Stir Welding Using a Thermal Model with Prescribed Material Flow

Friction stir welding (FSW) is an innovative solid-state joining process providing products with superior mechanical properties. It utilizes a rotating tool being submerged into the joint line and traversed while stirring the two pieces of metal together to form the weld. The temperature distribution in the weld zone, as a function of the heat generation, highly affects the evolution of the microstructure and the residual stresses, and also the performance of the weld. Therefore, thermal models play a crucial role in detailed analysis and improvement of this process. In this study, a three-dimensional steady state thermal model of FS welding of AA2024-T3 plates has been simulated. The effect of the tool rotation on the temperature distribution has been also taken into account. This thermal model has been integrated with the non-dominated sorting genetic algorithm (NSGA-II) to solve a common manufacturing problem having conflicting objectives, i.e., maximization of production rate and tool lifetime. The resulting multiple trade-off solutions are then investigated to unveil any design rules which have a strong potential in industrial use.

Hybrid search for faster production and safer process conditions in friction stir welding

The objective of this paper is to investigate optimum process parameters and tool geometries in Friction Stir Welding (FSW) to minimize temperature difference between the leading edge of the tool probe and the work piece material in front of the tool shoulder, and simultaneously maximize traverse welding speed, which conflicts with the former objective. An evolutionary multi-objective optimization algorithm (i.e. NSGA-II), is applied to find multiple trade-off solutions followed by a gradient-based local search (i.e. SQP) to improve the convergence of the obtained Pareto-optimal front. In order to reduce the number of function evaluations in the local search procedure, the obtained nondominated solutions are clustered in the objective space and consequently, a postoptimality study is manually performed to find out some common design principles among those solutions. Finally, two reasonable design choices have been offered based on several process specific performance and cost related criteria.

State-of-the-Art Multi-Objective Optimisation of Manufacturing Processes Based on Thermo-Mechanical Simulations

During the last couple of decades the possibility of modelling multi-physics phenomena has increased dramatically, thus making simulation of very complex manufacturing processes possible and in some fields even an everyday event. A consequence of this has been improved products with respect to properties, weight/stiffness ratio and cost. However this development has mostly been based on “manual iterations” carried out by the user of the relevant simulation software rather than being based on a systematic search for optimal solutions. This is, however, about to change because of the very tough competition between manufacturers of products in combination with the possibility of doing these highly complex simulations. Thus, there is a crucial need for combining advanced simulation tools for manufacturing processes with systematic optimisation algorithms which are capable of searching for single or multiple optimal solutions. Nevertheless, despite this crucial need, it is interesting to notice the very limited number of contributions in this field and consequently this makes us wonder about the underlying reasons for it. The understanding of the physical phenomena behind the processes, the current numerical simulation tools and the optimisation capabilities which all mainly are driven by the industrial or academic demands as well as computational power and availability of both the simulation and the multi-objective optimisation oriented software on the market are the main concerns to look for. These limitations eventually determine what is in fact possible today and hence define what the “state-of-the-art” is. So, seen from that perspective the very definition of the state-of-the-art itself in the field of optimisation of manufacturing processes constitutes an important discussion. Moreover, in the major research fields of manufacturing process simulation and multi-objective optimisation there are still many issues to be reserved.

A multi-objective optimization application in Friction Stir Welding: Considering thermo-mechanical aspects

The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermo-mechanical model implemented in the FE-code, ANSYS. The thermal model is based on a heat source description which in essence is governed by the rotational speed and the temperature dependent yield stress of the work piece material. This model in turn delivers the temperature field, in order to compute thermal strain field which is the main driver for the mechanical model predicting both transient and finally residual stresses in the work piece. This thermo-mechanical model is then used in the aforementioned constrained MOO case where the two objectives are conflicting. Following this, two reasonable design solutions among those multiple trade-off solutions have been selected based on the cost and the quality preferences.