Reliability-based preform shape design in forging (original) (raw)
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Design of Forging Process Variables under Uncertainties
Journal of Materials Engineering and Performance, 2005
Forging is a complex nonlinear process that is vulnerable to various manufacturing anomalies, such as variations in billet geometry, billet/die temperatures, material properties, and workpiece and forging equipment positional errors. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion, and reduced productivity. Identifying, quantifying, and controlling the uncertainties will reduce variability risk in a manufacturing environment, which will minimize the overall production cost. In this article, various uncertainties that affect the forging process are identified, and their cumulative effect on the forging tool life is evaluated. Because the forging process simulation is time-consuming, a response surface model is used to reduce computation time by establishing a relationship between the process performance and the critical process variables. A robust design methodology is developed by incorporating reliability-based optimization techniques to obtain sound forging components. A case study of an automotive-component forging-process design is presented to demonstrate the applicability of the method.
Improved utility and application of probabilistic methods for reliable mechanical design
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
In a modern product development process such as in the automotive and aerospace sectors, extensive analytical and simulation approaches often are used to assess the ability of a design in fulfilling its requirements. Consideration of uncertainty in such situations is critical in ensuring a reliable design is produced. Probabilistic methods facilitate an improved understanding of design performance through characterization of uncertainty in the design parameters. The probabilistic methods developed over the past several decades have a range of capabilities and modes of application, for example, to predict reliability, for optimization, and to perform sensitivity studies, but have yet to be taken up routinely by industry due to a number of reasons. In this paper, issues that have typically inhibited their use or prevented a successful outcome are addressed through a systematic framework for improved utility and successful application of probabilistic designing for mechanical reliability.
Accounting uncertainties in the optimal design of multi-stage hot forging
engopt.org
Today, most of product designs employ sophisticated computer models and finite element analysis in their design. Most of these models are based on physical models without taking into account the uncertainties that occur during manufacturing. Forging is an industrial process extensively used in metal forming. Process uncertainties can cause defective parts and so incorporating uncertainty analysis on an optimization model will diminish rejected parts. On one hand a very narrow tolerance on the process parameters would increase productions costs and on the other hand large tolerances would induce a high percentage of part rejection. Thus, controlling the tolerance limits on the process parameters would lead to an improvement on the product quality and to a reduction of the production costs of hot forged parts. Using a finite element thermal mechanical analysis coupled with a genetic algorithm an optimisation method has been developed for shape design of multi-stage forging processes. The design objective is to optimise the pre-form die shape and the initial temperature of the billet in order to make the achieved final forging product to approach the desired one as much as possible. The computational efficiency of the method simulating two-stage hot forging processes has been demonstrated earlier. The main purposes of this work are to identify, quantify and control uncertainties during the forming process based on a reasonable number of data sets acquired with a finite element analysis computer model. Initial temperature of the billet, friction between dies and billet and variations in the forging set up together with cooling rate are the main factors affecting the final part dimensions. Considering temperatures and friction to be random variables, an attempt is made to fit a reasonable probability distribution to the different data sets. The analysis of the parameters uncertainties on the optimal pre-form die shape will drive to the robust design of the forging parameters.
Design Certainties by using Uncertainties in Finite Element Design Optimization
Engineers in many industries have been simulating design behavior using traditional methods employing the conventional wisdoms gained from professional practice conducted over many years. In this study an approach is proposed for optimal design of multilevel system under uncertainties. We extend the numerical analysis target to probabilistic design approach by treating stochastic quantities as random variables and parameters and posing reliability-based design restrictions. When used in simulation, once the random variables of boundary conditions, geometry and material properties are specified for a specific analysis case, the input variables are studied simultaneously by using statistical sampling methods. The parametric finite element analysis (FEA) model is then invoked repeatedly, performing deterministic analyses over the resulting input parameters. The case studies assessed in this investigation has shown that effects of different parameters in relation to specific physical properties that have the greatest impact can be evaluated. In this way the probabilistic analysis was used to identify the steps needed for future optimization and that how FE simulation technology can be used to understand production processes uncertainties and related parameter variations in manufacturing process leading to increased product reliability and quality.
Optimising the design of mechanical components for reliability and cost
International Journal of Materials and Product Technology, 2002
This paper discusses a design optimization methodology that uses probabilistic methods. The methodology addresses product performance, reliability and cost that allows the engineer to evaluate the impact of design changes on all relevant measures of the design simultaneously. By considering variation in the design parameters, the influence of uncertainty can be determined and the design can be optimized to prevent early failures while minimizing overall cost. The sensitivities of the design variables to the statistical distribution parameters are determined. The sensitivities are then used in an optimization model to determine the best combination of design parameters including the manufacturing tolerances and acceptance testing routines. A demonstration problem that illustrates the application of the method is provided.
Robust Design Optimization in forming process simulation
2010
Today, FE-simulation of forming process has become an integral part for assessing and evaluating forming processes. The optimization, i.e. improvement of product characteristics, has been an integral part of forming simulation based virtual product development for several years now. On the other hand, the robustness of forming processes is becoming more and more focused on recently. Therefore the introduction of numerical robustness evaluation and the development of suitable measurements of robustness are integrated into the virtual product development right now. In fact, robustness is an additional demand on optimized forming processes. Therefore; a process is necessary of optimizing and at the same time securing the robustness. That process is called Robust Design Optimization (RDO). The optimization and robustness evaluation are either performed consecutively or simultaneously, and several methods are available for this. In the following, existing methods shall shortly be introdu...
Sensitivity analysis based preform die shape design for net-shape forging
International Journal of Machine Tools and Manufacture, 1997
Ahatract-A sensitivity analysis method for preform die shape design in net-shape forging processes is developed in this paper using the rigid viscoplastic finite element method. The preform die shapes are represented by cubic B-spline curves. The control points or coefficients of B-spline are used as the design variables. The optimization problem is to minimize the zone where the realized and desired final forging shapes do not coincide. The sensitivities of the objective function, nodal coordinates and nodal velocities with respect to the design variables are developed in detail. A procedure for competing the sensitivities of history-dependent functions is presented. The remeshing procedure and the interpolation/transfer of the history-dependent perameters, such as effective strain, are stated. The procedures of sensitivity analysis baaed preform die design are also described. In addition, a method for the adjustment of the volume loss resulting from the finite element analysis is given in order to make the workpiece volume consistent in each optimization iteration. The method developed in this paper is used to design the preform die shape of H-shaped forging processes, including plane strain and axisymmetric deformations. The results show that a flashless forging with a complete die fill is realized using the optimized preform die shape.
Journal of the Global Power and Propulsion Society, 2021
Methodologies to quantify the impact of manufacturing uncertainties in 3D CFD based design strategies have become increasingly available over the past years as well as optimization under uncertainties, aiming at reducing the systems sensitivity to manufacturing uncertainties. This type of non-deterministic simulation depends however strongly on a correct characterization of the manufacturing variability. Experimental data to characterize this variability is not always available or in many cases cannot be sampled in sufficiently high numbers. Principal Component Analysis (PCA) is applied to the sampled geometries and the influence of tolerances classes, sample size and number of retained deformation modes are discussed. It is shown that the geometrical reconstruction accuracy of the deformation modes and reconstruction accuracy of the CFD predictions are not linearly related, which has important implications on the total geometrical variance that needs to be retained. In a second app...
The correlation between design variables needs to be taken into consideration in the RBDO of mechanical components because an optimum design found without consideration of the correlation could be a weak or over design, or unreliable design when the correlation exists among the system variables. To clearly illustrate the significant effect of the correlation of random variables on the efficiency and effectiveness of the RBDO process, in this work, a case study, which is the RBDO of a cantilever beam with correlated random variables, is carried out using Monte Carlo Simulations (MCS). From the results of the simulations, it can be possible to conclude that an increase in correlation coefficient significantly increases the difficulty of finding the optimum point in RBDO problems. However, the influence of correlation coefficient on the RBDO performance can vary depending on the complexity of a design problem. The main critical point is that correlation coefficient must be accurately d...