Springback optimization in automotive Shock Absorber Cup with Genetic Algorithm
01 Feb 2018-Vol. 5, pp 1
TL;DR: Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback in deep drawing of an automotive Shock Absorber Cup with finite element method.
Abstract: Drawing or forming is a process normally used to achieve a required component form from a metal blank by applying a punch which radially draws the blank into the die by a mechanical or hydraulic action or combining both. When the component is drawn for more depth than the diameter, it is usually seen as deep drawing, which involves complicated states of material deformation. Due to the radial drawing of the material as it enters the die, radial drawing stress occurs in the flange with existence of the tangential compressive stress. This compression generates wrinkles in the flange. Wrinkling is unwanted phenomenon and can be controlled by application of a blank-holding force. Tensile stresses cause thinning in the wall region of the cup. Three main types of the errors occur in such a process are wrinkling, fracturing and springback. This paper reports a work focused on the springback and control. Due to complexity of the process, tool try-outs and experimentation may be costly, bulky and time consuming. Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback. Finite-element based simulations have been used popularly for such purposes. In this study, the springback in deep drawing of an automotive Shock Absorber Cup is simulated with finite element method. Taguchi design of experiments and analysis of variance are used to analyze the influencing process parameters on the springback. Mathematical relations are developed to relate the process parameters and the resulting springback. The optimization problem is formulated for the springback, referring to the displacement magnitude in the selected sections. Genetic Algorithm is then applied for process optimization with an objective to minimize the springback. The results indicate that a better prediction of the springback and process optimization could be achieved with a combined use of these methods and tools.
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TL;DR: This research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process and demonstrate that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors.
Abstract: Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure.,The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process.,The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well.,The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.
26 citations
TL;DR: In this paper, an attempt is made to develop the aluminium-based nano metal matrix composite reinforced with graphene nanoparticles (GNP) by using the stir casting method, which reveals the uniform distribution of GNP into the matrix material.
Abstract: The development of a new class of engineering materials is the current demand for aircraft and automobile companies. In this context metal, composite materials have a widespread application in different areas of manufacturing sectors.,In this paper, an attempt is made to develop the aluminium-based nano metal matrix composite reinforced with graphene nanoparticles (GNP) by using the stir casting method. Different weight percentage (0.4%, 0.8% and 1.2% by weight) of GNPs are used to fabricate metal matrix composites (MMCs). The developed nanocomposites were further validated by density calculation and optical microstructures to discuss the distribution of GNPs. The tensile test was conducted to determine the strength of the developed MMCs and also supported by fractographic analysis. In addition to it, the Rockwell hardness test and impact test (toughness) with fracture analysis were also conducted to strengthen the present work.,The results reveal the uniform distribution of GNPs into the matrix material. The yield strength and ultimate tensile strength obtained a maximum value of 155.67 MPa and 170.28 MPa, respectively. The hardness value (HRB) is significantly increased and 84 HRB was obtained for the sample with AA1100/0.4% GNP, while maximum hardness value (94 HRB) was obtained for the sample AA1100/1.2% GNP. The maximum value of toughness 14.3 Jules/cm2 is recorded for base alloy AA1100 while increasing the reinforcement percentage, it decreases up to 9.7 Jules/cm2 for AA1100/1.2% GNP.,Graphene nanoparticles are used to develop nanocomposites, which is one of the suitable alternatives for heavy engineering materials such as steels and cast irons. It has improved microstructural and mechanical properties which makes it preferable for many engineering and structural applications.
17 citations
TL;DR: In this paper, a simplified mathematical model for determining the electromagnetic and electromotive force for the electromechanical shock absorber is presented. But the model does not take into account the operational modes of permanent magnet based on the calculation of a magnetic circle.
Abstract: A procedure for determining basic estimation parameters has been devised for the proposed structure of the electromechanical shock absorber. The procedure is based on a simplified mathematical model for determining the electromagnetic and electromotive force for the electromechanical shock absorber. Feature of the model is taking into consideration the operational modes of permanent magnet based on the calculation of a magnetic circle. The model devised makes it possible to perform approximate calculation of the shock absorber operational modes and could be used for solving the problem on the optimization of parameters for an electric shock absorber. We have verified adequacy of the constructed simplified mathematical model by comparing the results from calculating the mechanical characteristic for a shock absorber based on the simplified procedure and those obtained using a finite element method in the axial-symmetrical statement of the problem. There is a good match between the results from calculations based on the simplified procedure and from modeling a magnetic field using the method of finite elements. We have determined the geometric relationships between the elements of the structure that ensure the optimal uniform magnetic load on the elements of the magnetic circuit. The problem on the conditional two-criteria optimization of parameters for the electromechanical shock absorber has been stated. We have chosen constraints that are divided into the three following categories. Constraints for a permanent magnet demagnetization that make it possible to maintain operability of the permanent magnet. Constraints for a current density, which ensures the thermal modes in the shock absorber operation. Constraints for assembly and constraints for the parameters of an optimization problem, which enable the arrangement of a structure within the running part of a carriage. It has been proposed to choose the reduced volume of a shock absorber as a criterion, which predetermines the cost of constructing a shock absorber, and its efficiency as a criterion, which predetermines the recuperated energy of oscillations. The parameters were convoluted to a single objective cost function; the weights were defined. We have chosen, as an optimization method, the combined method that includes a genetic algorithm at the preliminary stage of the search. At the final stage of an optimization procedure an optimum is refined by using the Nelder-Mead method. The result from solving the optimization problem on the shock absorber's parameters is the defined optimal geometric dimensions and the number of turns in the winding of the electromechanical shock absorber.
8 citations
TL;DR: In this paper, ternary lithium, sodium, potassium carbonates/silica microcomposites as PCMs were synthesized by a sol-gel coating method and the effects of heat treatment on the performance of the ternaries carbonates and the microcom composites were studied.
Abstract: Microencapsulated phase change material (PCMs) is an effective thermal energy storage medium. In this paper, ternary lithium, sodium, potassium carbonates/silica microcomposites as PCMs were synthesized by a sol-gel coating method. The effects of heat treatment on the performance of the ternary carbonates and the microcomposites were studied. The composition, microstructure, phase change characteristics, and thermal stability of the microcomposites were characterized by Fourier transform infrared spectrophotometer (FTIR), scanning electron microscope (SEM), differential scanning calorimeter (DSC), and X-ray diffraction (XRD). Results show that different carbonate eutectics were detected with or without heat treatment. The microcomposite with heat-treated ternary carbonates exhibited a latent heat of 192.2 kJ/kg and an encapsulation ratio of 84.8%. The supercooling degree was reduced by 43.7%. No leakage was detected after phase change cycles, indicating good thermal stability of the microcomposite. The results indicate that the microcomposites have great potential in high-temperature thermal energy storage.
4 citations
01 Jan 2019
TL;DR: In this article, the importance of forming parameters on the responses: punch force and springback in U-Bending of SS 304 has been analyzed and the optimum conditions have been determined based on their effect on punching force and Springback of the sheet metal.
Abstract: Springback is a crucial factor that influences the feature of sheet metal in the sheet metal forming (SMF). In SMF operations, springback of the component during unloading mostly determines whether the component confirms to the design dimensions and tolerances. The aim of the current work is to analyze the importance of forming parameters on the responses: punch force and springback in U-Bending of SS 304. Strip length, punch speed and lubricant with three levels each have been considered in the current work as the forming parameters. The effects of different process parameters on U-bending of sheet metal have been investigated by conducting experiments on SS 304. Experiments have been conducted as per Taguchi’s L9 orthogonal array. The optimum conditions have been determined based on their effect on punch force and springback of the sheet metal.
3 citations
References
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01 Jan 1989
2,298 citations
"Springback optimization in automoti..." refers methods in this paper
...In sixties Professor John Holland proposed this technique [22]....
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TL;DR: In this paper, the displacement adjustment method (DA) is used to adjust the trial die design until the target part shape is achieved, hence the term "displacement adjustment method" has been applied.
253 citations
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