Laminated Aluminum Composite Structure (LACS) has shown great potential for replacing traditional bulk aluminum parts, due to its ability to maintain low manufacturing costs and create complex geometries. In this study, a LACS, that consists of 20 aluminum layers joined by a structural tape adhesive, was fabricated and tested to understand its impact performance. Three impact tests were conducted: axial drop, normal and transverse three-point bending drop tests. Numerical simulations were performed to predict the peak loads and failure modes during impacts. Material models with failure properties were used to simulate the cohesive failure, interfacial failure, and aluminum fracture. Various failure modes were observed experimentally (large plastic deformation, axial buckling, local wrinkling, aluminum fracture and delamination) and captured by simulations. Cross-section size of the axial drop model was varied to understand the LACS buckling direction and force response. For three-point bending drop simulations, the mechanism causing the maximum plastic strain at various locations in the aluminum and adhesive layers was discussed. This study presents an insight to understand the axial and flexural responses under dynamic loading, and the failure modes in LACS. The developed simulation methodology can be used to predict the performance of LACS with more complex geometries.
Product Lifecycle Management (PLM) system requires consideration and ensuring efficient operating conditions for the most loaded parts in the product, not only at the product's design stage, but also at the production stage. Operational properties of the product can be significantly improved if we take into consideration the formation of the functional surfaces wear resistance parameters already at the planning stage of the technological process structure and parameters of the product's machining. The method of constructing predictive models of the influence of the technological process structure on the formation of a complex of product's operational properties is described in the article. The relative index of operational wear resistance of the machined surface, which is characterized by the use of different variants of the structure and parameters of this surface treatment, depends on the microtopographic state of the surface layer and the presence of cutting-induced residual stress. On the example of the eject pin machining it has been shown how the change in the structure of the manufacturing process from grinding to the turning by tool with the tungsten carbide insert affects the predicted wear resistance of the machined functional surface.
Material parameters identification by inverse analysis using finite element computations leads to the resolution of complex and time-consuming optimization problems. One way to deal with these complex problems is to use meta-models to limit the number of objective function computations. In this paper, the Efficient Global Optimization (EGO) algorithm is used. The EGO algorithm is applied to specific objective functions, which are representative of material parameters identification issues. Isotropic and anisotropic correlation functions are tested. For anisotropic correlation functions, it leads to a significant reduction of the computation time. Besides, they appear to be a good way to deal with the weak sensitivity of the parameters. In order to decrease the computation time, a parallel strategy is defined. It relies on a virtual enrichment of the meta-model, in order to compute q new objective functions in a parallel environment. Different methods of choosing the qnew objective functions are presented and compared. Speed-up tests show that Kriging Believer (KB) and minimum Constant Liar (CLmin) enrichments are suitable methods for this parallel EGO (EGO-p) algorithm. However, it must be noted that the most interesting speed-ups are observed for a small number of objective functions computed in parallel. Finally, the algorithm is successfully tested on a real parameters identification problem.
Resistance spotwelding is the most significant joining technique utilized in various industries, like automotive, boilers, vessels, etc., that are commonly subjected to variable tensile-shear forces due to the unsuitable use of the input spot welding variables, which mainly cause the welded joints failure during the service life of the welded assembly. So, in order to avoid such failures, the welding quality of some materials like aluminum must be improved taking into consideration the performance and weight saving of the welded structure. Thus, the need for optimizing the used welding parameters becomes essential for predicting a goodwelded joint.Accordingly, this study aims at investigating the influence of the spot welding variables, including the squeeze time, welding time, and current on the tensile-shear force of the similar and dissimilar lap joints for aluminum and steel sheets. It was concluded that the use of Taguchi design can improve the welded joints strength through designing the experiments according to the used levels of the input parameters in order to obtain their optimal values that give the optimum tensile-shear force as the response. As a consequence of the present work, the optimal spot welding parameters were successfully obtained.
Structural design analyses of industrial dye mixing machines, concerning mixing impeller geometries, mixing performances, and power requirements aren't generally of scientific quality. Our aim is to propose a practical method for minimizing execution time, using parametric design. In this study, Visual Basic API codes are developed in order to model the impellers in SolidWorks software, and then flow analyses are conducted. Thus, velocity values and moment/torque values required for mixing operation are determined. This study is carried out for different shaft rotational speeds and different impeller diameters. Flow trajectories are obtained. After that, frequency analyses are conducted and natural frequency values are obtained. In the scope of this study, two different impeller types are investigated.
This paper explores the parametric appraisal and machining performance optimization during drilling of polymer nanocomposites reinforced by graphene oxide/carbon fiber. The consequences of drilling parameters like cutting velocity, feed, and weight % of graphene oxide on machining responses, namely surface roughness, thrust force, torque, delamination (In/Out) has been investigated. An integrated approach of a Combined Quality Loss concept, Weighted Principal Component Analysis (WPCA), and Taguchi theory is proposed for the evaluation of drilling efficiency. Response surface methodology was employed for drilling of samples using the titanium aluminum nitride tool. WPCA is used for aggregation of multi-response into a single objective function. Analysis of variance reveals that cutting velocity is the most influential factor trailed by feed and weight % of graphene oxide. The proposed approach predicts the outcomes of the developed model for an optimal set of parameters. It has been validated by a confirmatory test, which shows a satisfactory agreement with the actual data. The lower feed plays a vital role in surface finishing. At lower feed, the development of the defect and cracks are found less with an improved surface finish. The proposed module demonstrates the feasibility of controlling quality and productivity factors.
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Archive of Mechanical
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