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EN
Purpose: The aluminum/steel structural transition joints (STJs) are widely used in shipbuilding industry due to the advantages of joining these two materials with important weight savings while exploiting their best properties. The research objective is the evaluation of mechanical strength of explosion welded structural transition joints by imposing severe thermal loads in specific temperature ranges. Design/methodology/approach: Mechanical characterization of heat treated specimen have been performed to evaluate the influence of these thermal cycles on final joint resistance and evaluate the product in service. Several specimen aluminum/steel joints have been heated at specific temperature and time and air-cooled in compliance with a Central Composite Design (CCD) experimental plan to investigate the influenced of these factors on inter-metallic layers. Findings: The micro-hardness measurements have been pointed-out that the hardness of the inter-metallic compounds decreased with the temperature (values greater than 300°C). The processing time influence has been less significant in the observed temporal ranges. This trend has been also confirmed by evaluating the maximum strength of the bond Fe/Al interface. None of the specimens exhibited significant strength variation for thermal loads with temperature lower than 300°C, independently from the processing time. Research limitations/implications: This methodology is very useful to perform acceptance controls of STJ before use. Practical implications: These observations are very important to suggest the application of laser techniques to weld this type of joints, thanks to the very narrow localized thermal input. Originality/value: The paper presents an alternative and cheap way to assess quality of STJs from the mechanical point of view.
2
Content available remote Optimisation of injection moulded parts by using ANN-PSO approach
EN
Purpose: The aim of the work was the optimization of injection molded product warpage by using an integrated environment. Design/methodology/approach: The approach implemented took advantages of the Finite Element (FE) Analysis to simulate component fabrication and investigate the main causes of defects. A FE model was initially designed and then reinforced by integrating Artificial Neural Network to predict main filling and packing results and Particle Swarm Approach to optimize injection molding process parameters automatically. Findings: This research has confirmed that the evaluation of the FE simulation results through the Artificial Neural Network system was an efficient method for the assessment of the influence of process parameter variation on part manufacturability, suggesting possible adjustments to improve part quality. Research limitations/implications: Future researches will be addressed to the extension of analysis to large thin components and different classes of materials with the aim to improve the proposed approach. Originality/value: The originality of the work was related to the possibility of analyzing component fabrication at the design stage and use results in the manufacturing stage. In this way, design, fabrication and process control were strictly links
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