Modelling of Temperature Distribution in Laser Assisted Metal-Plastic (LAMP) Joining Process
Keywords:
LAMP, temperature distribution, thermal modelingAbstract
Trends towards cost effectiveness, weight reduction and production flexibility in industrial production and manufacturing processes has led to a growing interest in hybrid components where two or more dissimilar materials are joined to achieve specifically optimized characteristics. Polymer and metals have become the most widely used combination of materials due to their superior characteristics. Polymers are lightweight and have a high degree of formability while metals have desirable properties such as high stiffness, high thermal conductivity and machinability. However, joining of these dissimilar materials presents challenges arising from their differences in chemical, mechanical and thermal properties. Joints are conventionally produced using adhesive bonds or glues, bolts, screws, and rivets. These conventional techniques have various limitation including low design flexibility, slow rate of joining and environmental restrictions. Laser Assisted Metal Plastic (LAMP)
joining is a recently developed method that can adress such limitation. This technique is fast,easy to automate, and the joint is stable for
a long period because of the direct utilization of a base plastic. However, the LAMP process has not fully been understood especially the thermal phenomena during the joining process. The need to predict the laser joining behaviour is very important because it forms a prerequisite for optimising the process parameters, thus improving the joint quality. This paper presents a model for prediction of temperature distribution in LAMP process, taking into consideration the different thermal properties of the materials. Finite element
method (FEM) is employed for the model development. The heat transfer from the laser source is analyzed at the interface where the
observation made will form a basis for further studies regarding the quality of the joints and important process parameters.
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Copyright (c) 2022 Francis Njihia, Bernard W. Ikua, Alphonse Niyibizi

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