https://popups.uliege.be/esaform21/index.php?id=1790 Publications of Auteurs Benjamin Klusemann fr 0 https://popups.uliege.be/esaform21/index.php?id=4327 This work evaluates the viability of applying Friction Riveting as an alternative for the assembly of components on printed circuit boards (PCBs). The popular press-fit technology for assembling components on PCBs consists of a pin inserted tightly into a relatively smaller hole, resulting in good electrical and mechanical properties. However, some limitations are highlighted, such as numerous processing steps and the need for predrilled holes. Friction Riveting is based on mechanical fastening and friction welding principles, where polymeric components are joined with metallic rivets through frictional heating and pressure. The main benefits of using Friction Riveting in PCBs compared with fit-press are (i) a reduced number of processing steps and (ii) shorter joining cycles, because there is no pre-drilling involved with fasteners anchored within the PCB in a single step. The joints were manufactured using 5 mm diameter AA-2024-T3 rivets and 1.5 mm thick glass-fiber-reinforced epoxy laminates (FR4-PCB). It is shown for the first time that it is possible to deform metallic rivets within thin composite plates at a reduced diameterto-thickness ratio. The feasibility study followed a one-factor-a-time approach for parameter screening and optical microscopy assessed joint formation of the deformed rivets inside the laminates through volumetric ratio (VR). The joints present significant deformation (VR=0.5) at the tip of the rivet inserted into overlapped PCBs plates, with thicknesses below 3.0 mm, which is considered the lowest achieved so far with Friction Riveting. Thu, 01 Apr 2021 18:09:34 +0200 Mon, 12 Apr 2021 11:56:12 +0200 https://popups.uliege.be/esaform21/index.php?id=4327 https://popups.uliege.be/esaform21/index.php?id=2589 The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining techniques. Whereas defects such as pores and hot cracking are often an issue in fusion-based joining processes, via solid-state joining processes they can be avoided to enable high-quality welds. To define an optimal process window for obtaining anticipated joint properties, numerous time and cost consuming experiments are usually required. Building a predictive model based on regression analysis enables the identification and quantification of process-property relationships. On the one hand, mechanical property and performance predictions based on specific process parameters are needed, on the other hand, inverse determination of required process parameters for reaching desired properties or performances are demanded. If these relations are obtained, optimized process parameter sets can be identified while vast numbers of required experiments can be reduced, as underlying physical mechanisms are utilized. In this study, different regression analysis algorithms, such as linear regression, decision trees and random forests, are applied to the refill Friction Stir Spot Welding process for establishing correlations between process parameters and joint properties. Experimental data sets used for training and testing are based on a Box-Behnken Design of Experiments (DoE) and additional test experiments, respectively. The machine-learning based regression analyses are benchmarked against linear regression and DoE statistics. The results illustrate a decryption of relationships along the process-property chain and its deployment to predict mechanical properties governed by process parameters. Wed, 24 Mar 2021 18:27:26 +0100 Thu, 27 May 2021 09:56:25 +0200 https://popups.uliege.be/esaform21/index.php?id=2589