Abstract The progressively more demanding needs of emissions and costs reduction in the transportation industry are pushing engineers towards the use of increasingly lightweight structures. This goal can be achieved only if dissimilar and/or new materials, including polymers and composites, are joined together to create complex structures. Conventional fusion welding processes have often been proven inadequate to this task because of the high heat input reducing the joint mechanical properties or even making the joining process impossible. Joining by forming technologies take advantage on the plastic deformation to create sound joints out of even very dissimilar materials. Over the last 25 years, several new processes, with increasing potential in effectively joining virtually every structural material, have been invented and developed. In the paper, a comprehensive overview of the most utilized joining by forming processes is given. For each process, an analysis of the current research trends and hot topics is provided, highlighting strengths and weaknesses for industrial applications. 

/pdf/joining-by-forming-technologies-current-solutions-and-future-3v7arg9n.pdf

Joining by forming technologies: current solutions and future trends

/pdf/joining-by-forming-technologies-current-solutions-and-future-1h3sq2uh.pdf

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%.

Directed Energy Deposition-Arc (DED-Arc) and Numerical Welding Simulation as a Hybrid Data Source for Future Machine Learning Applications

Abstract In this paper, the development of algorithm-based process models for the mechanical joining process self-pierce riveting with semi-tubular rivet (SPR-ST) is described. Therefore, an extensive experimental and numerical database regarding the SPR-ST process and strength of steel and aluminum joints with tensile strengths of the sheets between 200 and 1000 MPa was generated for the building of the models. This process data could then be used for the training and evaluation of different prediction algorithms. Furthermore, the simulation data is applied to predict the entire contour (mesh) of non-simulated joints. This includes the visualization of output values such as strains, stresses and damage for each element and node of the mesh. That approach enables to obtain more information about the joint than just discrete values such as interlock or strength. 

/pdf/algorithm-based-design-of-mechanical-joining-processes-1os9ppr9.pdf

Algorithm-based design of mechanical joining processes

Currently the design of mechanical joining processes like self-pierce riveting with semi-tubular rivet (SPR-ST) and clinching for production is subject to complex and experimental test series in which process parameters such as the rivet or die geometry are varied iteratively and based on experience until a suitable joint contour and strength is achieved. To simplify the use of mechanical joining technologies these development cycles and thereby the effort for implementation into production must be reduced. In this paper, the numerical data-acquisition and the development of algorithm-based and analytical models for strength prediction for SPR-ST and Clinching is described. Therefore, an extensive experimental and numerical database regarding the SPR-ST process and strength of steel and aluminum joints with tensile strengths of the sheets between 200 - 1000 MPa was generated for the building of the models. This process data could then be used for the training and evaluation of different prediction models. The goal of the research presented here is to enable an immediate prediction of the quasi-static joint strength, based on the input parameters such as properties of the parts to be joined and process parameters.

Julius Georgi

Papers

Gathering of Process Data through Numerical Simulation for the Application of Machine Learning Prognosis Algorithms