Current trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.

/pdf/how-do-digital-engineering-and-included-ai-based-assistance-4e6a548rsk.pdf

How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers

Within the Transregional Collaborative Research Centre 73 (SFB/TR 73) a self-learning engineering workbench (SLASSY) is being developed. SLASSY assists product developers in designing sheet-bulk metal formed (SBMF) parts by computing product properties based on given product and process charac-teristics. SLASSY enables product developers to evaluate the manufacturabil-ity of their current part design. For this, SLASSY uses data from manufactur-ing experts to create metamodels. Currently, it can handle product properties which apply for a whole part variant (on whole part level), for instance the minimum form filling degree. The further development of the SBMF manufac-turing technology requires the consideration of the product properties in higher detail (on local level). This requires a higher data density, that is, data for each part variant and product property need to be acquired on every point of interest. Due to the increased amount of data, the currently used data mining algorithms in SLASSY for creating the metamodels cannot be reused. To face this challenge, deep learning algorithms are utilized which are good in processing big data. In this contribution, two approaches for the use of deep learning to compute product properties on local level are presented.

Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteilei-genschaften

Approach and application to transfer heterogeneous simulation data from finite element analysis to neural networks

Titel, Kurzzusammenfassung, Abstract, Inhalt und Verzeichnisse 
Einleitung und Zielsetzung 
Theorie 
Material und Methoden 
Ergebnisse und Diskussion 
Schlussfolgerungen und Ausblick 
Zusammenfassung 
Literatur 
Glossar 
Danksagung 
Anhang

Bestimmung der Messunsicherheit für die Verfahren und Methoden zur Bodenanalytik des Anhanges 1 der Bundes-Bodenschutz- und Altlastenverordnung

Finite Element Analysis (FEA) is a very efficient tool for optimizing product performance and quality. Hence, more simulation engineers with a lot of experience are needed, but they are not available. Consequently other users, such as design engineers, should be able to perform valid, reliable FEA. One of the goals of the research cooperation FORPRO˛ is to create a knowledge-based FEA assistance system with an integrated plausibility check for structural mechanics. Within this paper a methodology for a plausibility check using spherical detector surfaces is presented. Thereby it is possible to reduce any FE simulation to matrices of fixed size for each boundary condition and each FEA result variable. The created matrices can then be combined to form a single larger image. These images can afterwards be classified as plausible or implausible by a Deep Learning Neural Network.

FEA-Assistenzsystem – Plausibilitätsprüfung für Finite-Elemente-Simulationen mittels sphärischen Detektorflächen

In modern product development, the use of sophisticated simulation tools for assessing the effects of design changes on the intended product behavior is essential. However, setting up valid simulations requires expert knowledge, acquired skills, and sufficient expertise. Design engineers, who perform finite element analysis (FEA) infrequently, must be assisted and their FEA results need to be checked for plausibility. An automatic plausibility check for finite element (FE) simulations in linear structural mechanics can identify non-plausible simulations and warn the user to interpret the results cautiously or ask for expert help. In this context, currently available tools can only compare very similar simulations. However, as the amount of available simulation data in the industry increases more and more, a data-driven simulation check is an obvious next step. Nevertheless, the question arises how simulation data of very different parts and simulations can be transferred to a single software tool, how this tool can learn the relevant rules behind plausible simulations, and how it can be applied to new simulations. In this context, it is especially important to train a metamodel that is able to generalize the rules so that it can later on be applied to unknown simulations. This paper presents an approach to transfer different FE meshes, corresponding FE results and boundary conditions to an individual matrix of fixed size for very different structural mechanic FE simulation. The novel approach uses spherical detector surfaces to project three-dimensional information on its surface. It allows generating the so-called “DNA of an FE simulation”; classification algorithms i.e. Support Vector Machines or Deep Learning Neural Networks such as Convolutional Neural Networks (CNN) can then classify this information. The whole methodology reduces the dimension of a 3D finite element simulation to a 2D matrix of numeric values. The matrix contains all the relevant information for the classification in “plausible” or “non-plausible”. An implausible simulation contains errors, which would be quickly identified by an experienced simulation engineer, whereas a plausible simulation does not contain such errors. As less experienced simulation users in design departments are not trained to find such errors in their simulation setup, they cannot detect them and take adequate countermeasures. In the paper, every single step of the novel methodology for plausibility checking of structural mechanics simulations will be illustrated and explained in detail for simplified parts and corresponding simulations.

/pdf/methodology-for-plausibility-checking-of-structural-59gppjwfst.pdf

Methodology for plausibility checking of structural mechanics simulations using deep learning on existing simulation data

Influence of beam shape on spatter formation during PBF-LB/M of Ti6Al4V and tungsten powder

Laser Metal Deposition of Carbide-reinforced low-alloyed steel Bainidur AM

Surface layer porosity and surface quality in dependency of contour scanning strategy of Ti6Al4V parts

The commonly used Gaussian intensity distribution during the laser-based processing of metals can negatively affect melt pool stability, which might lead to defects such as porosity, hot cracking, or poor surface quality. Hot cracking is a major factor in limiting production rates of high-strength aluminium alloys in laser-based processes such as welding or the powder bed fusion of metals (PBF-LB/M). Going away from a Gaussian intensity distribution to ring-shaped profiles allows for a more even heat distribution during processing, resulting in more stable melt pools and reduced defect formations. Therefore, the aim of this study is to investigate the influence of different laser beam profiles on the processing of high-strength aluminium alloys by using a multicore fiber laser, allowing for in-house beam shaping. Single weld tracks on the aluminium alloy EN AW-5083 are produced with varying laser powers and weld speeds, as well as different beam profiles, ranging from Gaussian intensity distribution to point/ring profiles. The molten cross sections are analyzed regarding their geometry and defects, and the surface roughness of the weld tracks is measured. By using point/ring beam profiles, the processing window can be significantly increased. Hot cracking is considerably reduced for weld speeds of up to 1000 mm/s compared to the Gaussian beam profile. Furthermore, the melt pool width and depth are more stable, with varying parameters for the point/ring profiles, while the Gaussian beam tends to keyhole formation at higher beam powers. Finally, a strong decrease in surface roughness for the point/ring profiles, accompanied by a significantly reduced humping effect, starting even at lower beam powers of 200 W, can be observed. Therefore, these results show the potential of beam shaping for further applications in laser-based processing of high-strength aluminium alloys.

/pdf/influence-of-novel-beam-shapes-on-laser-based-processing-of-19q6ziip.pdf

Richard Rothfelder

Papers

Methodology for plausibility checking of structural mechanics simulations using deep learning on existing simulation data

Influence of beam shape on spatter formation during PBF-LB/M of Ti6Al4V and tungsten powder

Laser Metal Deposition of Carbide-reinforced low-alloyed steel Bainidur AM

Surface layer porosity and surface quality in dependency of contour scanning strategy of Ti6Al4V parts

Influence of Novel Beam Shapes on Laser-Based Processing of High-Strength Aluminium Alloys on the Basis of EN AW-5083 Single Weld Tracks