Currently, the concept of Industry 4.0 (I4.0) is reaching most companies by changing the characteristics of their production systems. The aim of this study was to determine the possibilities of applying I4.0 concepts and tools to the product development process in a small- to medium-sized enterprise (SME)—a food processing technology supplier that offers individually tailored solutions. The study employs the case study methodology along with conceptual studies. Based on the available I4.0 concepts/technologies, the areas of the process that could be improved were identified. The study concludes with proposing several modifications in the process in the context of I4.0. Some of the process stages can be shortened by implementing I4.0 tools. It was found that the length of the process can be shortened from 36 to 26 months, and possible gains also include energy savings. Technological tools, referred to as augmented/virtual reality, along with simulations and virtual manufacturing, seem to have particular applicability. The energy savings estimation emerged as a check gate evaluating if technological implementations are justified. Directions for further research with reference to I4.0 and studied processes are determined.

/pdf/application-of-industry-4-0-to-the-product-development-4oxx8v6qw3.pdf

Application of Industry 4.0 to the Product Development Process in Project-Type Production

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

Neural network-based build time estimation for additive manufacturing: a performance comparison

Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms

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

Sebastian Bickel

Papers

Comparing CAD part models for geometrical similarity: A concept using machine learning algorithms

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

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

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

A function-oriented surface reconstruction framework for reverse engineering