@inproceedings{570, abstract = {{Additive manufacturing (AM) has matured rapidly during the last years due to the advancement of AM machines and materials. Nevertheless, the widespread adoption of AM is still challenged by producing parts with reliable quality. The aim of this paper is t o introduce a first approach to apply in-situ monitoring for quality evaluation of produced parts. Based on the monitored data, a model is developed, in order to predict the quality of ready built parts.}}, author = {{Scheideler, Eva and Huxol, Andrea and Villmer, Franz-Josef}}, booktitle = {{Production Engineeringand Management}}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-946856-01-6}}, keywords = {{Nondestructive quality control, Predictive analytics, Metal model, Additive manufacturing}}, location = {{Pordenone, Italy}}, number = {{1}}, pages = {{89--100}}, title = {{{Nondestructive Quality Check of Additive Manufactured Parts Using Empirical Models}}}, year = {{2017}}, } @inproceedings{577, abstract = {{A rising number of product variants together with decreasing lot sizes are a result of the trend of individualization. Besides the upcoming organizational issues, changes in the production technologies are required. Direct digital manufacturing contributes to solve this problem by enabling the production of parts right from the CAD data.Process capability analysis is applied in several industries to prove the reliable compliance of products with quality requirements. As it is based on statistical methods, new challenges arise in the context of single-part production.The paper describes and compares different approaches for the adoption of process capability analysis for single-part production with special focus on additive manufacturing technologies. The statistical background and the applicability of different capability parameters are discussed. An overview of existing research work is given and supplemented by own approaches for the adoption of statistical methods for single-part production. The aim of the research work is to establish a first approach for the qualification of new technologies in single-part production.}}, author = {{Huxol, Andrea and Davis, Andrea and Villmer, Franz-Josef and Scheideler, Eva}}, booktitle = {{Production Engineering and Management}}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-946856-01-6}}, keywords = {{Statistical process control, Process capability analysis, Single-part production, Process optimization}}, location = {{Pordenone, Italy}}, number = {{1}}, pages = {{63--74}}, title = {{{Deployment of Process Capability Analysis for Single-Part Production}}}, year = {{2017}}, } @inproceedings{579, abstract = {{Selective Laser Melting (SLM) is a powder bed fusion process to produce additively metal parts. From the current point of view, it seems to be one of the most promising additive manufacturing technologies for the production of end use parts. An increasing number of examples prove the successful application of SLM for technical part production. Nevertheless, they also show the enormous effort that is still required to qualify the production process of every single part individually.The present paper gives an overview of the major influencing factors of the SLM process. To get a comprehensive research approach, existing publications on the topic are taken into account as well as own experimental work, evaluating the effects of the process parameters on the relative density of samples made from tool steel. The experimental setup and the results are described and opportunities for the further research work are discussed.}}, author = {{Huxol, Andrea and Scheideler, Eva and Villmer, Franz-Josef}}, booktitle = {{Production Engineering and Management}}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-946856-01-6}}, keywords = {{Selective laser melting, Additive manufacturing, Process parameters, Process optimization}}, location = {{Pordenone, Italy}}, number = {{1}}, pages = {{13--34}}, title = {{{Influencing Factors on Part Quality in Selective Laser Melting}}}, year = {{2017}}, } @inproceedings{580, abstract = {{Additive Manufacturing (AM) is increasingly used to design new products. This is possible due to the further development of the AM-processes and materials. The lack of quality assurance of AM built parts is a key technological barrier that prevents manufacturers from adopting. The quality of an additive manufactured part is influenced by more than 50 parameters, which make process control difficult. Current research deals with using real time monitoring of the melt pool as feedback control for laser power. This paper illustrates challenges and opportunities of applying statistical predictive modeling and unsupervised learning to control additive manufacturing. In particular, an approach how to build a feedforward controller will be discussed.}}, author = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}}, booktitle = {{ Production engineering and management : proceedings 7th international conference, September 28 and 29, 2017, Pordenone, Italy }}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-946856-01-6}}, keywords = {{Additive manufacturing, Process control, Predictive modeling, Predictive control}}, location = {{Pordenone, Italy}}, number = {{1}}, pages = {{3--12}}, title = {{{Quality Control of Additive Manufacturing Using Statistical Prediction Models}}}, volume = {{2017}}, year = {{2017}}, } @inproceedings{581, author = {{Scheideler, Eva and Villmer, Franz-Josef}}, booktitle = {{Rapid.Tech – International Trade Show & Conference for Additive Manufacturing}}, isbn = {{978-3-44645459-0}}, number = {{1}}, pages = {{10--24}}, publisher = {{Carl Hanser Verlag GmbH & Co. KG}}, title = {{{Anforderungen an integrierte Prozessketten in der Additiven Fertigung}}}, doi = {{10.3139/9783446454606.001}}, year = {{2017}}, } @article{609, abstract = {{Einfach, ohne Expertenwissen anzuwenden – solch ein Planungswerkzeug spart Zeit und Geld. Dieser Artikel stellt eine neue, effiziente Berechnungsmöglichkeit vor, die z.B. Vertriebsmitarbeiter oder Architekten befähigt, Risiken bezüglich Durch‐ oder Absturzsicherheit bei allseitig gelagerten Verglasungen früh in der Planung schnell abzuprüfen. Dabei werden statistische Modelle und Simulationsberechnungen eingesetzt. Verifiziert wurde die Methode gemäß DIN 18008 Teil 4 und Teil 6 mit den Möglichkeiten der Finite‐Element‐Rechnung und Referenzdatensätzen. Sie kann eine finale statische Beurteilung (z.B. prüffähige Statik) nicht ersetzen, doch sie kann im Lauf der Planung verlässliche Abschätzungen liefern und somit Geld und Zeit einsparen.}}, author = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea and Scheideler, Josef}}, issn = {{2509-7075}}, journal = {{ce/papers : Proceedings in Civil Engineering}}, number = {{1}}, pages = {{142--152}}, publisher = {{Ernst & Sohn, a Wiley brand }}, title = {{{Statistische Modellierung zur Unterstützung von Industrie 4.0 im Glasbau}}}, doi = {{10.1002/cepa.16}}, volume = {{1}}, year = {{2017}}, } @inproceedings{457, abstract = {{Additive Manufacturing (AM) increasingly enables the realization of structures, which have a much greater freedom of design und can therefore better use nature as a design ideal. Bionic design principles have already been introduced into general design approaches, and several topology optimization systems (TO) are available today to increase structural stiffness and to enable lightweight design. AM and TO, used in synergy, promise completely new application areas. However, staircase effects resulting from a layer-by-layer build process and unavoidable support structures which must be mechanically removed afterwards are disadvantageous with respect to surface texture and strength properties. The present article addresses the question of how far the notches resulting from the staircase effect of Additive Manufacturing and the support structures removed decrease the strength of components. Most engineers try to follow the inner flow of forces in a part’s design by smoothening surfaces in notched areas. Considering this, a elected component is investigated with finite element analysis (FEA) with special regard for the concentration of tress arising from surface notch effects. An outlook is given as regards how a reduction of the notch effect from the taircase effect can be achieved effectively.}}, author = {{Scheideler, Eva and Villmer, Franz-Josef and Adam, G. and Timmer, Mirco}}, booktitle = {{Production Engineering and Management Proceedings 6th International Conference}}, editor = {{Villmer, Franz-Josef and Padoano, Elio}}, isbn = {{978-3-946856-00-9}}, keywords = {{Additive Manufacturing, Topology optimization, Staircase effect, Support structures, Stress concentration, Lightweight construction, Design rules, Notch effect}}, location = {{Lemgo}}, number = {{1}}, pages = {{39--50}}, title = {{{Topology Optimization and Additive Manufacturing – A Perfect Symbiosis?}}}, year = {{2016}}, } @inproceedings{594, abstract = {{Due to steadily increased demand for customized products, as well as their enhanced complexity and shorter product lifecycles, companies in all industries require a reliable prediction of the expected product development costs from the very start of product realization. Incorrectly estimated project costs may lead to serious consequences in the course of a development project. For example, offers are most often based on such early cost estimations and consequently, a major safety margin has to be added, which may result in the refusal of an order. A too low estimation of the costs of aproduct development project, on the other hand, may result in a loss for the project.In this paper, a software tool is presented for the prediction of product development costs which offers the user the ability to create a more accurate prediction of project costs on the basis of a minimum of retrograde project information. By combining a parametric cost model and cost result with stochastic character, based on the Monte Carlo method, in one software system, it is possible to significantly improve projectcost estimations.}}, author = {{Otte, Andreas and Scheideler, Eva and Villmer, Franz-Josef}}, booktitle = {{Department of Production Engineering and Management}}, editor = {{Villmer, Franz-Josef and Padoano, Elio}}, isbn = {{978-3-946856-00-9}}, keywords = {{Cost prediction, Product realization projects, Monte Carlo method, Parametric cost model, Software tool}}, location = {{Lemgo}}, number = {{1}}, pages = {{281--292}}, title = {{{Project Cost Estimator - A Parameter-Based Tool to Predict Product Realization Costs at a Very Early Stage}}}, year = {{2016}}, } @inproceedings{472, abstract = {{In the context of Industrie 4.0 respectively direct digital manufacturing, seamless process chains are an important factor. The objective is to shorten the time between quoting for individually designed products and their production and delivery. Therefore, reliable automated and fast evaluation procedures are needed to ensure the quality of the individually designed products in terms of product safety and reliability. This paper aims to demonstrate how a metamodel, generated on simulated data, adapts to the type of product and delivers the required quality and evaluation procedure. The metamodel guarantees the requested characteristics of the final product without the consultation of human expert knowledge. As proof of concept, a simple, well-documented task from the field of construction has been chosen. The estimation from of the metamodel will meet all safety requirements, is based on the individual input variables and is confirmed without expert interaction. Fast, reliable prediction models deriving from complex simulation models are indispensable conditions for direct digital manufacturing. Using metamodels in automation contexts will be a foundation of manufacturing in future.}}, author = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}}, booktitle = {{Production engineering and management : proceedings 6th international conference, September 29 and 30, 2016 Lemgo, Germany }}, editor = {{Villmer, Franz-Josef and Padoano, Elio}}, keywords = {{Simulation, Metamodel, Computer experiment, Design of experiments}}, location = {{Lemgo}}, number = {{1}}, pages = {{269--280}}, publisher = {{Hochschule Ostwestfalen-Lippe}}, title = {{{Expert Knowledge Systems to Ensure Quality and Reliability in Direct Digital Manufacturing Enviroments}}}, volume = {{2016}}, year = {{2016}}, } @inproceedings{597, abstract = {{This paper is aimed to discuss current research using data mining techniques and industry statistics in production environments. The general research approach is based on the idea of using data mining processes and techniques of industry statistics to find rare and hidden patterns behind failures of complex components. A case study will be applied to illustrate how the technique is carried out and where the limits of this approach occur. The case study deals with a component supplier of printing machines, which received an increasing number of client complaints, all related to one distinct problem. The observed failures seem to occur only among clients with very high quality standards. The affected component undergoes a very complex production process with several steps in different departments. Every single production unit records data information from multiple process variables and at different points in time. In the beginning there was no understanding of the failure causes in production at all. Therefore a huge amount of production data had to be analyzed to find the pattern that discloses the failure. The data mining process starts with a first step in which the given data sets are prepared and then cleaned. Followed up by building a prediction model. The aim is to detect the root causes for failures and to predict potential failures in affected components. This paper shows how to use data mining to get the answer on pressing production failures. }}, author = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}}, booktitle = {{Production engineering and management : proceedings, 5th international conference, October 1 and 2, 2015, Trieste, Italy}}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-941645-11-0}}, keywords = {{Data mining, production failure, multi-variant analysis, multivariate process control, predictive modelling, case study}}, location = {{Trieste, Italy}}, number = {{1}}, pages = {{163--174}}, publisher = {{Hochschule Ostwestfalen-Lippe}}, title = {{{Data Mining: A Potential Detector to Find Failure in Complex Components}}}, year = {{2015}}, }