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Luke Phillips
Luke Phillips

Moldflow Insight 2017 Key ~UPD~



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Moldflow Insight 2017 Key



Moldflow Adviser Ultimate 2017: 572I1CADdoctor for Autodesk Simulation 2017: 577I1Moldflow Design (Simulation DFM) 2017: 837I1Moldflow Insight Ultimate 2017: 575I1Moldflow Synergy 2017: 579I1


Interests in polymer rheology and processing, including thermodynamics and processing of polymer blends and liquid crystal polymers, proton exchange membranes using field alignment, mechanism of so-called sharkskin melt fracture, biodegradable composites for bone repair. (d) Selected awards and honors Tau Beta Pi; Phi Kappa Phi; Phi Eta Sigma; Secretary of the Society of Rheology, 1977 81, Treasurer, 1997-2015; Best Paper Award, SPE RETEC, 1988 and SPE ANTEC, 2005; Assoc. Editor, IEEE Transactions on Dielectrics and Electrical Insulation, 1991-2013; International Research Award, SPE, 1998, Distinguished Engineering Professor, 1999; SPE Fellow, 2000; International Award, SPE, 2002; A. T. DiBenedetto Distinguished Engineering Professor, 2002; SPE-PAD Founders' Award, 2004; Connecticut Academy of Science and Engineering, 2004; Service Award, Society of Rheology, 2017; Fellow, The Society of Rheology.


Robert Setter studied mechanical engineering at Technical University of Munich with majors in aerospace, light weight design and carbon fiber reinforced plastics. In 2017, he worked for 10 months as a visiting scholar in the field of resin-based additive manufacturing at the Polymer Engineering Center by Prof. Tim A. Osswald at the University of Wisconsin. In 2019, he wrote his master's thesis at BMW about additively manufactured injection molding polymer tools. Since March 2020, he is a research associate at the Professorship of Laser-based Additive Manufacturing by Prof. Dr.-Ing. Katrin Wudy at Technical University of Munich. He currently works in the field of innovative polymer-based additive manufacturing processes with a focus on powder- and resin-based technologies. This includes the conceptualization and development of new processes as well as the experimental analysis and characterization of polymer-based materials and additively manufactured parts.


Marco Klute studied mechanical engineering with specialization in plastics engineering at the University of Kassel/ Germany. His master thesis focused on two-component injection molding of polyamide and liquid silicone rubber (LSR) using different surface treatments. Since 2017 he has been working as a research fellow at the Institute for Material Engineering - Polymer Engineering at the University of Kassel, in the field of natural based composites as well as simulation and machine learning. He is currently working on two-component injection molding of bio-based polymers regarding material development and analytics of adhesion characteristics as well as the digitalization of injection molding processes using digital twins (supervisor Prof. Dr.-Ing. Heim).


Accompanying the era of digitalization into business leads to a new manufacturing concept called "Smart Factory". Smart Factories promise more efficient production processes, manifesting in integrated autonomous asset configuration and data-based decision-support in the operator's daily business. Therefore, mapping the production assets into the virtual world by establishing their interconnection and to enterprise software systems is essential. Although the origin of Smart Factories lies in 2011, it can be identified that integrated Smart Factories are rarely implemented and often comprise one encapsulated, specific use case. One main reason is a lack of standard semantics that serves as a basis for asset communication and providing decision-support. This contribution presents the foundations for enabling an integrated smart factory within the injection molding domain and demonstrates a real use case that presents the benefits of a smart injection molding factory. As a basis, the reference architecture model industry 4.0 (RAMI 4.0) with respect to the characteristics of an injection molding factory is analyzed. The RAMI 4.0 consists of three dimensions: business layers, life cycle and value stream, and enterprise hierarchy levels, and it allows concrete derivations of technologies, their requirements, and their interactions. Two of these technologies are Digital Twins and Digital Shadows. Digital Twins represents the asset virtually. The Digital Twin provides all relevant properties, ideally in a standardized manner, and pictures its master data (e.g., id, vendor, year of manufacture) as also its transaction data, e.g., current temperature or the number of cycles, into the virtual world. Another core technology is Digital Shadows. The Digital Shadow uses the exact dataset and suitable models for solving a specific task, e.g., calculating an optimal shopfloor configuration, and acts as decision-support for deciders. This contribution gives insights into building Digital Twins and Digital Shadows using the RAMI 4.0 concerning the characteristics of an injection molding factory. Furthermore, it will demonstrate how Digital Twins and Digital Shadows can interact autonomously by introducing semantics and dictionaries. Hence, a draft of a standardized dictionary for the injection molding domain, enriched with metadata that describes the dictionary terms semantically, is provided. This contribution focuses on the shopfloor level, i.e., production planning and control. Thus, a real case in scheduling production orders within an injection molding factory will be applied. Inside this use case, the Digital Shadow retrieves suitable optimization models for providing decision-support under consideration of trade-offs in different optimization objectives and the current condition of the injection molding assets using the corresponding Digital Twin. It can be shown that the Digital Shadow autonomously provide decision-support for production planners so they can initiate an optimal schedule.


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