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This literature overview shows that some studies have investigated the optimization of the injection molding process for assembled foils. However, an in-depth examination of the prevailing forces during filling is still lacking. By analyzing numerically performed parameter studies, this paper aims to gain insight into how the injection molding settings influence the forces on the components.


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.


In the present paper, we have studied thermal properties and thermo-chemical stability of a medical-grade adhesive comprised of a cationic, cycloaliphatic epoxy resin system by using differential scanning calorimetry (DSC) and thermo-gravimetric analysis (TGA) techniques. Then, we have explored UV curability of the adhesive by performing a series of the UV-cure experiments using special photo-DSC (or p-DSC) technique and investigated relevant re

X-force Moldflow Insight 2013 Keygen Download Pc

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