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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

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Autodesk® Moldflow® simulation software provides plastic injection molding and compression molding insights to improve manufactured part quality. Advanced tools and a simplified user interface help you address manufacturing challenges such as part warpage, cooling channel efficiency, material selection, and cycle time reduction.


Autodesk Moldflow simulations provide analysts insights to help predict and possibly prevent part defects such as sink marks, weld lines, and part warpage. It also allows for exploring options that reduce cycle time.


Autodesk Moldflow is used for simulating the plastic injection molding process and the plastic compression molding process.\n \nIt enables engineers, analysts, and mold designers to optimize material choice, process settings, cooling efficiency, and feed system designs to minimize the risk of poor molded part quality.\n \nAutodesk Moldflow simulations provide analysts insights to help predict and possibly prevent part defects such as sink marks, weld lines, and part warpage. It also allows for exploring options that reduce cycle time.\n"}]},"@type":"Question","name":"Who uses Autodesk Moldflow?","acceptedAnswer":["@type":"Answer","text":"
\n
Engineers and analysts who are interested in understanding how plastic material is flowing through a mold cavity, and the resulting part quality.\n
Plastic process engineers who are interested in understanding the molding process of a given plastic part and gaining insights into a processing window.\n
Mold tool designers and engineers who are interested in optimizing a mold\u2019s cooling design or the mold\u2019s runner or gating design.\n\n"],"@type":"Question","name":"What is the difference between Moldflow Adviser and Moldflow Insight?","acceptedAnswer":["@type":"Answer","text":"Moldflow Adviser provides manufacturability guidance and directional feedback for thermoplastic part and mold designs through high-level material flow, mold cooling, and part warpage results.\n \nMoldflow Insight provides detailed material flow, cooling, and warpage results for both injection molding and compression molding processes. Additional control on materials, machines, molds, and processes is possible with Moldflow Insight.\n \nAutomation with scripts/API accelerate setup and post-processing capabilities. Thermoplastic and thermoset materials are supported, along with customization of materials for part inserts and mold inserts. Advanced molding methods, such as gas-assisted injection molding, injection compression molding, co-injection molding, microcellular injection molding, and many more, are available to discover the best option for your part. Moldflow Insight also has advanced customization of the process including the ability to select the molding machine, specify valve gates, adjust mold materials, solve for a Design of Experiments (DOE) or parametric study, and much more.\n"],"@type":"Question","name":"Which operating system does Moldflow run on?","acceptedAnswer":["@type":"Answer","text":"Moldflow Adviser runs on Microsoft® Windows®. See Moldflow Adviser system requirements for details.\n \nMoldflow Insight and Moldflow Synergy can run on Microsoft® Windows® and Linux®. See Moldflow Insight and Synergy system requirements for details\n"],"@type":"Question","name":"Does Autodesk Moldflow support cloud solving?","acceptedAnswer":["@type":"Answer","text":"Yes, Moldflow Insight has a cloud solving option using Autodesk Tokens. The cost of cloud solving varies between the simulation study types. This cloud solve is in addition to the Moldflow Insight local solve and remote server solve options.\n \nMoldflow Adviser does not support cloud solving.\n"],"@type":"Question","name":"Is there a certification program for Moldflow?","acceptedAnswer":["@type":"Answer","text":"Yes, there are three certification levels for Autodesk Moldflow: Associ

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Moldflow® simulation software provides plastic injection molding insights to improve manufactured part quality. Advanced tools and a simplified user interface help you address manufacturing challenges such as part warpage, cooling channel efficiency, and cycle time reduction.


Autodesk® Moldflow® simulation software provides plastic injection molding and compression molding insights to improve manufactured part quality. Advanced tools and a simplified user interface help you address manufacturing challenges such as part warpage, cooling channel efficiency, material selection, and cycle time reduction.


Autodesk Moldflow simulations provide analysts insights to help predict and possibly prevent part defects such as sink marks, weld lines, and part warpage. It also allows for exploring options that reduce cycle time.


Autodesk Moldflow is used for simulating the plastic injection molding process and the plastic compression molding process.\n \nIt enables engineers, analysts, and mold designers to optimize material choice, process settings, cooling efficiency, and feed system designs to minimize the risk of poor molded part quality.\n \nAutodesk Moldflow simulations provide analysts insights to help predict and possibly prevent part defects such as sink marks, weld lines, and part warpage. It also allows for exploring options that reduce cycle time.\n"}]},"@type":"Question","name":"Who uses Autodesk Moldflow?","acceptedAnswer":["@type":"Answer","text":"
\n
Engineers and analysts who are interested in understanding how plastic material is flowing through a mold cavity, and the resulting part quality.\n
Plastic process engineers who are interested in understanding the molding process of a given plastic part and gaining insights into a processing window.\n
Mold tool designers and engineers who are interested in optimizing a mold\u2019s cooling design or the mold\u2019s runner or gating design.\n\n"],"@type":"Question","name":"What is the difference between Moldflow Adviser and Moldflow Insight?","acceptedAnswer":["@type":"Answer","text":"Moldflow Adviser provides manufacturability guidance and directional feedback for thermoplastic part and mold designs through high-level material flow, mold cooling, and part warpage results.\n \nMoldflow Insight provides detailed material flow, cooling, and warpage results for both injection molding and compression molding processes. Additional control on materials, machines, molds, and processes is possible with Moldflow Insight.\n \nAutomation with scripts/API accelerate setup and post-processing capabilities. Thermoplastic and thermoset materials are supported, along with customization of materials for part inserts and mold inserts. Advanced molding methods, such as gas-assisted injection molding, injection compression molding, co-injection molding, microcellular injection molding, and many more, are available to discover the best option for your part. Moldflow Insight also has advanced customization of the process including the ability to select the molding machine, specify valve gates, adjust mold materials, solve for a Design of Experiments (DOE) or parametric study, and much more.\n"],"@type":"Question","name":"Which operating system does Moldflow run on?","acceptedAnswer":["@type":"Answer","text":"Moldflow Adviser runs on Microsoft® Windows®. See Moldflow Adviser system requirements for details.\n \nMoldflow Insight and Moldflow Synergy can run on Microsoft® Windows® and Linux®. See Moldflow Insight and Synergy system requirements for details\n"],"@type":"Question","name":"Does Autodesk Moldflow support cloud solving?","acceptedAnswer":["@type":"Answer","text":"Yes, Moldflow Insight has a cloud solving option using Autodesk Tokens. The cost of cloud solving varies between the simulation study types. This cloud solve is in addition to the Moldflow Ins

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MATLAB and Simulink 2023 SYSTEM RECOMMENDATIONS


System requirements for MATLAB and Simulink family of software 2023 (Windows)


Operating System 64-bit Microsoft® Windows® 10. or Windows 11.




ZWCAD & ZWCAD Mechanical 2023 SYSTEM RECOMMENDATIONS


System requirements for ZWCAD & ZWCAD Mechanical family of software 2023 (Windows)


Operating System 64-bit Microsoft® Windows® 10.




Adobe 2023 SYSTEM RECOMMENDATIONS


System requirements for Adobe family of software 2023 (Windows)


Operating System 64-bit Microsoft® Windows® 10. or Windows 11.

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After studying mechanical engineering, he received his doctoral degree from RWTH Aachen University. Following a senior vice-director position at IKV, he started his industrial career in 2005 at the plastics processing company RKW SE, latterly as Managing Director of RKW Sweden A.B. in Helsingborg/Sweden. He participated in the Program for Executive Development at the International Institute for Management Development (IMD) in Lausanne, Switzerland. Hopmann received the Innovation Award of Germany's federal state North Rhine-Westphalia in 2014. He has been appointed visiting professor at the Beijing University of Chemical Technology, Beijing/China in 2017 and fellow of the Society of Plastics Engineers, CT/USA, in 2019. Hopmann serves as international representative of the Polymer Processing Society since 2021 and is member of the board of directors and the scientific advisory board as well as chairman of the board of the material engineering division of the VDI - The Association of German Engineers since 2022.


Raj Krishnaswamy is currently the Vice-President of Polymers R&D at CJ Biomaterials (Industrial Biotechnology Division). He has 20+ years of polymers R&D experience at Chevron Phillips Chemical, Metabolix and Braskem prior to his current stint at CJ. Raj is a co-inventor on 50+ patents and a co-author on 40+ publications. He is a Fellow of the Society of Plastics Engineering (SPE), has also been recognized with the Research/Technology lifetime achievement award by the SPE and is an alumnus of the NAE Frontiers of Engineering. Raj also serves on the advisory boards of the Macromolecules Innovation Institute at Virginia Tech and the Chemical Engineering Department at the University of Kentucky.


Dr. Avraam I. Isayev is Distinguished Professor Emeritus of Polymer Engineering at University of Akron and former Editor-in-Chief of Advances in Polymer Technology. He is a co-founding faculty member of the department. He received his Ph.D. in Polymer Science and Engineering, Institute of Petrochemical Synthesis of USSR Academy of Sciences, Moscow; M.Sc. in Applied Mathematics, Institute of Electronic Machine Building, Moscow, USSR; M.Sc.in Chemical Engineering, Azerbaijan Institute of Oil and Chemistry, Baku, USSR. Prior to joining the University in 1983, Isayev conducted research at Cornell University, Technion, USSR Academy of Sciences, and State Research Institute of Nitrogenic Industry, USSR. His research interests focus in polymer and composite processing, process modeling, rheo-optics, rheology and constitutive equations of polymers, oil products and disperse systems; the injection, co-injection, transfer, compression and gas-assisted injection molding; processing of selfreinforced or in-situ composites based on blends of flexible and thermotropic liquid crystalline polymers; decrosslinking of thermosets, devulcanization of rubbers and in-situ copolymer formation in immiscible blends with the aid of ultrasonic waves; replacement of petroleum olis in rubbers by modified soybean oils; high temperature and high performance composites and nanocomposites. Isayev has co-authored 4 editions of 1 monograph on rheology, edited or co-edited 8 books, published 274 papers in journals, 37 chapters in books, 8 papers in encyclopedias, 176 papers in conference proceedings. He holds 30 patents. He advised 49 PhD, 42 MS students and 31 postdocs and visiting scientists. He serves on editorial and advisory boards of many journals.
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