scholarly journals Development of a Novel Design Strategy for Moving Mechanisms Used in Multi-Material Plastic Injection Molds

2021 ◽  
Vol 11 (24) ◽  
pp. 11805
Author(s):  
Fátima de Almeida ◽  
Vitor F. C. Sousa ◽  
Francisco J. G. Silva ◽  
Raúl D. S. G. Campilho ◽  
Luís P. Ferreira
Keyword(s):  
Injection Molding ◽  
Environmental Issues ◽  
Design Strategy ◽  
Plastics Processing ◽  
Plastics Injection Molding ◽  
Reduced Costs ◽  
Plastic Injection ◽  
Novel Design ◽  
Minimize Costs

Plastics injection molding is a sector that is becoming increasingly competitive due to the environmental issues it entails, pressuring consumers to reduce its use. Thus, plastics processing companies attempt to minimize costs, with the aim of increasing competitiveness. This pressure is transmitted to the mold manufacturers, as the mold conditions the equipment that it is used for, which may have significantly different amortization costs. The present work aimed to design a novel mechanism able to deal with the necessary movements in 2K injection molding in a more compact way. A novel hybrid mechanical and hydraulic movement was developed. More compact movements lead to smaller molds, which can be used on smaller injection machines, leading to reduced costs. This methodology consists of multiplying a disproportionate movement to the mold through several movements, which results in a slightly more complex, but much more compact, system for molds devoted to multi-material injected parts.

A novel design strategy of a practical carbon anode material from a single lignin-based surfactant source for sodium-ion batteries

2020 ◽  
Vol 56 (45) ◽  
pp. 6078-6081 ◽  
Author(s):  
Changhao Li ◽  
Yi Sun ◽  
Qiujie Wu ◽  
Xin Liang ◽  
Chunhua Chen ◽  
...  
Keyword(s):  
Anode Material ◽  
Design Strategy ◽  
Sodium Ion ◽  
Carbon Anode ◽  
Sodium Ion Batteries ◽  
Lignin Sulfonate ◽  
Novel Design

A schematic illustration showing the preparation of HCM from a single sodium lignin sulfonate source and the process of Na storage.

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

scholarly journals Energy and Eco-Impact Evaluation of Fused Deposition Modeling and Injection Molding of Polylactic Acid

2021 ◽  
Vol 13 (4) ◽  
pp. 1875
Author(s):  
Emmanuel Ugo Enemuoh ◽  
Venkata Gireesh Menta ◽  
Abdulaziz Abutunis ◽  
Sean O’Brien ◽  
Labiba Imtiaz Kaya ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Injection Molding ◽  
Polylactic Acid ◽  
Fused Deposition Modeling ◽  
Plastic Injection Molding ◽  
Measurement Models ◽  
Fused Deposition ◽  
Dog Bone ◽  
Deposition Modeling ◽  
Plastic Injection

There is limited knowledge about energy and carbon emission performance comparison between additive fused deposition modeling (FDM) and consolidation plastic injection molding (PIM) forming techniques, despite their recent high industrial applications such as tools and fixtures. In this study, developed empirical models focus on the production phase of the polylactic acid (PLA) thermoplastic polyester life cycle while using FDM and PIM processes to produce American Society for Testing and Materials (ASTM) D638 Type IV dog bone samples to compare their energy consumption and eco-impact. It was established that energy consumption by the FDM layer creation phase dominated the filament extrusion and PLA pellet production phases, with, overwhelmingly, 99% of the total energy consumption in the three production phases combined. During FDM PLA production, about 95.5% of energy consumption was seen during actual FDM part building. This means that the FDM process parameters such as infill percentage, layer thickness, and printing speed can be optimized to significantly improve the energy consumption of the FDM process. Furthermore, plastic injection molding consumed about 38.2% less energy and produced less carbon emissions per one kilogram of PLA formed parts compared to the FDM process. The developed functional unit measurement models can be employed in setting sustainable manufacturing goals for PLA production.

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