Multi-Scale Simulation of Injection Molding Process with Micro-Features Replication: Relevance of Rheological Behaviour and Crystallization

Effects of Cavity Thickness and Mold Surface Roughness on the Polymer Flow during Micro Injection Molding

In micro injection molding, the cavity thickness and surface roughness are the main effects factors of polymer flow in the die designing and affect the quality of molded products significantly. In this study, the effects of cavity thickness and roughness of cavity surface were investigated mainly on polymer flow during molding and on the roughness of molded products. The parts were molded in the cavities with the thickness from 0.05 mm to 0.25 mm and surface roughness from R_a = 46.55 nm to R_a = 462.57 nm, respectively. The filling integrities and roughness replication ratio of molded parts were used to evaluate the statements of polymer flow and microstructure replication during micro injection molding, respectively. The results showed that the filling integrity changing trends in the thinner cavities were obviously different or even opposite to those in the thicker cavities with the changing of cavity surface roughness instead of single trend in the conventional studies. For each cavity surface roughness, the filling integrity showed an upward trend with the increasing cavity thickness. In different cavity thickness, the maximum gap of filling integrity was 23.76 mm, reaching 544.94% from 0.05 mm to 0.25 mm. Additionally, the surface roughness ratio was slightly smaller than one before, reaching the polymer surface roughness limit around R_a = 71.27 nm, which was decided by the nature of the polymer itself. This study proposed the references for the design and fabrication of mold cavities and parts, and saved time and cost in the actual product manufacturing.

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

Polymer crystallization under external flow

The general aspects of polymer crystallization under external flow, i.e., flow-induced crystallization (FIC) from fundamental theoretical background to multi-scale characterization and modeling results are presented. FIC is crucial for modern polymer processing, such as blowing, casting, and injection modeling, as two-third of daily-used polymers is crystalline, and nearly all of them need to be processed before final applications. For academics, the FIC is intrinsically far from equilibrium, where the polymer crystallization behavior is different from that in quiescent conditions. The continuous investigation of crystallization contributes to a better understanding on the general non-equilibrium ordering in condensed physics. In the current review, the general theories related to polymer nucleation under flow (FIN) were summarized first as a preliminary knowledge. Various theories and models, i.e., coil-stretch transition and entropy reduction model, are briefly presented together with the modified versions. Subsequently, the multi-step ordering process of FIC is discussed in detail, including chain extension, conformational ordering, density fluctuation, and final perfection of the polymer crystalline. These achievements for a thorough understanding of the fundamental basis of FIC benefit from the development of various hyphenated rheometer, i.e., rheo-optical spectroscopy, rheo-IR, and rheo-x-ray scattering. The selected experimental results are introduced to present efforts on elucidating the multi-step and hierarchical structure transition during FIC. Then, the multi-scale modeling methods are summarized, including micro/meso scale simulation and macroscopic continuum modeling. At last, we briefly describe our personal opinions related to the future directions of this field, aiming to ultimately establish the unified theory of FIC and promote building of the more applicable models in the polymer processing.