Advanced Measurement, Prediction, and Testing Techniques in Polymer Manufacturing, Processing, and End-Use

The viscoelastic nature of polymeric formulations utilised in drug products imparts unique thermomechanical attributes during manufacturing and over the shelf life of the product. Nevertheless, it adds to the challenge of understanding the precise mechanistic behaviour of the product at the microscopic and macroscopic level during each step of the process. Current thermomechanical and rheological characterisation techniques are limited to assessing polymer performance to a single phase and are especially hindered when the polymers are undergoing thermomechanical transitions. Since pharmaceutical processing can occur at these transition conditions, this study successfully proposes a thermomechanical characterisation approach combining both mechanical and rheological data to construct a comprehensive profiling of polymeric materials spanning both glassy and rubbery phases. This approach has been used in this study to assess the mechanical and rheological behaviour of heterogenous polymer blends of hydroxypropyl cellulose (HPC) and hydroxypropyl methylcellulose (HPMC) over a shearing rate range of 0.1–100 s⁻¹ and a temperature range of 30–200 °C. The results indicate that HPC and HPMC do not appear to interact when mixing and that their mixture exhibits the mechanistic properties of the two individual polymers in accordance with their ratio in the mixture. The ability to characterise the behaviour of the polymers and their mixtures before, throughout, and after the glassy to rubbery phase transition by application of the combined techniques provides a unique insight towards a quality-by-design approach to this and other polymer-based solid dosage forms, designed with the potential to accelerate their formulation process through obviating the need for multiple formulation trials. Full article

The effective ionic capacities of strong cation ion-exchange resins were investigated and compared using conditions similar to those found in white goods, in order to establish behavioral differences between commercial products and evaluate their capacity in a broader business context. Nine different products of equivalent TDS (Technical Data Sheet) capacity were observed to examine their differences in approximately real-life conditions. For a broader context of applicability analysis, besides the absolute ionic operating capacity, the following additional factors were included in the evaluation: the standard deviation in the resins’ performances and their relative prices. A complete method for material applicability evaluation was hereby proposed and shown to offer cost factor benefits of up to 21.1% within the range of products examined, in comparison to a cost-only evaluation for equivalent materials. Full article

Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams. Full article

Soft sensor technology has become an effective tool to enable real-time estimations of key quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring and a control of rubber manufacturing. However, it remains a challenging issue to develop high-performance soft sensors due to improper feature selection/extraction and insufficiency of labeled data. Thus, a deep semi-supervised just-in-time learning-based Gaussian process regression (DSSJITGPR) is developed for Mooney viscosity estimation. It integrates just-in-time learning, semi-supervised learning, and deep learning into a unified modeling framework. In the offline stage, the latent feature information behind the historical process data is extracted through a stacked autoencoder. Then, an evolutionary pseudo-labeling estimation approach is applied to extend the labeled modeling database, where high-confidence pseudo-labeled data are obtained by solving an explicit pseudo-labeling optimization problem. In the online stage, when the query sample arrives, a semi-supervised JITGPR model is built from the enlarged modeling database to achieve Mooney viscosity estimation. Compared with traditional Mooney-viscosity soft sensor methods, DSSJITGPR shows significant advantages in extracting latent features and handling label scarcity, thus delivering superior prediction performance. The effectiveness and superiority of DSSJITGPR has been verified through the Mooney viscosity prediction results from an industrial rubber-mixing process. Full article

This study presents the effect of temperature on the mechanical and tribological properties of SU-8 polymer. The temperature of investigated samples increasing during testing and the variation of mechanical and tribological properties were monitored. The samples for tests were SU-8 hard baked at different temperatures. The hard bake temperature changes the mechanical and tribological properties of polymers. The aim of this research work is the reliability design improvement of SU-8 microstructures from electro-thermally actuated devices where a thermal gradient produces the softening and modification of SU-8 behavior. As a function of the hard baked temperature, different mechanical and tribological properties were experimentally determined using the atomic force microscopy (AFM) technique. The mechanical properties of interest are the modulus of elasticity and hardness. The investigated tribological properties involve the adhesion and friction forces. The modulus of elasticity and hardness decrease if the operating temperature increases based on the thermal relaxation of material and their viscoelastic behavior. The adhesion force between AFM tip and investigated samples increases if the operating temperature increases, respectively. The same evolution was experimentally observed in the case of friction force. Moreover, for the same testing temperature, the modulus of elasticity and hardness increase, and the adhesion and friction forces decrease if the SU-8 is hard baked at high temperature. Full article

The free radical polymerization of styrene (FRPS) is a complex process system with uncertain parameters in its mechanistic model. When the reaction conditions are switched, or the reaction process generates faults, the parameters will change. Therefore, state and parameter estimation (SPE) becomes an important part of the process monitoring and process control for free radical polymerization of styrene. The unscented Kalman filter (UKF) is widely used for nonlinear process systems, but it rarely considers the problem of model parameter uncertainty. UKF can be used for SPE, called UKF-based SPE (UKF-SPE), where the parameters are usually estimated simultaneously as an extension of the state space. However, when the parameters change with system switching, the traditional UKF-SPE cannot detect and track the parameter changes in time, and inaccurate parameters generate modeling errors. To deal with the problem, a UKF-based robust SPE method (UKF-RSPE) for the free radical polymerization of styrene with variable parameters is proposed, introducing a parameter testing criterion based on hypothesis testing and moving windows to directly detect whether the parameters have changed. Based on the detection results, a gradient descent method with adaptive learning rate is used to iteratively update the parameters to speed up the tracking of the parameters and to obtain more accurate parameters and states. Finally, the proposed UKF-based robust SPE is applied to free radical polymerization of styrene in a jacketed continuous stirred tank reactor. The experimental results verify the effectiveness and robustness of the method, which can track the parameters faster and obtain more accurate states. Full article

Vulcanized rubbers are commonly used to provide the energy absorption under compressive deformation from other engineering components. However, if a constant compressive deformation is maintained on rubber, the load response is not constant but decreases with time; i.e., the stress relaxation. A decrease in force response with time of rubber can be experimentally evaluated by the stress relaxation test. In the present work, the localized stress of vulcanized rubber during a compressive stress relaxation test (i.e., ASTM D6147) was evaluated. Hyperelastic behavior was assumed during rapid application of strain, while the viscoelastic behavior was assumed during stress relaxation. Hyperelastic and viscoelastic parameters were experimentally evaluated using a standard specimen. Finite element analysis (FEA) models were applied for the predictions of stress relaxations of rubbers with various geometries and applied strains. FEA results were in good agreement with results of the stress relaxation tests. Localized stresses in rubber during rapid application of compressive strain and stress relaxation were successfully evaluated. The findings can give the localized phenomena of vulcanized rubber during a stress relaxation test, which can be used as a guideline for the design, usage, and improvement of rubber and viscoelastic polymeric components. Full article

Several researchers have investigated the phenomenon of polymer–gas mixtures, and a few have proposed diffusion coefficient values instead of a diffusion coefficient model. There is a paucity of studies focused on the continuous change in the diffusion coefficient corresponding to the overall pressure and temperature range of the mixture. In this study, the gas sorption and desorption experiments of poly(methyl methacrylate) (PMMA) were conducted via solid-state batch foaming, and the weight change was measured using the gravimetric method with a magnetic balance. The control parameters were temperature, which ranged from 290 to 370 K, and pressure, which ranged from 2 to 5 MPa in the subcritical regime. Based on the experimental data, the diffusion coefficient of the PMMA was calculated using Fick’s law. After calculating the diffusion coefficient in the range of the experiment, the diffusion coefficient model was employed using the least-squares method. Subsequently, the model was validated by comparing the obtained results with those in the literature, and the overall trend was found to be consistent. Therefore, it was confirmed that there were slight differences between the diffusion coefficient obtained using only Fick’s equation and the value using by a different method. Full article

3D Printed Injection Moulds (3DIM), commonly used for low volume production and prototyping purposes, are known to fail abruptly and have a comparatively shorter life than conventional moulds. Investigating the underlying critical factors affecting failure may help in reducing the risk of abrupt failures and possibly prolong the 3DIM tool life. A hypothesis that the cooling time of the Injection Moulding (IM) process is a critical factor for 3DIM tool failure has been proposed. The failure hypothesis has been validated by theoretical calculations, FEA simulations and experimental investigations. Experiments were performed using two different materials for the 3DIM tool (Visijet M3-X and Digital ABS) and an engineering thermoplastic (Lexan 943-A) as the moulding material. Results showed that cooling time was a critical factor on tool life and managing the thermal loading on a 3DIM tool could lead to increased tool life. The paper identifies cooling time as the critical factor affecting 3DIM tool life and presents a cooling regime that could possibly lead to prolonged tool life. Full article

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method. Full article

In this article, we show an alternative low-cost fabrication method to obtain poly(dimethyl siloxane) (PDMS) microfluidic devices. The proposed method allows the inscription of micron resolution channels on polystyrene (PS) surfaces, used as a mold for the wanted microchip’s production, by applying a high absorption coating film on the PS surface to ablate it with a focused low-power visible laser. The method allows for obtaining micro-resolution channels at powers between 2 and 10 mW and can realize any two-dimensional polymeric devices. The effect of the main processing parameters on the channel’s geometry is presented. Full article

Due to the significant properties of silica, thermostatics can be enhanced using silica-additives to maximize the quality of polymer compounds and transform plastics into tailored properties. The silica additives can enhance the handling and quality performance of composites and thermoplastic polymers due to their diverse potential. Besides, using silica as an additive in different characteristics can allow granulates and powders to flow easily, minimize caking, and control rheology. On the other hand, the eruption of 3D printing technology has led to a massive new waste source of plastics, especially the polylactic acid (PLA) that is associated with the fused deposition modeling (FDM) process. In this paper, the impact on the mechanical properties when silica is mixed with waste PLA from 3D printing was studied. The PLA/silica mixtures were prepared using different blends through twin extruders and a Universal Testing Machine was used for the mechanical characterization. The result indicated that increasing silica composition resulted in the increase of the tensile strength to 121.03 MPa at 10 wt%. Similar trends were also observed for the toughness, ductility, and the yield stress values of the PLA/silica blends at 10 wt%, which corresponds to the increased mechanical property of the composite material reinforced by the silica particles. Improvement in the mechanical properties of the developed composite material promotes the effective recycling of PLA from applications such as 3D printing and the potential of reusing it in the same application. Full article

Being an important part of aerial insulated cable, the semiconductive shielding layer is made of a typical polymer material and can improve the cable transmission effects; the structural parameters will affect the cable quality directly. Then, the image processing of the semiconductive layer plays an essential role in the structural parameter measurements. However, the semiconductive layer images are often disturbed by the cutting marks, which affect the measurements seriously. In this paper, a novel method based on the convolutional neural network is proposed for image segmentation. In our proposed strategy, a deep fully convolutional network with a skip connection algorithm is defined as the main framework. The inception structure and residual connection are employed to fuse features extracted from the receptive fields with different sizes. Finally, an improved weighted loss function and refined algorithm are utilized for pixel classification. Experimental results show that our proposed algorithm achieves better performance than the current algorithms. Full article

The objective of this paper is to determine how the supporting structure in the DLP 3D printing process has influences on the characteristics of the flat and cylindrical surfaces. The part is printed by using the Light Control Digital (LCD) 3D printer technology. A Coordinate Measuring Machine (CMM) with contact probes is used for measuring the physical characteristics of the printed part. Two types of experiment were chosen by the authors to be made. The first part takes into consideration the influence of the density of the generated supports, at the bottom of the printed body on the characteristics of the flat surface. In parallel, it is studying the impact of support density on the dimension and quality of the surface. In the second part of the experiment, the influence of the printed supports dimension on the flatness, straightness and roundness of the printed elements were examined. It can be observed that both the numerical and dimensional optimum zones of the support structure for a prismatic element could be determined, according to two experiments carried out and the processing of the resulting data. Based on standardized data of flatness, straightness and roundness, it is possible to put in accord the values determined by measurement within the limits of standardized values. Full article

Infrared thermography (IRT) is a competitive method for nondestructive testing; yet it is susceptible to errors when testing objects with complex geometries. This work investigates the effects of regulating different thermographic testing parameters to optimize the IRT outcomes when testing complex shaped geometries, particularly cylindrical coupons. These parameters include the scanning routine, feed-rate, and heat intensity. Fine-tuning these parameters will be performed with respect to three different variables consisting of workpiece density, defect size, and defect depth. The experimental work is designed around 3D-printed cylindrical coupons, then the obtained thermal images are stitched via image processing tool to expose defects from different scans. The analysis employs a Signal-to-Noise Ratio (SNR) metric in an orthogonal tabulation following a Taguchi Design of Experiment. Moreover, test sensitivity and the best combination of factor levels are determined using Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The outcomes show that the heating intensity factor is the most dominant in exposing flaws with close to 40% mean shift and up to 47% variance fluctuation. The paper introduces the tools employed in the study, and then explains the methodology followed to test one sample quadrant. The results for running the testing on all the scenarios are presented, interpreted, and their implications are recommended. Full article

To expand the use of polylactic acid (PLA) in high-temperature environments, crystallization morphology regulation was studied to enhance the heat resistance of PLA. PLA crystallinity was controlled using heat treatment and nucleating agent (zinc phenylphosphonate, brand TMC). The heat deflection temperatures of PLAs with same crystallinities considerably varied using different treatments. The crystallization morphology of PLA (4032D) and PLA/TMC composites was studied using X-ray diffraction (XRD) and polarized optical microscopy. XRD test results show that TMC can improve the crystallization rate and heat treatment can enhance the crystallinity and thickness of PLA, suggesting that the crystallization morphology improved after heat treatment. Nucleating agents can increase the crystallinity of PLA but cannot improve its crystallization morphology. The findings indicate that at the same crystallinity, PLAs exhibit improved crystallization morphology and high heat resistance; these results can provide guidance for improving the heat resistance of PLAs and facilitate the design of new nucleating agents. Full article

Shrinkage and warping of additive manufacturing (AM) parts are two critical issues that adversely influence the dimensional accuracy especially in powder bed fusion processes such as selective laser sintering (SLS). Powder fusion, material solidification, and recrystallization are the key stages causing volumetric changes of polymeric materials during the abrupt heating–cooling process. In this work, the mechanisms of shrinkage and warping of semi-crystalline polyamide (PA) 12 in SLS are well investigated. Heat-transfer and thermo-mechanical models are established to predict the process-dependent shrinkage and warping. The influence of raw material- and laser-related parameters are considered in the heat-transfer and thermo-mechanical models. Such models are established considering the natural thermal gradient and dynamic recrystallization, which induce internal strain and volumetric change. Moreover, an experimental design via orthogonal approach is introduced to validate the feasibility and accuracy of the proposed models. Finally, the quantitative relationships of process parameters with product shrinkage and warping are established; the dimensional accuracy in part-scale can be well predicted and validated with printed parts in a real experiment. Full article