Optimization simulated injection molding process for ultrahigh molecular weight polyethylene nanocomposite hip liner using response surface methodology and simulation of mechanical behavior

Replication Study of Molded Micro-Textured Samples Made of Ultra-High Molecular Weight Polyethylene for Medical Applications

In articular joint implants, polymeric inserts are usually exploited for on-contact sliding surfaces to guarantee low friction and wear, a high load-bearing capacity, impact strength and stiffness, and biocompatibility. Surface micro-structuring can drastically reduce friction and wear by promoting hydrostatic friction due to synovial fluid. Ultra-High Molecular Weight Polyethylene (UHMWPE) is a suitable material for these applications due to its strong chemical resistance, excellent resistance to stress, cracking, abrasion, and wear, and self-lubricating property. However, surface micro-texturing of UHMWPE is hardly achievable with the currently available processes. The present study investigates UHMWPE's micro-textured surface replication capability via injection molding, comparing the results with the more easily processable High-Density Polyethylene (HDPE). Four different micro-texture cavities were designed and fabricated on a steel mold by micro-EDM milling, and used for the experimental campaign. Complete samples were fabricated with both materials. Then, the mold and samples were geometrically characterized, considering the dimensions of the features and the texture layout. The replication analysis showed that HDPE samples present geometrical errors that span from 1% to 9% resulting in an average error of 4.3%. In comparison, the UHMWPE samples display a higher variability, although still acceptable, with percentage errors ranging from 2% to 31% and an average error of 11.4%.

Critical Review on the Developments in Polymer Composite Materials for Biomedical Implants

There has been a lack of research for developing functional polymer composites for biomedical implants. Even though metals are widely used as implant materials, there is a need for developing polymer composites as implant materials because of the stress shielding effect that causes a lack of compatibility of metals with the human body. This review aims to bring out the latest developments in polymer composite materials for body implants and to emphasize the significance of polymer composites as a viable alternative to conventional materials used in the biomedical industry for ease of life. This review article explores the developments in functional polymer composites for biomedical applications and provides distinct divisions for their applications based on the part of the body where they are implanted. Each application has been covered in some detail. The various applications covered are bone transplants and bone regeneration, cardiovascular implants (stents), dental implants and restorative materials, neurological and spinal implants, and tendon and ligament replacement.

Multi-objective optimization of injection molding process parameters based on BO-RFR and NSGAⅡ methods

Injection molding of thin-walled plastic parts with minimum deformation in warpage and volume shrinkage is crucial for part quality. Simulation combined Latin hypercube sampling approach was used to research the effects of different process parameters on deformation. Then, random forest regression (RFR) is used to construct the mathematical relationship between process parameters and defects, such as warpage and volume shrinkage. The gaussian process is used as probabilistic surrogate model, while the probability of improvement is used as acquisition function to construct a Bayesian optimization for RFR’s hyperparameters, and the performance of random search is compared. In addition, the gradient boosting regression (GBR) and support vector regression (SVR) were also adopted to establish the prediction models, respectively. Comparing all the above prediction models, it can be found that the Bayesian optimized random forest regression (BO-RFR) has the highest accuracy. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is interfaced with the predictive models to find the optimum design parameters for the purpose of effectively predicting and controlling warpage and volume shrinkage. The results show that warpage is reduced by 66.03% while volume shrinkage is 46.20% after optimizing. The final finite element simulation and physical tests indicate that this proposed method can effectively achieve the multi-objective optimization of injection molding.

Non-Isothermal Crystallisation Kinetics of Polypropylene at High Cooling Rates and Comparison to the Continuous Two-Domain pvT Model

The modelling of the correlation between pressure, specific volume and temperature (pvT) of polymers is highly important for applications in the polymer processing of semi-crystalline thermoplastics, especially in injection moulding. In injection moulding, the polymer experiences a wide range of cooling rates, for example, 60 °C/min near the centre of the part and up to 3000 °C/min near the mould walls. The cooling rate has a high influence on the pvT behaviour, as was shown in the continuous two-domain pvT model (CTD). This work examined the Hoffman–Lauritzen nucleation and growth theory used in the modified Hammami model for extremely high cooling rates (up to 300,000 °C/min) by means of Flash differential scanning calorimeter (DSC) measurements. The results were compared to those of the empirical continuous two-domain pvT model. It is shown that the Hammami model is not suitable to predict the crystallisation kinetics of polypropylene at cooling rates above 600 °C/min, but that the continuous two-domain pvT model is well able to predict crystallisation temperatures at high cooling rates.

Continuous Two-Domain Equations of State for the Description of the Pressure-Specific Volume-Temperature Behavior of Polymers

The two-domain Schmidt equation of state (EoS), which describes the pressure-specific volume–temperature (pvT) behavior of polymers in both the equilibrium molten/liquid state and non-equilibrium solid/glassy state, is often used in the simulation of polymer processing. However, this empirical model has a discontinuity problem and low fitting accuracy. This work derived a continuous two-domain pvT model with higher fitting accuracy compared with the Schmidt model. The cooling rate as an obvious influencing factor on the pvT behavior of polymers was also considered in the model. The interaction parameters of the equations were fitted with the experimental pvT data of an amorphous polymer, acrylonitrile-butadiene-styrene (ABS), and a semi-crystalline polymer, polypropylene (PP). The fitted results by the continuous two-domain EoS were in good agreement with the experimental data. The average absolute percentage deviations were 0.1% and 0.16% for the amorphous and semi-crystalline polymers, respectively. As a result, the present work provided a simple and useful model for the prediction of the specific volume of polymers as a function of temperature, pressure, and cooling rate.

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

Optimization of process parameters in plastic injection molding for minimizing the volumetric shrinkage and warpage using radial basis function (RBF) coupled with the k-fold cross validation technique

In this study, a multi-objective design optimization method based on a radial basis function (RBF) model was applied to minimize the volumetric shrinkage and warpage of hip liners as an injection-molded biomedical part. The hip liners included an ultrahigh molecular weight polyethylene (UHMWPE) liner and UHMWPE reinforced with a nano-hydroxyapatite (nHA) liner. The shrinkage and warpage values of the hip liners were generated by simulation of the injection molding process using Autodesk Moldflow. The RBF model was used to build an approximate function relationship between the objectives and the process parameters. The process parameters, including mold temperature, melt temperature, injection time, packing time, packing pressure, coolant temperature, and type of liner, were surveyed to find the interaction effects of them on the shrinkage and warpage of the liners. The results indicated that the addition of nHA helps the liners to obtain more dimensional stability. The model was validated by the k-fold cross validation technique. Finally, the model revealed the optimal process conditions to achieve the minimized shrinkage and warpage simultaneously for various weights.