Preclinical wear testing of joint implants has primarily focussed on the wear properties of Ultra-High Molecular Weight Polyethylene (UHMWPE) articulating on wrought/cast metals. Advancements in additive manufacturing (AM) technologies, such as laser-based powder bed fusion (LPBF), have led to the increasing use of this manufacturing method in metal articulating joint components. There is, however, still uncertainty regarding the wear properties of UHMWPE against AM metals. This study employed LPBF Co-Cr-Mo and Ti-6Al-4V pins in articulation against UHMWPE to assess the wear of the latter. A multidirectional pin-on-plate wear testing machine was used to simulate in vivo knee joint conditions over 5 × 106 cycles. Wear testing was conductedwith ASTM F732 as guideline. The LPBF Ti-6Al-4V pins underwent a thermal oxidation heat treatment to improve the material's wear properties. The state of the thermal oxide layer was investigated after wear testing by sectioning the pins and measuring the thickness of the oxide layer. Wear testing showed that UHMWPE against Co-Cr-Mo had better wear properties compared to UHMWPE on Ti-6Al-4V. The wear properties of UHMWPE against Co-Cr-Mo and UHMWPE on Ti-6Al-4V were within ASTM F732 requirements and comparable to those reported in the literature. The thermal oxide layer on the LPBF Ti-6Al-4V pins showed signs of delamination after 5 × 106 cycles. A small oxygen diffusion zone of 1–2 μm was argued to be the reason for the delamination.
Functionally graded materials (FGMs) have found extensive applications in the biomedical industry, due to their excellent mechanical and tribological properties. However, new FGM fabrication techniques are yet to be examined for possible enhancements in their properties. This study investigates the tribological properties of Titanium/Hydroxyapatite (Ti-HA) and Titanium/Silicon oxide (Ti-SiO2) FGM samples fabricated by three die compaction techniques for the application of dental implants. The constituting powder particles were functionally dispersed and mixed using a newly-designed mixer and consolidated by hot dynamic and quasi-static compaction techniques at three different strain rates. Microstructure, hardness, wear resistance, wear penetration depth, and friction coefficient of the FGM samples were then studied in this work. Optical microscopy images exhibited smooth dispersion of powders conforming to a linear grading function. Vickers hardness values of the FGM samples was found to be directly proportional to the strain rate and inversely proportional to the content of the reinforcing phase (HA or SiO2). Higher strain rate also resulted in higher wear resistance and lower wear penetration depth in all FGMs, with the highest wear resistance being observed in the samples prepared using the Split Hopkinson Bar. Similarly, the coefficient of friction was also shown to be the lowest in Ti-HA samples prepared using Split Hopkinson Bar. Further, microscopic observations revealed that adhesion, delamination and abrasion as the dominant wear mechanisms in all FGM samples. It was concluded that the Ti-HA sample produced by the SHB method enjoyed superior tribological properties, being comparable to that of natural human teeth.
A number of analytical procedures (Spectrum Descriptive Analysis (SDA), Quantitative Descriptive Analysis (QDA), and Check All That Apply (CATA)) are used for characterizing sensorial attributes of topical formulations. However, these techniques are evaluated by expert panels/consumers, which are subjective, expensive and time consuming. Despite widespread use of these methods, the techniques do not necessarily aid in the development of innovative formulations and understanding of consumer liking. In addition, the hedonic attributes of a product can significantly dominate the sensation. Therefore, a more rapid, quantitative, and objective approach is required for understanding the formulation factors that governs sensory perception at different points of consumer application. Rheological evaluations of topical formulations were carried out under steady and oscillatory (SAOS and LAOS) rheology using a commercial rheometer. Friction measurements were performed using an in-house built tribometer on non-biological skin model to investigate how surface properties are influenced by application of different topical formulations. Further, a broad range of instrumental texture measurements was performed to characterize the formulations. Principal component analysis was used for dimensionality reduction of the instrumental data. Supervised machine learning was performed on the closely related PCA parameters to develop predictive models. We identified physical parameters relevant to different perceptual attributes by comparing a range of commercial topical formulations with various compositions using rheological and tribological methods. Multi-variate machine learning models were developed where several key instrumental textural attributes and skin feel could be predicted from data obtained from linear and non-linear rheological measurements. Our study shows rheological analysis based on a few material parameters can be effectively used for predicting instrumental sensory attributes that is of relevance to consumer care industry. The machine learning models may benefit development of innovative formulations, valorisation of new materials and serve as a platform for mapping commercial formulations for product optimization. The models can likely shorten design cycle time by screening through large volume of formulations and short list only those with highest potential to be evaluated by a very specialized and expensive human test panel.
