Performance Analysis of Retrofitted Tribo-Corrosion Test Rig for Monitoring In Situ Oil Conditions

Analyses of used engine oils via atomic spectroscopy - Influence of sample pre-treatment and machine learning for engine type classification and lifetime assessment

The analysis of used engine oils from industrial engines enables the study of engine wear and oil degradation in order to evaluate the necessity of oil changes. As the matrix composition of an engine oil strongly depends on its intended application, meaningful diagnostic oil analyses bear considerable challenges. Owing to the broad spectrum of available oil matrices, we have evaluated the applicability of using an internal standard and/or preceding sample digestion for elemental analysis of used engine oils via inductively coupled plasma optical emission spectroscopy (ICP OES). Elements originating from both wear particles and additives as well as particle size influence could be clearly recognized by their distinct digestion behaviour. While a precise determination of most wear elements can be achieved in oily matrix, the measurement of additives is performed preferably after sample digestion. Considering a dataset of physicochemical parameters and elemental composition for several hundred used engine oils, we have further investigated the feasibility of predicting the identity and overall condition of an unknown combustion engine using the machine learning system XGBoost. A maximum accuracy of 89.6% in predicting the engine type was achieved, a mean error of less than 10% of the observed timeframe in predicting the oil running time and even less than 4% for the total engine running time, based purely on common oil check data. Furthermore, obstacles and possibilities to improve the performance of the machine learning models were analysed and the factors that enabled the prediction were explored with SHapley Additive exPlanation (SHAP). Our results demonstrate that both the identification of an unknown engine as well as a lifetime assessment can be performed for a first estimation of the actual sample without requiring meticulous documentation.

Tribocorrosion Behavior of Inconel 718 Fabricated by Laser Powder Bed Fusion-Based Additive Manufacturing

Additive manufacturing (AM) by laser powder bed fusion (LPBF) has gained significant research attention to fabricate complex 3D Inconel alloy components for jet engines. The strategic advantages of LPBF-based AM to fabricate jet components for aerospace applications are well reported. The jet components are exposed to a high degree of vibration during the jet operation in a variable aqueous environment. The combined vibration and the aqueous environment create a tribological condition that can accelerate the failure mechanism. Therefore, it is critical to understand the tribocorrosion behavior of the Inconel alloy. In the present work, tribocorrosion behavior of the LPBF fabricated standalone coating of Inconel 718 in the 3.5% NaCl aqueous solution is presented. The LPBF fabricated samples are analyzed to determine the impact of porosity, generated as a result of LPBF, on the triobocorrosion behavior of AM Inconel 718. The study includes potentiodynamic tests, cathodic polarization, along with OCP measurements. The corrosive environment is found to increase the wear by 29.24% and 49.5% without the initiation of corrosion in the case of AM and wrought Inconel 718, respectively. A corrosion accelerated wear form of tribocorrosion is observed for Inconel 718. Additionally, the corrosive environment has a significant effect on wear even when the Inconel 718 surface is in equilibrium potential with the corrosive environment and no corrosion potential scan is applied. This study provides an insight into a critical aspect of the AM Inconel components.

Development of surface properties of ultra-high-molecular-weight polyethylene film using side-chain crystalline block copolymers

Ultra-high-molecular-weight polyethylene (UHMWPE) has been widely used in industry; however, the applications for UHMWPE are limited because of low hydrophilic and adhesive properties. Herein, we developed the surface properties of UHMWPE by using side-chain crystalline block copolymers (SCCBCs), which consist of a side-chain crystalline unit and a functional unit. This process only required immersing the UHMWPE film in the diluted SCCBC solution, which enabled the UHMWPE surface to be coated homogeneously. The results of the contact angle and tensile shear test showed that the surface of UHMWPE modified with SCCBC was improved in hydrophilicity and adhesive properties. In addition, high adhesion strength was measured on UHMWPE surfaces dipped in a SCCBC solution at high temperature with the UHMWPE film becoming elongated at all parts other than the adhesion contact area.

Surface Energy and Tribology of Electrodeposited Ni and Ni–Graphene Coatings on Steel

Composite electrochemical coatings (CECs) are some of the most widely investigated coatings due to its versatility in tailoring physio-mechanical and tribological properties. The effectiveness of the CECs for tribological applications is dependent on the solid–liquid interfaces. The active and passive nature of the contact boundaries for a CEC with a solid/liquid interface is defined by the surface energy of these boundaries. Unless the effect of surface energy on the tribological properties of the CEC are understood, it is not possible to get a holistic picture on properties, such as corrosion and tribocorrosion. The present study investigates the surface energy of optimized nickel (Ni) and Ni–graphene (Ni–Gr) coatings and their effect on the dynamic friction and wear behavior. It was found that the addition of Gr to the Ni coating in small quantities could decrease the polar component of surface energy significantly than the dispersive component. The presence of Gr in the coating was able to reduce the wear while providing low friction. The Ni–Gr coating exhibited low surface energy that includes weak adhesive forces, which can prevent embedding of the wear particles during sliding.

Analyzing and Modelling the Corrosion Behavior of Ni/Al2O3, Ni/SiC, Ni/ZrO2 and Ni/Graphene Nanocomposite Coatings

A study has been presented on the effects of intrinsic mechanical parameters, such as surface stress, surface elastic modulus, surface porosity, permeability and grain size on the corrosion failure of nanocomposite coatings. A set of mechano-electrochemical equations was developed by combining the popular Butler–Volmer and Duhem expressions to analyze the direct influence of mechanical parameters on the electrochemical reactions in nanocomposite coatings. Nanocomposite coatings of Ni with Al₂O₃, SiC, ZrO₂ and Graphene nanoparticles were studied as examples. The predictions showed that the corrosion rate of the nanocoatings increased with increasing grain size due to increase in surface stress, surface porosity and permeability of nanocoatings. A detailed experimental study was performed in which the nanocomposite coatings were subjected to an accelerated corrosion testing. The experimental results helped to develop and validate the equations by qualitative comparison between the experimental and predicted results showing good agreement between the two.