Surface treatment of the adherends prior to adhesive bonding plays an important role in the enhancing of strength and durability of bonded joints. In this work, an investigation on effect of adherend surface roughness on adhesive bond strength was performed. Single strap joints with different adherends (mild steel and aluminium) bonded with an epoxy resin (Araldite(r) 2015) were tested. The adherend surface was treated by mechanical abrasion process using an emery paper. Contact angle measurement and SEM analysis to understand the wettability and the failure mechanism of the joints were performed. It was found that an optimum surface roughness exists for a maximum bonding strength and the roughness range depends on the adherend material. The joint strength changes are associated not only simply by the increased bonding area, surface texture or mechanical interlocking, but also by the chemical characteristics of the surface and the chemical bond between them.

Metal matrix composites have evoked a keen interest from the automobile and aerospace sectors owing to their attractive mechanical properties and applications. Over the past two decades, researchers have unearthed many secrets pertaining to these advanced materials. This paper briefly reviews the research revelations of the mechanisms that make these materials so superior. Turning of metal matrix composites is focused in particular. Mechanisms such as particle fracture, particle pullout, debonding, dislocation phenomena, thermal softening, wear modes, surface generation, cutting forces, chip formation, strains and stresses are addressed. Discussions on related phenomena such as effects of tool coatings, adhesion, friction, microstructures and strain hardening are also presented

The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle parameters, and formula Bharat track features and guidelines. All developed soft sensors were tested for mean squared error (MSE) and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation, and testing datasets; training state indicators such as gradient, mu, and validation fails; validation performance over successive epochs; and predicted versus derived plots over one lap time. Moreover, the prediction accuracy of the proposed soft sensors was compared against linear or nonlinear regression models and parametric structure models used for system identification such as autoregressive with exogenous variables (ARX), autoregressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ). The testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The proposed soft sensors attained higher prediction accuracy than that of the modelling structures mentioned above. FSEV results also indicated that the SOC and SOE dropped from 97% to 93.5% and 93.8%, respectively, during the running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be applied for the effective monitoring and prediction of SOC, SOE, and PL during the operation of EVs.

American scientific publishing group

Machine learning has revolutionized the way complex problems are solved in engineering. In the current work, machine learning methodology has been applied for predictive modeling of surface roughness generation during machining of Al-Mg based metal matrix composites (MMCs) reinforced with micro boron carbide and multiwalled carbon nanotube particles. Machine learning was used for parameter estimation of modeling structures such as auto regressive with exogenous variables (ARX), auto regressive moving average with exogenous variables (ARMAX), Box Jenkins (BJ) and Output Error (OE). The identified models were validated on the basis of FIT, final prediction error (FPE) and mean squared error (MSE). The PID, fractional order PID (FOPID), complex order PID (COPID) and model predictive controllers (MPC) were employed to effectively control machined surface roughness based on the best performing predictive models. Primary results indicate that: (1) CNT MMCs generate surface roughness comparable to that due to the micro MMCs with tenfold higher reinforcement fractions (2) ARX441 and ARMAX3331 are the best performing predictive models for the nano and micro MMCs respectively (3) PID and MPC are the best controllers for micro and nano MMC systems respectively considering the peak overshoots as the foremost performance metric (safety), followed by settling time (productivity).