Application of box‐behnken design and neural computation for tribo‐mechanical performance analysis of iron‐mud‐filled glass‐fiber/epoxy composite and parametric optimization using PSO

Abstract

This study investigates the possible utilization of iron mine waste for developing a new class of hybrid polymer composites. The composites were fabricated using hand‐layup process by reinforcing woven glass fibers in the epoxy polymer filled with different weight proportions of iron‐mud. Abrasion wear experiments were conducted according to Box‐Behnken design approach under controlled laboratory conditions using a dry abrasion tester. It was found that hardness, tensile modulus, impact energy and abrasion resistance of the fabricated composites improved with filler addition. Also, a prediction tool based on artificial neural network was implemented to investigate the tribological properties of the composites and the results were compared with the experimental ones. A metaheuristic approach like particle swarm optimization was also used to reveal the minimum wear (in volume) at optimal parametric combination. The results showed increase in both wear (in volume) and specific wear rate with respect to increase in the loads as well as sliding velocity. It also exhibited an increase in the wear with decrease in the specific wear rate with respect to the abrading distance. Finally, the morphology of the abraded surfaces was examined by using scanning electron microscopy and the possible abrasion mechanisms were critically examined and presented. POLYM. COMPOS., 40:1433–1449, 2019. © 2018 Society of Plastics Engineers

A Critical Review on Tribological and Mechanical Characteristics of Hybrid Polymer Composite Materials

“…Apart from this, they have implemented particle swarm numerical optimization along with neural soft computation techniques for evaluating wear at extreme environmental situation. [39,40] Rangdale Anandarao et al [41] investigated experimentally influence of molybdenum disulphide on tribological properties of hybrid PTFE polymer composites with Taguchi (L 18 ) statistical design of experiments technique and analyzed friction and wear of hybrid polymer composites. Singh et al [42] investigated the effects of polytetrafluoroethylene micro-particles on mechanical properties of polyoxymethylene composites.…”

“…Taguchi's orthogonal array design gives a detailed explanation of various parameters and their interactions in different engineering processes including friction, coefficient of friction, frictional force, and wear analysis. Pani et al [39] studied experimentally the different types of wear mechanism of hybrid polymer composite using Box-Behnken design of experiment with the response surface methodology and Taguchi technique. [40] They have presented scientific Box-Behnken design of experiment technique is an inexpensive and easy-to-handle, which is helpful in studying the interaction effects, as well as, reduce the number of experimental runs without compromising the individual interaction effect of the input and output.…”

Tribological Performance of Composites Reinforced with the Agricultural, Industrial and Post-Consumer Wastes: A Review

“…The authors observed that the best wear performance was exhibited by the variant containing 40 wt % of slate powder, which was slightly below the level of the composite produced with the use of barite. Iron mud, a major solid waste produced in iron mining and ore processing, was an object of interest in numerous studies performed by Pani et al [ 63 , 64 , 65 , 66 ]. This waste is composed mainly of iron oxide (III) Fe 2 O 3 , aluminum oxide Al 2 O 3 and silicon oxide SiO 2 and became a serious threat to the soil environment due to its long-term storage.…”

Estimation of adhesive wear behavior of the glass fiber reinforced polyester composite materials using ANFIS model

“…In this purpose, a three layered ANNs structure is proposed for prediction of erosion wear characteristics of a material whose inputs and output are; erodent discharge rate, iron mud content, impingement angle, erodent velocity and erosion rate, respectively. 2 ANNs are used to estimate wear on C120 and Rp3 steel surfaces reinforced with short glass fibers, under linear contact. 3 These composite mixtures result in nonlinearity in the machining 3 processes and make difficulties in wear predictions with analytical models under different pressures and speeds.…”

“…These two artificial intelligence methods unveil that wear is directly proportional to load and sliding velocity. 2 Specific wear rate of TiO 2 filled polyester-based composite is found out by ANNs methods. Statistical significance of factors is determined by analysis of variance (ANOVA) from experimental data and sliding velocity is found to be the main factor that affects the wear rate.…”