Enhancement of productivity in the automated anodizing industry using machine learning and evolutionary algorithms

Abstract

Aluminium is one of the most important materials in human newlineinventions due to its vast applications. Aluminium is silvery in appearance and newlinehas impressive mechanical properties such as strength and durability. newlineAluminium has a high strength-to-weight ratio compared to other earth newlinematerials such as Stainless steel and cast iron due to its low density. Due to newlinethis fact, Aluminium and aluminium alloys are used in a wide range of newlineaerospace, space, and outer space applications. However, one of the very newlinedemerits of Aluminium is that it will undergo corrosion and pitting when it is newlineexposed to acidic environments easily. Aluminium is reactive to water as well newlineas the moisture content present in the air. Due to this fact, raw aluminium is newlinenot recommended for engineering applications that require a high degree of newlineprecision and durability. This causes the Raw aluminium to undergo special newlinecoating processes, such as Anodizing, so that the Aluminium becomes non newlinereactive to the environment. Since aluminium requires Anodizing before newlineapplication, the demand for Quality anodizing processes has also exponentially newlineincreased due to the demand for Anodized aluminium components. This led to newlinethe building of many anodizing industries in different regions around the newlineworld. However, many studies show the still-required quality of anodized newlinecomponents has not been obtained since these industries are not modernized newlineand are still working with outdated mechanisms and processes. To address newlinethese issues, a number of experiments have been planned, including newlineautomation, optimizing the anodizing bath, optimizing all baths, and proposing newlineinternet of things systems in the anodizing environment. newline