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This study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.

Samples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.

Among the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.

The effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.

It was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.

It is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.

This work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.

Erosion and abrasion are the prominent wear mechanisms reducing the lifetime of machine components. Both wear mechanisms are playing a role meanwhile, generating a synergy, leading to a material removal on the target. The purpose of study is to create a mathematical expression for erosive abrasive wear.

Many factors such as environmental cases and material character have an influence in erosive abrasive wear. In the work, changes in abrasive size and material hardness have been analyzed. As an abrasive particle, quartz sand has been used. All tests have been done in 20 wt.% slurry. Heat treatment has been applied to different steel specimens (steel grades C15, St 37 and Ck45) to change hardness value, which ranged from 185 to 880 Vickers hardness number.

After the four-hour test, it is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. Worn surfaces of the targets have been examined to figure out the wear mechanisms at different conditions under scanning electron microscopy. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease. The diameters of micro-craters have been about 3–8 µm in hard materials and about 120–140 µm in soft materials.

It is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease.

The study enables to indicate the dominant factor in worn steel used in mechanical components.

After analyzing the test results, a novel mathematical expression, considering both abrasive size and material hardness, has been developed.