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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.

This research aims to reveal the role of sustainability in tourism in terms of smart technologies by addressing the relationship between smart tourism and sustainability through a thematic literature analysis.

The research is based on examining the role of smart tourism in terms of sustainability within the studies in the literature.

Tourism is a mass-movement industry that uses natural and artificial resources intensively. For this reason, intensive studies are carried out on this industry in sustainable studies. Based on smart cities, smart destinations have started to be created to focus on specific tourism activities, balance the use of resources in this field, ensure the welfare of tourists and local people, and, most importantly, create sustainable activities. These technology-based cities can save many resources that people need to consider. In addition, thanks to its integration with technological devices, it enables people to be much more effective in social and business life and to act more practically in terms of time, distance, and communication. Besides the stakeholders within the destination, tourists visiting the destination can have a more comfortable and resource-efficient holiday experience thanks to the technological applications and systems created here.

The research presents a thematic literature review on the importance of sustainability in the sector within the framework of smart tourism practices.