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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.

The aim of this study is to systematically review scientific research on sustainability in the event industry to understand the current state of affairs and provide a roadmap for future studies. By combining the topics of the event industry and sustainability, it aims to thematically analyze the scientific literature in this area.

This study analyzed 1,710 studies in the Web of Science (WoS) database as of June 1, 2024. The keywords “event industry” and “sustainability” were used to search for academic articles, review articles, and conference proceedings. The data were analyzed geographically, thematically, and temporally. Cross-national research contributions, document types, researcher profiles, and interdisciplinary relationships were examined in detail.

The analysis shows that sustainability is an increasingly important issue in the events industry, with countries such as the USA, China and Australia leading the way. Most research is in the form of articles, with conference proceedings and reviews playing an important role. There is a particular concentration in categories such as “management,” “hospitality, leisure, sport and tourism” and “sustainability science.” Even during the pandemic, there has been a significant increase in research activity, underscoring the importance of this topic on a global scale. Researchers have made significant scientific contributions in this area. These interdisciplinary studies have revealed important intersections between environmental sustainability and event management.

This study provides a comprehensive overview of the current state of research on the event industry and sustainability, illustrating the interdisciplinary nature of the field and the geographical distribution of research activity. A systematic review of the literature consolidates the body of knowledge in this field and provides directions for future research. This study highlights the need for more in-depth and comprehensive research on sustainability, and provides an important framework for strengthening the links between event management and sustainability.

Rotating functionally graded (FG) disks of variable thickness generates vibration. This study aims to analyze the vibration generated by the rotating disks using a finite element program and compare the results obtained with the regression methods.

Transverse vibration values of rotating FG disks with variable thickness were modeled using different regression methods. The accuracies of the obtained models are compared. In the context of comparing regression methods, multiple linear regression (MLR), extreme learning machine (ELM), artificial neural networks (ANNs) and radial basis function (RBF) were used in this study. The error graph between the observed value and the predicted value of each regression method was obtained. The error values of the regression methods used with scientific error measures were calculated.

The analysis of the transverse vibration of rotating FG disks with the finite element program is consistent with the studies in the literature. When the variables and vibration value determined on the disk are modeled with ELM, MLR, ANN and RBF regression methods, it is concluded that the most accurate model order is RBF, ANN, MLR and ELM.

There are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling the vibration value of rotating discs.