Cosimi Corleto

Conventional roughness parameters capture only a limited portion of a surface’s multiscale structure, which restricts their ability to describe how machined surfaces evolve under different conditions. This study introduces a continuous wavelet-based descriptor, obtained through reconstruction driven multiscale decomposition to characterize surface features from fine roughness to coarse waviness. High resolution chromatic confocal scans of milled stainless steel surfaces were collected under controlled variations in tool condition and two feed rates, providing a dataset with diverse multiscale content for validating the method. The SMa(a) curves reveal clear scale dependent changes in surface structure that are not detected by single valued roughness metrics. To address the computational cost of wavelet reconstruction, a deep learning surrogate model is proposed to estimate the SMa spectrum directly from surface patches. The model captures the global trend of the multiscale response with good fidelity, offering a practical and efficient alternative to the proposed wavelet analysis. The study offers both a multiscale analysis framework and a step-by-step methodological basis, from the fundamentals to culminating in an efficient CWT implementation for surface characterization. Overall, the proposed framework enables scalable and interpretable multiscale surface characterization suitable for tribological studies, where a multiscale characterization of surface topography is critical to understanding contact, lubrication, and wear behavior.

In the era of Industry 4.0 and the digital transformation of the manufacturing sector, this article explores the significant potential of machine learning (ML) and deep learning (DL) techniques in evaluating surface roughness—a critical metric of product quality. The integration of edge computing with current computational resources and intelligent sensors has revolutionized the application of AI-driven algorithms in smart manufacturing. It provides real-time data analysis and decision-making capabilities that were unattainable only a decade ago. The research effort intends to improve data-driven decision-making for product quality evaluation by leveraging data integration from manufacturing operations and surface quality measurements. Although a substantial amount of research has been conducted in the related fields, it is still difficult to comprehend and compile all the data on surface roughness research predictive assessment in the form of a process pipeline. This thorough systematic analysis examines scholarly articles published between 2014 and 2024 focusing on surface roughness assessment in precision manufacturing settings. The article is thoroughly classified based on the manufacturing processes, datasets, and ML models used, giving light on the present status, prominent approaches, and existing issues in this sector. A table summarizing the relevant works in this domain providing an easy access to the current trends have been provided. The article not only compiles essential findings and identifies research gaps and similarities in existing methodologies, but it also discusses future research directions and open issues in AI-aided surface roughness evaluation. The critical analysis of the literature reveals a scientific gaps which includes consistent development of benchmarked datasets and making the AI models more explainable using the state-of-the-art explainable AI (XAI) algorithms. The ultimate objective of the article is not only to provide a guide for the practitioners in either of the three domains of AI, manufacturing or surface metrology but also to pave the path for more robust, efficient, and accurate surface quality evaluation processes in production.