Applied Sciences

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Dear Colleagues,

This Special Issue is compiled in cooperation with the 5th International Electronic Conference on Applied Sciences, organized by Applied Sciences from 4–6 Dec 2024 on the MDPI Sciforum platform (https://sciforum.net/event/ASEC2024). We welcome submissions from conference participants of the conference, with the aim being to publish selected extended versions of the presented papers in this Special Issue of Applied Sciences.

The following areas were covered:

Applied Biosciences and Bioengineering
Nanosciences, Chemistry and Materials Science
Computing and Artificial Intelligence
Electrical, Electronics and Communications Engineering
Mechanical and Aerospace Engineering
Energy, Environmental and Earth Science
Food Science and Technology
Applied Physical Science

Prof. Dr. Nunzio Cennamo
Dr. Stefano Toldo
Guest Editors

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Keywords

applied biosciences and bioengineering
nanosciences, chemistry and materials science
computing and artificial intelligence
electrical, electronics and communications engineering
mechanical and aerospace engineering
energy, environmental and earth science
food science and technology
applied physical science

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18 pages, 5069 KiB

Open AccessArticle

A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings

by Cezarina Chivu, Mitica Afteni, Gabriel Radu Frumusanu and Florin Susac

Appl. Sci. 2025, 15(9), 4846; https://doi.org/10.3390/app15094846 - 27 Apr 2025

Viewed by 164

Abstract

The optimal management of manufacturing processes can be achieved through a set of optimal decisions, which must be made to choose the best method to follow every time the process planner reaches a point when several potential manufacturing paths branch off. A dedicated method, namely the Holistic Optimization Method (HOM), has already been developed for this purpose and has been validated in several studies based on artificial- and real-instance databases. The HOM consists of two algorithms: (i) the causal identification of a manufacturing process and (ii) a comparative assessment with similar already-assessed manufacturing cases recorded in an instance database. The two algorithms can be used to estimate the values of the different performance indicators of manufacturing processes. Their application for processing cost estimation in the case of the manufacturing processes of bearing components has already shown good results. In this paper, the HOM is presented as a solution for predicting the timespan needed for grinding roller bearing rings. The specific algorithms of the HOM were applied, grounded in the use of a database with data collected from the industrial environment. The cause variables selected to describe the grinding process of the roller bearing rings were the inner and outer diameter of the ring, its width and weight, the machined surface roughness, the grinding stone rotation speed, the cutting speed, the feed rate, and the cutting depth, while the effect variable to be used by the process planner as the decision criterion was the timespan. Full article

(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Applied Sciences)

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