Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects
Scopus
Toplam 215 atıf DOI
The most important aspect of sustainability in manufacturing is the preservation of energy and natural resources. For modern production, optimized processes that minimize negative impacts on the environment are becoming increasingly important. This can be achieved by increasing energy efficiency through low, clean, and renewable energy consumption. There are many ways to produce less pollution, emissions, and waste in machining: by using more environmentally friendly cooling methods; by applying methods that reduce or eliminate the need for utilization of cooling lubrication; improving the energy efficiency of machining operations; determining the optimal cutting conditions that save resources by increasing machining productivity or reducing the metal removal rate (MRR); minimizing power consumption; and reducing carbon dioxide emissions. This article gives an idea of modern manufacturing with a focus on analyzing the current state of machining operations in terms of saving production resources and ensuring more environmentally friendly production using greener cooling methods of machining such as Dry, Conventional cooling systems, Minimum quantity of lubricant (MQL), Minimum quantity of cooling lubrication (MQCL), Nanofluids, Biodegradable Vegetable Oils, Cryogenic Lubrication, and High-Pressure Cooling (HPC). Finally, the important modern trends of providing resource-saving and environmentally efficient technologies in modern sustainable manufacturing are discussed in this paper.
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Atıf Yapan Yayın
A Comprehensive Analysis of Surface Roughness, Vibration, and Acoustic Emissions Based on Machine Learning during Hard Turning of AISI 4140 Steel
Scopus
Havuzumuzda Open Access 35 atıf almış
Industrial materials are materials used in the manufacture of products such as durable machines and equipment. For this reason, industrial materials have importance in many aspects of human life, including social, environmental, and technological elements, and require further attention during the production process. Optimization and modeling play an important role in achieving better results in machining operations, according to common knowledge. As a widely preferred material in the automotive sector, hardened AISI 4140 is a significant base material for shaft, gear, and bearing parts, thanks to its remarkable features such as hardness and toughness. However, such properties adversely affect the machining performance of this material system, due to vibrations inducing quick tool wear and poor surface quality during cutting operations. The main focus of this study is to determine the effect of parameter levels (three levels of cutting speed, feed, and cutting depth) on vibrations, surface roughness, and acoustic emissions during dry turning operation. A fuzzy inference system-based machine learning approach was utilized to predict the responses. According to the obtained findings, fuzzy logic predicts surface roughness (88%), vibration (86%), and acoustic emission (87%) values with high accuracy. The outcome of this study is expected to make a contribution to the literature showing the impact of turning conditions on the machining characteristics of industrially important materials.
Atıf Yapan Makale Bilgileri
Kurumlar (3)
Necmettin Erbakan Üniversitesi
Meram, Turkey
Niğde Ömer Halisdemir University
Nigde, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey