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Using cutting fluids is considered in industrial applications and academia due to their increased influence over many aspects such as machinability, sustainability and manufacturing costs. This paper addresses the machinability perspective by examining indicators such as roughness, cutting temperature, tool wear and chip morphology during the milling of mold steel. A special type of steel is Nimaxm which is a difficult-to-cut material because of its high strength, toughness, hardness and wear resistance. Since mold steels have the reverse geometry of the components produced by this technology, their surface quality and dimensional accuracy are highly important. Therefore, two different strategies, i.e., dry and minimum quantity lubrication (MQL), were chosen to conduct an in-depth analysis of the milling performance during cutting at different cutting speeds, feed rates and cutting depths. Without exception, MQL technology showed a better performance than the dry condition in obtaining better surface roughnesses under different cutting parameters. Despite that only a small improvement was achieved in terms of cutting temperature, MQL was found to be successful in protecting the cutting tool from excessive amounts of wear and chips. This paper is anticipated to be a guide for manufacturers and researchers in the area of mold steels by presenting an analysis of the capabilities of sustainable machining methods.

The resources of the earth are being consumed day by day with the increasing population and necessities of humankind in many areas, such as industrial applications and basic needs in houses, workplaces and transportation. As a consequence, careful usage of the energy sources and the conversed energy is of great importance in order to obtain sustainable development. Machining operations have a large percentage of all manufacturing methods in terms of depleted energy which gives them a high potential for reducing the total energy consumption. The approaches handled in the literature for the minimization of the consumed energy in the machining industry were considered in this study. While several machinability characteristics under different machining processes were investigated broadly in the context of composites and superalloys, the comparison of these systems has been given cursory attention in the current literature, specifically for cutting energy saving. The overall performance of these group material systems utilizing widely in numerous significant industrial areas supplies important signs about manufacturing costs, service conditions and environmental impacts. It is highly crucial to monitor the indicators of energy-saving phenomena of the machined parts since the mechanisms behind the energy consumption of these systems is very complex and dynamic owing to different process-induced variables. This well-organized review paper distinguishes itself from previous studies in this field since the comprehensive literature survey paves the way for diverse approaches that regard energy saving, especially for composites and superalloys under different machining operations. This overview paper aims to contribute to the current literature by highlighting the effects of the state-of-the-art approaches in reducing energy consumption in the machining of industrially important materials. This study can also establish a framework in the context of the process-property interactions to comprehend the influence of energy-saving mechanisms through machining in a system of interest.

In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

Machining of AISI 304 austenitic stainless steel is considered to be difficult due to its structural aspects and low thermal conductivity, which leads to increased temperatures during machining. To overcome the challenges of machining AISI 304 stainless steel, several cooling and lubricating techniques have been developed. The main objective of this experimental study is to evaluate the different turning conditions of AISI304 stainless steel under dry and minimum quantity lubrication (MQL) environment conditions. The machining experiments were conducted using a two-level full factorial design method and utilized a TiC-coated cutting tool. The tool-tip temperature, cutting force and surface roughness were analyzed regarding three cutting parameters namely, cutting speed, feed rate and cutting depth. Also, chip macro-morphology was investigated to define the interaction at the tool-chip-workpiece region. The cutting medium affects the surface roughness significantly (more than 100%) for all cutting parameter values. In some environmental cutting conditions, high cutting speed provides 10% lesser surface roughness than low cutting speed parameters. Also, the cutting force decreases by 20% in low feed rate machining conditions. However, the effect of this parameter disappeared for cutting forces in high feed rates and low cutting depth conditions in both MQL and dry environments. Cutting speed was observed as the most influential factor on surface roughness, followed by feed rate. The depth of cut was the main parameter that caused the temperature to increase in the dry machining environment.

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.

In this study, surface roughness analysis was performed in the turning of C45 steel underMinimum Quantity Lubrication (MQL) and dry machining conditions. For this purpose, turningexperiments were carried out using two different cutting speeds and feed parameters. In order to improvethe machinability of C45 steel, which is one of the most used industrial materials, a graphical and statisticalevaluation was made as well as the optimization approach. With three different evaluations, both thepositive effect of MQL on the roughness in the turning process has been explained and differentperspectives have been created for C45 steel in terms of surface roughness. The optimum values of the bestsurface roughness were determined and the cutting conditions was revealed as the most important parametereffecting the roughness by analysis of variance.