Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (V_B) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L₉ orthogonal design principle, the basic machining parameters cutting speed (v_c), feed rate (f) and depth of cut (a_p) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, V_B, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at v_c = 150 m/min, f = 0.09 mm/rev, a_p = 1 mm) to obtain the best results for V_B, Ra and the sensorial data, with a high success rate (82.5%).

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 present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

Ductile iron can acquire enhanced thermal and mechanical properties from austempering heat treatment. The present study aims to identify the function of different cutting parameters affecting machinability and to quantify its effects. Turning was performed to test machinability according to the ISO3685-1993 (E) standard. After austenitizing at 900 °C for 90 min, austempered ductile iron (ADI) specimens were quenched in a salt bath at 380 °C for 90 min. The cutting force signals along three directions were measured in real time, whereas flank wear and surface roughness were measured offline. For the cutting parameters, the cutting speed and depth of cut were varied, but the feed rate was kept constant. In the flank wear tests, machining length was corresponded to tool life. In addition, in order to find out the effect of cutting parameters on surface roughness (Ra), tangential force (Ft), and flank wear (VB) during turning, response surface methodology (RSM) was utilized by using experimental data. The effect of the depth of cut on the surface roughness was negligible but considerable in the cutting forces. The increased cutting speed produced a positive effect on surface roughness. It is found that the cutting speed was the dominant factor on the surface roughness, tangential force, and flank wear.