The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

Minimum quantity lubrication (MQL) is a sustainable method that has been efficiently applied to achieve machinability improvements with various materials in recent years, such as hardened steels, superalloys, soft metals, and composites. This study is the first to focus on the performance evaluation of MQL and dry milling environments with AISI 1040 steel. The tool wear, cutting temperature, and power consumption were considered as the quality responses while cutting speed, feed rate and machining environment are taken as input parameters. The effects of the influential factors are analyzed using analysis of variance (ANOVA) and bar charts. Additionally, Taguchi signal-to-noise (S/N) ratios are utilized in order to determine the optimum parameters for the best quality responses. The results show that the MQL system provides better performance compared to dry milling by reducing the tool wear, cutting temperature, and power consumption. According to the ANOVA results, the cutting environment affects the cutting temperature (37%) and power consumption (94%), while cutting speed has importance effects on the tool wear (74%). A lower cutting speed (100 m/min) and feed rate (0.10 mm/rev) should be selected under MQL conditions to ensure minimum tool wear and power consumption; however, a higher feed rate (0.15 mm/rev) needs to be selected along with a low cutting speed and MQL conditions to ensure better temperatures. A comparative evaluation is carried out on the tool wear, cutting temperature, and power consumption under MQL and dry environments. This investigation is expected to contribute to the current literature, highlighting the superiority of sustainable methods in the milling of industrially important materials.

AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (R_a) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as V_c = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.

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%).

The possibility of producing complex metallic parts in various industries can be attributed to the selective laser melting (SLM) additive manufacturing method. As a powder bed fusion technique, SLM fabricates the product layer by layer. The state-of-the-art research on SLM, the metallic alloys utilized in the process, and the surface morphology of fabricated parts are discussed in this paper. The present report contributes to the literature by providing a comprehensive overview of the surface morphology of metallic alloys fabricated using the SLM additive manufacturing method. The article covers recent research on SLM, metallic alloys used in the process, and the surface morphology of fabricated parts. Insights into the challenges and opportunities of SLM for the fabrication of metallic parts with desired surface properties are provided. In the first part, parameters representing surface morphology are introduced and types of surface defects are viewed. Subsequently, influence of process variables during the production phase is discussed in-depth, overviewing several parameters such as laser, scanning, and geometric parameters. Surface morphology enhancement, namely in situ treatment, post-processing, and finishing-machining techniques, is viewed separately by classifying them into subtopics, in respect of their improvement effectiveness. Mechanical aspects of the microstructure and surface are evaluated in correlation with the surface morphology. The discussion of the findings considering the advantages and disadvantages of this technology is summarized finally. It is concluded that laser parameter effect’s significance depends on the work alloy. In the literature, process parameters are systematically studied, and better surface quality and favourable surface morphology of as build surfaces are possible. Moreover, it has been concluded that the surface morphology and quality of SLMed products can be improved with in situ techniques and post-treatments. Relieving residual stresses and decreasing porosity on the surface (various types of holes, pinholes, vacancies, etc.) during SLM operation are possible by base plate heating, powder preheating, and re-scanning. It is also noticed that the machinability studies of SLMed parts mainly focus on mechanical machining such as grinding and milling. Studies on the correlation between surface morphology of SLMed parts and mechanical properties are relatively scarce comparing to works on SLM production parameters. Finally, as an emerging technology for vast production of industrial items, it is concluded that surface morphology of SLMed products needs systematic correlation studies between process parameters and surface results.

The combination of powder metallurgy and ball milling method has been widely regarded as the most beneficial route for producing multi-walled carbon nanotubes (MWCNTs)-reinforced aluminum matrix composites. In this study, the effects of different milling times (1, 2, 4, and 8 h) on the structural, morphological, and crystallographic properties of MWCNTs-reinforced Al7075 composite powders were characterized by particle size analyzer, Raman spectroscopy, scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), and X-ray diffraction (XRD). After the morphological and structural characterization of the milled powders, the microstructural and mechanical properties of the hot-pressed composites were evaluated using an optical microscope, SEM, density, and Brinell hardness measurements. Considering milled powder characterization, the MWCNTs were gradually distributed and embedded within the matrix as the milling time increased. Milling for 8 h resulted in a minimum level of particle size (11 µm) with shortened and uniformly dispersed CNTs. Brinell hardness of the composite increased from 91 to 237 HB -a ⁓%160 after 8 h of milling. Such a remarkable increment in hardness could be attributed to several concurrent strengthening effects related to dispersion, solution, grain refinement, and Orowan looping mechanisms. However, relative density results revealed that the composite produced by 2 h milled powders exhibited the highest density (%99.96). The observed differences between hardness and density results were ascribed to powders’ deteriorated packing and sintering behavior due to an increment in the hardness of particles and variation in particle size range and morphology, which resulted from following different milling protocols.