Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via Tool Condition Monitoring System in turning of AISI 5140

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 (VB) 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 (vc), feed rate (f) and depth of cut (ap) 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, VB, 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 vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%)

Parametric optimization for cutting forces and material removal rate in the turning of AISI 5140

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

Optimization study on surface roughness and tribological behavior of recycled cast iron reinforced bronze MMCs produced by hot pressing

Surface roughness reflects the quality of many operational parameters, namely service life, wear characteristics, working performance and tribological behavior of the produced part. Therefore, tribological performance is critical for the components used as tandem parts, especially for the MMCs (Metal Matrix Composites) which are a unique class of materials having extensive application areas such as aerospace, aeronautics, marine engineering and the defense industry. Current work covers the optimization study of production parameters for surface roughness and tribological indicators of newly produced cast iron reinforced bronze MMCs. In this context, two levels of temperature (400 and 450 °C), three levels of pressure (480, 640 and 820 MPa) and seven levels of reinforcement ratios (60/40, 70/30, 80/20, 90/10, 100/0 of GGG40/CuSn10, pure bronze-as received and pure cast iron-as received) are considered. According to the findings obtained by Taguchi’s signal-to-noise ratios, the reinforcement ratio has a dominant effect on surface roughness parameters (Ra and Rz), the coefficient of friction and the weight loss in different levels. In addition, 100/0 reinforced GGG40/CuSn10 gives minimum surface roughness, pure cast iron provides the best weight loss and pure bronze offers the desired coefficient of friction. The results showed the importance of material ingredients on mechanical properties by comparing a wide range of samples from starting the production phase, which provides a perspective for manufacturers to meet the market supply as per human requirements

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

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

Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

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 (Ra) 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 Vc = 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

Evaluation of the Role of Dry and MQL Regimes on Machining and Sustainability Index of Strenx 900 Steel

Sustainable technologies draw attention in the machining industry thanks to their contributions in many aspects such as ecological, economic, and technological. Minimum quantity lubrication (MQL) is one of these techniques that enable to convey of the high pressurized cutting fluid toward the cutting zone as small oil particulates. This study examines the potency of MQL technology versus dry conditions on the machining quality during the milling of structural Strenx 900 steel within the sustainability index. High strength and toughness properties make this steel a hard-to-cut material providing an important opportunity to test the performances of dry and MQL environments. The outcomes of the experimental data demonstrated that MQL is superior in enhancing the quality of significant machining characteristics namely surface roughness (up to 35%), flank wear (up to 94%), wear mechanisms, cutting energy (up to 28%), and cutting temperatures (up to 14%). Furthermore, after analyzing the main headings of the sustainable indicators, MQL provided the same (+5) desirability value with a dry (+5) medium. This experimental work presents a comparative approach for improved machinability of industrially important materials by questioning the impact of sustainable methods

The Effects of MQL and Dry Environments on Tool Wear, Cutting Temperature, and Power Consumption during End Milling of AISI 1040 Steel

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

Investigation of the Effects of Cooling and Lubricating Strategies on Tribological Characteristics in Machining of Hybrid Composites

Engineering materials are expected to contain physical and mechanical properties to meet the requirements and to improve the functionality according to their application area. In this direction, hybrid composites stand as an excellent option to fulfill these requests thanks to their production procedure. Despite the powder metallurgy method that allows for manufacturing products with high accuracy, machining operations are still required to obtain a final product. On the other hand, such materials are characterized with uncertainties in the structure and extremely hard reinforcement particles that aggravate the machinability. One of the prominent solutions for better machinability of composites is to use evolutionary cooling and lubricating strategies. This study focuses on the determination of tribological behavior of Cu-based, B-Ti-SiCP reinforced, about 5% wt. hybrid composites under milling of several environments, such as dry, minimum quantity lubrication (MQL)-assisted and cryogenic LN2-assisted. Comprehensive evaluation was carried out by considering tool wear, temperature, energy, surface roughness, surface texture and chips morphology as the machinability characteristics. The findings of this experimental research showed that cryogenic cooling improves the tribological conditions by reducing the cutting temperatures, flank wear tendency and required cutting energy. On the other hand, MQL based lubricating strategy provided the best tool wear index and surface characteristics, i.e., surface roughness and surface topography, which is related to spectacular ability in developing the friction conditions in the deformation zones. Therefore, this paper offers a novel milling strategy for Cu-based hybrid composites with the help of environmentally-friendly techniques