Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.

Optimization of Processing Parameters and Adhesive Properties of Aluminum Oxide Thin-Film Transition Layer for Aluminum Substrate Thin-Film Sensor

A thin-film strain micro-sensor is a cutting force sensor that can be integrated with tools. Its elastic substrate is an important intermediate to transfer the strain generated by the tools during cutting to the resistance-grid-sensitive layer. In this paper, 1060 aluminum is selected as the elastic substrate material and aluminum oxide thin film is selected as the transition layer between the aluminum substrate and the silicon nitride insulating layer. The Stoney correction formula applicable to the residual stress of the aluminum oxide film is derived, and the residual stress of the aluminum oxide film on the aluminum substrate is obtained. The influence of Sputtering pressure, argon flow and negative substrate bias process parameters on the surface quality and sputtering power of the aluminum oxide thin film is discussed. The relationship model between process parameters, surface roughness, and sputtering rate of thin films is established. The sputtering process parameters for preparing an aluminum oxide thin film are optimized. The micro-surface quality of the aluminum oxide thin film obtained before and after the optimization of the process parameters and the surface quality of Si₃N₄ thin film sputtered on alumina thin film before and after the optimization are compared. It is verified that the optimized process parameters of aluminum oxide film as a transition layer can improve the adhesion between the insulating-layer silicon nitride film and the aluminum substrate.

A Comparative Review of Thermocouple and Infrared Radiation Temperature Measurement Methods during the Machining of Metals

During the machining process, substantial thermal loads are generated due to tribological factors and plastic deformation. The increase in temperature during the cutting process can lead to accelerated tool wear, reducing the tool’s lifespan; the degradation of machining accuracy in the form of dimensional inaccuracies; and thermally induced defects affecting the metallurgical properties of the machined component. These effects can lead to a significant increase in operational costs and waste which deviate from the sustainability goals of Industry 4.0. Temperature is an important machining response; however, it is one of the most difficult factors to monitor, especially in high-speed machining applications such as drilling and milling, because of the high rotational speeds of the cutting tool and the aggressive machining environments. In this article, thermocouple and infrared radiation temperature measurement methods used by researchers to monitor temperature during turning, drilling and milling operations are reviewed. The major merits and limitations of each temperature measurement methodology are discussed and evaluated. Thermocouples offer a relatively inexpensive solution; however, they are prone to calibration drifts and their response times are insufficient to capture rapid temperature changes in high-speed operations. Fibre optic infrared thermometers have very fast response times; however, they can be relatively expensive and require a more robust implementation. It was found that no one temperature measurement methodology is ideal for all machining operations. The most suitable temperature measurement method can be selected by individual researchers based upon their experimental requirements using critical criteria, which include the expected temperature range, the sensor sensitivity to noise, responsiveness and cost.

Study of the Pattern Preparation and Performance of the Resistance Grid of Thin-Film Strain Sensors

The thin-film strain sensor is a cutting-force sensor that can be integrated with cutting tools. The quality of the alloy film strain layer resistance grid plays an important role in the performance of the sensor. In this paper, the two film patterning processes of photolithography magnetron sputtering and photolithography ion beam etching are compared, and the effects of the geometric size of the thin-film resistance grid on the resistance value and resistance strain coefficient of the thin film are compared and analyzed. Through orthogonal experiments of incident angle, argon flow rate, and substrate negative bias in the ion beam etching process parameters, the effects of the process parameters on photoresist stripping quality, etching rate, surface roughness, and resistivity are discussed. The effects of process parameters on etching rate, surface roughness, and resistivity are analyzed by the range method. The effect of substrate temperature on the preparation of Ni Cr alloy films is observed by scanning electron microscope. The surface morphology of the films before and after ion beam etching is observed by atomic force microscope. The influence of the lithography process on the surface quality of the film is discussed, and the etching process parameters are optimized.

Investigations on the Potential of 5G for the Detection of Wear in Industrial Roller-Burnishing Processes

Roller burnishing represents an economical alternative to conventional surface-finishing processes, such as fine turning or honing. In contrast to the well-known wear mechanisms of chip-forming processes, the wear behavior in roller-burnishing is strongly based on the experience of the machine operators. The nature of the finishing process makes roller-burnishing very sensitive to surface defects, as it is often not possible to rework the last step in a process chain. In the present work, a prototype for a smart roller-burnishing tool with 5G communication is presented, which serves as an inline-monitoring tool to detect tool wear. A suitable metric to monitor the tool wear of the manufacturing roll is suggested, and the potentials of 5G communication for the described use-case are evaluated. Based on the signal-to-noise ratio of the process-force, a metric is found that distinguishes new rolls from worn rolls with very small defects on the micrometer scale. Using the presented approach, it was possible to distinguish the signal-to-noise ratio of a roll with very small wear marks by 3.8% on average. In the case of stronger wear marks, on the order of 20 µm, the difference increased to up to 15.6%.

Contribution Ratio Assessment of Process Parameters on Robotic Milling Performance

Robotic milling has broad application prospects in many processing fields. However, the milling performance of a robot in a certain posture, such as in face milling or grooving tasks, is extremely sensitive to process parameters due to the influence of the serial structure of the robot system. Improper process parameters are prone to produce machining defects such as low surface quality. These deficiencies substantially decrease the further application development of robotic milling. Therefore, this paper selected a certain posture and carried out the robotic flat-end milling experiments on a 7075-T651 high-strength aeronautical aluminum alloy under dry conditions. Milling load, surface quality and vibration were selected to assess the influence of process parameters like milling depth, spindle speed and feed rate on the milling performance. Most notably, the contribution ratio based on the analysis of variance (ANOVA) was introduced to statistically investigate the relation between parameters and milling performance. The obtained results show that milling depth is highly significant in milling load, which had a contribution ratio of 69.25%. Milling depth is also highly significant in vibration, which had a contribution ratio of 51.41% in the X direction, 41.42% in the Y direction and 75.97% in the Z direction. Moreover, the spindle speed is highly significant in surface roughness, which had a contribution ratio of 48.02%. This present study aims to quantitatively evaluate the influence of key process parameters on robotic milling performance, which helps to select reasonable milling parameters and improve the milling performance of the robot system. It is beneficial to give full play to the advantages of robots and present more possibilities of robot applications in machining and manufacturing.

Ecodesign of the Aluminum Bronze Cutting Process

The realization of products from materials with high properties generally involves very high energy consumption. Thus, in the research, it was considered to optimize the machining process by cutting of an aluminum bronze alloy, so as to obtain a reduction in energy consumption in correlation with the roughness of the machined surfaces. The research focused on the processing of a semi-finished product with a diameter of Ø = 20 mm made of aluminum bronze (C62300). In addition, in the research, the aim was to establish some correlations between the amount of power consumed and the quality of the surfaces processed by cutting. In this sense, the forces were measured in the 3 directions specific to the cutting process (F_c; F_f; F_p) for 3 tools construction variants and power consumed. The results showed that, if a certain constructive variant of the cutting tool is used in the processing, a reduction of the power consumed to cutting can be obtained by approximately 30% and a reduction of the roughness of the processed surface by approximately 90–100%. Furthermore, following the statistical processing of the results, it was shown that it would be advisable to use, especially in roughing processes, the cutting tool variant that offers the greatest reduction in roughness and cutting power.

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet

Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 μm and 100 μm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.

Fabrication, Performance, Characterization and Experimental Calibration of Embedded Thin-Film Sensor for Tool Cutting Force Measurement

Thin-film strain sensors are widely used because of their small volume, fast strain response and high measurement accuracy. Among them, the thin-film material and preparation process of thin-film strain sensors for force measurement are important aspects. In this paper, the preparation process parameters of the transition layer, insulating layer and Ni-Cr alloy layer in a thin-film strain sensor are analyzed and optimized, and the influence of each process parameter on the properties of the thin film are discussed. The surface microstructure of the insulating layer with Al2O3 or Si3N4 transition layers and the film without transition layer were observed by atomic force microscopy. It is analyzed that adding a transition layer between the stainless steel substrate and insulation layer can improve the adhesion and flatness of the insulation layer. The effects of process parameters on elastic modulus, nanohardness and strain sensitivity coefficient of the Ni-Cr resistance layer are discussed, and electrical parameters such as the resistance strain coefficient are analyzed and characterized. The static calibration of the thin-film strain sensor is carried out, and the relationship between the strain value and the output voltage is obtained. The results show that the thin-film strain sensor can obtain the strain generated by the cutting tool and transform it into an electrical signal with good linearity through the bridge, accurately measuring the cutting force.

Smart Cutting Tools Used in the Processing of Aluminum Alloys

The processing of aluminum alloys in optimal conditions is a problem that has not yet been fully resolved. The research carried out so far has proposed various intelligent tools, but which cannot be used in the presence of cooling-lubricating fluids. The objective of the research carried out in the paper was to design intelligent tools that would allow a control of the vibrations of the tool tip and to determine a better roughness of the processed surfaces. The designed intelligent tools can be used successfully in the processing of aluminum alloys, not being sensitive to coolants-lubricants. In the research, the processing by longitudinal turning of a semi-finished product with a diameter Ø = 55 mm of aluminum alloy A2024-T3510 was considered. Two constructive variants of smart tools were designed, realized, and used, and the obtained results were compared with those registered for the tools in the classic constructive variant. The analysis of vibrations that occur during the cutting process was performed using the following methods: Fast Fourier Transform (FFT); Short-Time Fourier-Transformation (STFT); the analysis of signal of vibrations. A vibration analysis was also performed by modeling using the Finite Element Method (FEM). In the last part of the research, an analysis of the roughness of the processed surfaces, was carried out and a series of diagrams were drawn regarding curved profiles; filtered profiles; Abbott-Firestone curve. Research has shown that the use of smart tools in the proposed construction variants is a solution that can be used in very good conditions for processing aluminum alloys, in the presence of cooling-lubrication fluids.

Recovering Evaluation of Narrow-Kerf Teeth of Mini Sash Gang Saws

Sash gang saws with narrow-kerf saw blades are used in the production of glued laminate flooring elements in plants where dry technology is applied. This means that boards or friezes are sawn into top layer lamellae in dry conditions (moisture content of about 10–12%) from expensive wood species, often exotic. The object of this research was stellite-tipped teeth of narrow kerf saw blades sharpened under industrial conditions. A NIKON ECLIPSE Ti-S microscope equipped with a NIKON DS-Fi2 recording camera was used to take pictures of teeth, which were analysed in a graphical software to measure the radii of the main cutting edges. The high-quality images obtained were used to determine the values of the rounding radii of the cutting edges. It was noted that the quality of edges regenerated in industrial conditions, some of which had chipping, was lower than that of brand new saw blades.

Modelling and Analysis of Surface Evolution on Turning of Hard-to-Cut CLARM 30NiCrMoV14 Steel Alloy

Industrial practitioners are working on predictive solutions for the precise evaluation of input parameters and processed surfaces of engineering materials. To aid the aeronautical industry, this study is an effort to develop the mathematical modelling for comprehensive surface analysis of input parameters and surface finish after dry machining of CLARM HBR, a steel alloy with attractive mechanical properties and wide applications in large caliber gun barrels and high-pressure vessels. Feed rate, rotational speed, and depth of cut were taken as quantitative parameters, whereas machining time was considered as a categorical factor with a classification of three levels. Response surface methodology (RSM) with a central component design has been used for the constitution of the experimental design, mathematical modelling, and analysis of developed models. Eighteen samples were prepared to perform the experimentation for the development of prediction models. The adequacy of the developed models was verified using analysis of variance (ANOVA), and the models were validated using confirmatory trial experiments, which revealed the experimental results agreeing with predictions. The feed rate was the most significant parameter in achieving the desired surface finish. An increase in rotational speed at a low feed rate resulted in very fine surface texture, as though it deteriorated the surface finish at higher feed rates. The superior surface quality obtained was 0.137 μm at parametric settings of 0.19 mm/rev feed, 90 rpm speed, 3 mm depth of cut, and 4 min time. Overall, higher values of surface roughness were frecorded in the third level of process variable time. The developed empirical models are expected to aid manufacturers and machining practitioners in the prediction of the desired surface finish concerning different parameters before the experimentations.

Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

In turning, the wear control of a cutting tool benefits product quality enhancement, tool-related costs‘ optimisation, and assists in avoiding undesired events. In small series and individual production, the machine operator is the one who determines when to change a cutting tool, based upon their experience. Bad decisions can often lead to greater costs, production downtime, and scrap. In this paper, a Tool Condition Monitoring (TCM) system is presented that automatically classifies tool wear of turning tools into four classes (no, low, medium, high wear). A cutting tool was monitored with infrared (IR) camera immediately after the cut and in the following 60 s. The Convolutional Neural Network Inception V3 was used to analyse and classify the thermographic images, which were divided into different groups depending on the time of acquisition. Based on classification result, one gets information about the cutting capability of the tool for further machining. The proposed model, combining Infrared Thermography, Computer Vision, and Deep Learning, proved to be a suitable method with results of more than 96% accuracy. The most appropriate time of image acquisition is 6–12 s after the cut is finished. While existing temperature based TCM systems focus on measuring a cutting tool absolute temperature, the proposed system analyses a temperature distribution (relative temperatures) on the whole image based on image features.

Towards Analysis and Optimization for Contact Zone Temperature Changes and Specific Wear Rate of Metal Matrix Composite Materials Produced from Recycled Waste

Tribological properties are important to evaluate the in-service conditions of machine elements, especially those which work as tandem parts. Considering their wide range of application areas, metal matrix composites (MMCs) serve as one of the most significant materials equipped with desired mechanical properties such as strength, density, and lightness according to the place of use. Therefore, it is crucial to determine the wear performance of these materials to obtain a longer life and to overcome the possible structural problems which emerge during the production process. In this paper, extensive discussion and evaluation of the tribological performance of newly produced spheroidal graphite cast iron-reinforced (GGG-40) tin bronze (CuSn10) MMCs, including optimization, statistical, graphical, and microstructural analysis for contact zone temperature and specific wear rate, are presented. For this purpose, two levels of production temperature (400 and 450 °C), three levels of pressure (480, 640, and 820 MPa), and seven different samples reinforced by several ingredients (from 0 to 40 wt% GGG-40, pure CuSn10, and GGG-40) were investigated. According to the obtained statistical results, the reinforcement ratio is remarkably more effective on contact zone temperature and specific wear rate than temperature and pressure. A pure CuSn10 sample is the most suitable option for contact zone temperature, while pure GGG-40 seems the most suitable material for specific wear rates according to the optimization results. These results reveal the importance of reinforcement for better mechanical properties and tribological performance in measuring the capability of MMCs.

Tribological Aspects, Optimization and Analysis of Cu-B-CrC Composites Fabricated by Powder Metallurgy

Tribological properties of engineering components are a key issue due to their effect on the operational performance factors such as wear, surface characteristics, service life and in situ behavior. Thus, for better component quality, process parameters have major importance, especially for metal matrix composites (MMCs), which are a special class of materials used in a wide range of engineering applications including but not limited to structural, automotive and aeronautics. This paper deals with the tribological behavior of Cu-B-CrC composites (Cu-main matrix, B-CrC-reinforcement by 0, 2.5, 5 and 7.5 wt.%). The tribological characteristics investigated in this study are the coefficient of friction, wear rate and weight loss. For this purpose, four levels of sliding distance (1000, 1500, 2000 and 2500 m) and four levels of applied load (10, 15, 20 and 25 N) were used. In addition, two levels of sliding velocity (1 and 1.5 m/s), two levels of sintering time (1 and 2 h) and two sintering temperatures (1000 and 1050 °C) were used. Taguchi’s L₁₆ orthogonal array was used to statistically analyze the aforementioned input parameters and to determine their best levels which give the desired values for the analyzed tribological characteristics. The results were analyzed by statistical analysis, optimization and 3D surface plots. Accordingly, it was determined that the most effective factor for wear rate, weight loss and friction coefficients is the contribution rate. According to signal-to-noise ratios, optimum solutions can be sorted as: the highest levels of parameters except for applied load and reinforcement ratio (2500 m, 10 N, 1.5 m/s, 2 h, 1050 °C and 0 wt.%) for wear rate, certain levels of all parameters (1000 m, 10 N, 1.5 m/s, 2 h, 1050 °C and 2.5 wt.%) for weight loss and 1000 m, 15 N, 1 m/s, 1 h, 1000 °C and 0 wt.% for the coefficient of friction. The comprehensive analysis of findings has practical significance and provides valuable information for a composite material from the production phase to the actual working conditions.

Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry

In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy

The features of composite materials such as production flexibility, lightness, and excellent strength put them in the class of materials that attract attention in various critical areas, i.e., aerospace, defense, automotive, and shipbuilding. However, the machining of composite materials displays challenges due to the difficulty in obtaining structural integrity. In this study, Cu/Mo-SiC_P composite materials were produced by powder metallurgy with varied reinforcement ratios and then their machinability was investigated. In machinability experiments, the process parameters were selected as cutting speed (v_C), feed rate (f), depth of cut (a_P), and reinforcement ratio (R_R). Two levels of these parameters were taken as per the Taguchi’s L8 orthogonal array, and response surface methodology (RSM) is employed for parametric optimization. As a result, the outcomes demonstrated that R_R = 5%, f = 0.25 mm/rev, a_P = 0.25 mm, v_C = 200 m/min for surface roughness, R_R = 0%, f = 0.25 mm/rev and a_P = 0.25 mm and v_C = 200 m/min for flank wear and R_R = 0%, f = 0.25 mm/rev, a_P = 0.25 mm, v_C = 150 m/min for cutting temperature for cutting temperature and flank wear should be selected for the desired results. In addition, ANOVA results indicate that reinforcement ratio is the dominant factor on all response parameters. Microscope images showed that the prominent failure modes on the cutting tool are flank wear, built up edge, and crater wear depending on reinforcement ratio.

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.

Assessment of CVD- and PVD-Coated Carbides and PVD-Coated Cermet Inserts in the Optimization of Surface Roughness in Turning of AISI 1045 Steel

In this study, an experimental and statistic investigation approach based on analysis of variance (ANOVA) and response surface methodology (RSM) techniques was performed to find the significant main effects and two-factor interaction effects and to determine how the controllable factors such as cutting speed, feed rate, depth of cut (DOC), tool nose radius, substrate and coating method of cutting tools influence surface quality in turning of AISI 1045 steel. The first optimal or near-optimal conditions for the quality of the generated surface and the second ones, including maximum material removal rate, were established using the proposed regression equations. The group mean roughness of the turned workpieces was lower from using chemical vapor deposition (CVD)-coated carbide inserts than the group means of other types of inserts; however they could not achieve the specific lowest roughness. The physical vapor deposition (PVD)-coated carbide and cermet inserts achieved the best surface quality when the specific combinations within the range interval of controllable factors were used in the experiment, showing that they may be applied to finish turning processes or even to particular high material removal rate conditions associated with the lowest roughness.

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 (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.