Machined Surface Roughness Research Articles

Wear Behavior of TiAlVN-Coated Tools in Milling Operations of INCONEL® 718

The use of coatings on cutting tools offers several advantages from the point of view of wear resistance. A recent technique with great coating deposition potential is PVD HiPIMS. TiAlN-based coatings have good resistance to oxidation due to the oxide layer that is formed on their surface. However, by adding doping elements such as Vanadium, it is expected that the wear resistance will be improved, as well as its adhesion to the substrate surface. INCONEL® 718 is a nickel superalloy with superior mechanical properties, which makes it a difficult-to-machine material. Milling, due to its flexibility, is the most suitable technique for machining this alloy. Based on this, in this work, the influence of milling parameters, such as cutting speed (Vc), feed per tooth (fz), and cutting length (Lcut), on the surface integrity and wear resistance of TiAlVN-coated tools in the milling of INCONEL® 718 was evaluated. The cutting length has a great influence on the process, with the main wear mechanisms being material adhesion, abrasion, and coating delamination. Furthermore, it was noted that delamination occurred due to low adhesion of the film to the substrate, as well as low resistance to crack propagation. It was also observed that using a higher cutting speed resulted in increased wear. Moreover, in general, by increasing the milling parameters, machined surface roughness also increased.

The evaluation of machining performances and surface roughness of TZM-molybdenum superalloy processed by silicon carbide powder mixed EDM process using RSM and ANOVA

In the present era, the concept of Industry 4.0 plays a significant part in improving the efficiency, quality, and utilization of resources by automation and enabling intelligent operations in manufacturing processes. As a result, there is a requirement to develop an environment that improves efficiency as well as quality in order to accomplish net zero in manufacturing. The objective of powder mixed electric discharge machining (PMEDM) is to enhance the quality of the surface and machining efficiency of traditional electrical discharge machining. The PMEDM method was applied to TZM-molybdenum superalloy in the present investigation by incorporating silicon carbide particles into the dielectric medium. Powder concentration, peak current, pulse on time, pulse off time, and gap voltage were the considered input variables for the investigation. The impacts of the input variables on the surface of the specimen’s roughness and the rate of material removal (MRR) were examined. The experiment model was built using response surface methodology. To find out if the input factors were significant with regard to each response, an analysis of variance (ANOVA) was performed. Powder concentration, pulse current, gap voltage, and pulse on time are found to be the important input variables for both surface roughness and MRR based on ANOVA analysis.

Design and Study of Machine Tools for the Fly-Cutting of Ceramic-Copper Substrates.

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate's copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate's copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line's efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface's roughness. The device's structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine's gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool's overall dimensions, and simulation validated the fly-cutterring's constituent parts. Enhancing the machine tool's stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway's straightness. The result verified the machine tool's design index, with the Z- and Y-axes' straightness being better than 2.42 μm/800 mm and 2.32 μm/200 mm, respectively. Ultimately, the device's machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications.

A Review of the Factors Influencing Surface Roughness in Machining and Their Impact on Sustainability

Understanding surface roughness generation in machining is critical to estimate the final quality of the part, optimize cutting conditions, reduce costs and improve manufacturing sustainability in industry. This work presents a review of the factors that affect surface roughness generation in machining (turning/milling) processes. Up to twenty-five different factors were identified, which were classified as setup factors (cutting tool, machine tool/fixturing and workpiece factors), operational factors (cutting and process parameters) and processing factors, which are related to the resulting cutting processes, such as built-up edge, chatter or tool wear. The importance of understanding these factors to improve machining sustainability is highlighted through three case studies, ranging from a simple change in the cutting insert to a more complex case where a controlled surface roughness leads to the elimination of a grinding stage. A case study illustrating the potential benefit of MQL in the sustainability of the machining process is also reported from the mold manufacturing industry. In all of the cases, the improvement in sustainability in terms of the reduction in kg of CO2 equivalent is notable, especially when grinding operations are reduced or eliminated from the manufacturing process. This paper can be of interest to practitioners in finishing operations at milling and turning operations that want to increase machining sustainability through a deep understanding of surface roughness generation.

Comparative analysis over microstructural, mechanical properties and cutting performance of TiN, TiVN coatings deposited by magnetron sputtering on SiAlON ceramic tool insert

In this work, TiN and TiVN hard coatings were deposited by magnetron sputtering process on SiAlON cutting tools. FESEM and XRD were used to analyze the coating's microstructure and phase composition. The hardness of the coatings was measured through nanoindentation test and the adhesion of the coatings was determined using the scratch test. Turning tests were carried out on uncoated, TiN and TiVN coated cutting tools in D3 hardened steel turning under dry environment. Tool life, wear progression, and cutting force were investigated under different cutting parameters. Meanwhile, the wear patterns and mechanisms were studied. Surface roughness after machining and cutting temperature during machining were also recorded and analyzed during the experimentation work. Chips after every set of experiments were collected and their morphology study was done using FESEM. For cutting tools coated in TiN and TiVN, abrasion with grooves on the worn faces was the main wear mechanism. The results indicate a significant optimization in performance, with cutting forces minimized by up to 30 %, cutting temperatures reduced by 20 %, and machined surface roughness improved by 45 % when compared to the TiN coated cutting tool. The experimental findings showed that the TiVN-coated SiAlON cutting tool outperformed the uncoated cutting tool and the TiN coated SiAlON cutting tool in terms of cutting performance. This research additionally offers a comparative experimental behavior of coated cutting tools, along with an investigation into cutting performance, which is essential for advancing research and enhancing our understanding of machining hardened steels.

Study on the Effect of Electrolytes on Processing Efficiency and Accuracy of Titanium Alloy Utilizing Laser and Shaped Tube Electrochemical Machining.

Electrochemical machining (ECM) has become more prevalent in titanium alloy processing. However, the presence of the passivation layer on the titanium alloys significantly impacts the performance of ECM. In an attempt to overcome the passivation effects, a high-temperature electrolyte or the addition of halogen ions to the electrolyte has been used. Still, it often results in compromised machining accuracy and surface roughness. This study applied laser and shaped tube electrolytic machining (Laser-STEM) for titanium alloy drilling, where the laser was guided to the machining zone via total internal reflection. The performance of Laser-STEM using different types of electrolytes was compared. Further, the effects of laser power and pulse voltage on the machining side gap, material removal rate (MRR), and surface roughness were experimentally studied while drilling small holes in titanium alloy. The results indicated that the use of passivating electrolytes improved the machining precision, while the MRR decreased with an increase in laser power during Laser-STEM. The MRR showed an increase while using aggressive electrolytes; however, at the same time, the machining precision deteriorated with the increase in laser power. Particularly, the maximum feeding rate of 6.0 mm/min for the tool electrode was achieved using NaCl solution as the electrolyte during Laser-STEM, marking a 100% increase compared to the rate without the use of a laser. Moreover, the model and equivalent circuits were also established to illustrate the material removal mechanisms of Laser-STEM in different electrolytes. Lastly, the processing of deep small holes with a diameter of 1.5 mm, a depth of 38 mm, and a surface roughness of Ra 2 µm was achieved via Laser-STEM without the presence of a recast layer and heat-affected zones. In addition, the cross-inner flow channels in the titanium alloys were effectively processed.

Parametric optimization to establish eco-friendly nanofluid minimum quantity lubrication (NMQL) practice for turning superalloy Inconel 718

Purpose The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra). Design/methodology/approach The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments. Findings The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results. Practical implications The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity. Originality/value No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

An in-process machined surface roughness classification using an ensemble learning algorithm based on extracted automated features from real-time surface images in milling process

An in-process machined surface roughness classification using an ensemble learning algorithm based on extracted automated features from real-time surface images in milling process

Effect of Erosion on Surface Roughness and Hydromechanical Characteristics of Abrasive-Jet Machining

The article contains the fundamental results of the experimental and numerical investigations for pneumo-abrasive unit nozzles with different geometries. The research was purposed by the pressing need to develop an inexpensive and effective working nozzle design of the air-abrasive unit which can be applied for surface processing before some technological processes are performed, as well as for surface coating, descaling after thermal treatment, processing of hollow holes of the crankshafts, smoothing of the inner surfaces of the narrow channels between the impeller blades after electric discharge machining for ultrahigh-pressure combination compressors. Several designs were considered, ranging from the simplest to those with a complicated inner channel geometry. The impact of the nozzle material and challenging inner surface application on its characteristics has also been studied. The research was done using the application of modern CFD complexes for numerical modeling of the air-abrasive mixture discharge from the working nozzle of the pneumo-abrasive unit. In addition, physical experimentation was provided. The methods applied in the research allow for profound, systematic research of spraying units operating on the air-abrasive mixture within a wide range of geometrical and mode parameters. The novelty of the gained results lies in the development of the mathematical model of the pneumo-abrasive nozzle operating process, the working out of a cheaper nozzle design, getting information about air-abrasive mixture distribution along the nozzle length, giving practical recommendations for calculation and designing a working nozzle for the jet-abrasive unit.

The research of the influence of traverse speed and depth of cut on surface roughness in abrasive water jet machining

The main goal in today's production is to make as many products as possible in the shortest possible time. When machining with an abrasive water jet, this means that it is necessary to cut with the highest possible traverse speeds. Machining with a high traverse speed results in an increase in the surface roughness parameters of the surface machined with an abrasive water jet. With the increase in the thickness of the machined material, i.e. the depth of the cut, this is more and more pronounced. The aim of this work is to determine the influence of traverse speed on the roughness of the machined surface, Ra. Also, the influence of the thickness of the samples on the roughness of the processed surface, Ra , was investigated. The material of the samples was AlMg3 of different thicknesses (6, 8, 10 and 12 mm). The samples were cut with traverse speed of 200, 400, 600, 800, 1000 and 1200 mm/min. The roughness parameter of the machined surface, Ra, was measured at different depths, h and at several places along the samples. Based on the measured values of Ra , it was concluded that with the increase in traverse speed, the roughness of the machined surface increases. It was observed that the roughness parameter Ra at the same depth of measurement, h, has approximately the same values. The mathematical model, that describes the influence of traverse speed on the roughness of the machined surface was developed. This model showed satisfactory agreement with the measured values.

Tool wear prediction in edge trimming of MD CFRP considering interlaminar effect

The tool flank wear progresses rapidly in edge trimming of carbon fiber reinforced polymer (CFRP) components, affecting the machined surface quality and process efficiency. This paper presents a tool wear prediction method considering the unique interlaminar effect of the multi-directional (MD) CFRP in the edge trimming process. The experimental results show that different laminar configurations of MD CFRP change the tool wear progression of unidirectional (UD) plies at specific fiber orientations. The interlaminar effects from the adjacent layers with different fiber orientations are explained by their varying supporting strengths on the target layer due to the change of bouncing back height. Therefore, the interlaminar effect from two sides of a UD ply is not a simple summation of the contributions by the individual side. The machined surface roughness of unidirectional ply varies up to 30 % due to the interlaminar effect. A long short-term memory (LSTM) - back propagation (BP) network is developed to predict tool wear length, including the effect of different interlaminar configurations. With the proposed model, the effect of the interlaminar configurations on tool wear progression in edge trimming of MD CFRP is predicted quantitatively. The prediction of the wear length progression based on the developed machine learning model is validated by the experimental results.

Precision machining performance and mechanism of Ni/Ni3Al alloy under cryogenic temperature

Nickel-based superalloys are extensively used in extreme environments due to their excellent mechanical properties, which are attributed to the large volume fraction of the γ' phase. However, the manufacturing process of these alloys is still challenging and can be improved by cryogenic machining. Despite numerous experimental studies on this topic, experimental methods are not sufficient to analyze the ultra-precision machining of Ni-based alloys at low temperatures. There is a lack of mechanistic understanding of the low-temperature nano-cutting process of Ni-based alloys. Thus, we performed a simulation analysis of cryogenic nano-cutting on a Ni/Ni3Al alloy using molecular dynamics (MD). We investigated the stability of cutting forces and microstructural evolution of this two-phase single-crystal alloy. We found that appropriate low temperatures can significantly improve the cutting performance of Ni/Ni3Al two-phase single-crystal alloy. Specifically, we found that the stability of cutting at 123 K is the best for workpiece A, while the cutting stability at 173 K is the best for workpiece B. Additionally, we calculated the machined surface roughness and precision. The minimum surface roughness is observed at 123 K in workpiece A, followed by 223 K. In workpiece B, the minimum roughness is observed at 223 K, while the roughness at 123 K is considerably higher compared to other temperature levels. This study outlines a reference method for determining the optimal temperature during the material removal process. This approach is significant as it helps in understanding the micro-removal mechanism of Ni-based alloys better.

Optimization of Process Parameters to Minimize the Surface Roughness of Abrasive Water Jet Machined Jute/Epoxy Composites for Different Fiber Inclinations

Composites materials like jute/epoxy exhibit high hardness and are considered as difficult-to-machine materials. As a result, alternatives to conventional machining become essential to post-process the composites. Accordingly, due to its non-thermal nature, abrasive water jet machining has recently come to be seen as one of the most promising machining methods for composite materials. In the current study, the impact of machining parameters such as traverse speed (TS), standoff distance (SOD) and abrasive mass flow rate (MFR) on machined surface roughness (Ra) has been investigated. In addition, the optimum combination of process parameters to machine a jute fiber-reinforced polymer composite with minimum Ra is predicted. The experimental results are analyzed using Taguchi and Response Surface Methodology (RSM) approaches to determine the optimum set of process parameters to achieve the lowest roughness values. Without making any changes in the machining conditions, the optimum set of values is determined for two conditions by reinforcing the fiber with 45° inclination and 90° inclination. The results reflect the different optimum combinations for each fiber inclination. For 45° fiber inclination, to achieve the minimum Ra value, the predicted combination is TS = 30 mm/min, SOD = 2 mm and MFR = 0.35 kg/min. When the fiber inclination is 90°, the predicted optimum combination is TS = 25 mm/min, SOD = 2 mm, and MFR = 0.35 kg/min. It is evident from the results that the optimum combination will be changed according to the machining conditions as well as material properties. The results confirm the effect of fiber orientation on surface roughness. The specimen with 45° fiber inclination produces a lower Ra with an average of 4.116 µm, and the specimen with 90° fiber inclination generates a higher Ra with an average of 4.961 µm.

Experimental Investigation of Surface Roughness and MRR in Rotary-Ultrasonic Machining of Float Glass

Experimental Investigation of Surface Roughness and MRR in Rotary-Ultrasonic Machining of Float Glass

Performance assessment of vegetable-based additive enriched cutting fluid for eco-friendly machining environment.

The investigation focuses on determining the effects of canola oil-based cutting fluid with three different volume percentages of boric acid additives over the machining forces and surface roughness while turning hardened AISI 1018 mild steel. Experiments were carried out under Taguchi's design of the experiment concept. The minimum quantity lubrication (MQL) technique was followed to minimize the cutting fluid consumption. The homogeneity of the additives dispersed in the fluid has been validated through a zeta potential study. Machining forces and surface roughness were considered as chief machining objectives. The hybrid mathematical model, grey relational analysis (GRA)-artificial neural network (ANN), has been implemented to assess the performance of developed cutting fluid. The results explored that the canola oil cutting fluid with 5 wt% of boric acid additive exhibits lesser cutting forces and surface roughness. The optimal machining parameters identified by the hybrid modeling are 665rpm of cutting speed, 35mm/min of feed rate, and 0.3mm of depth of cut, along with 5 wt% of boric acid composition in cutting fluid. The results explore the 2.677 times improvement in machining objective in comparison with a non-optimal set of parameters. The implementation of hybrid modeling is considered to be a novel attempt to minimize the machining objectives. It has been recorded a negligible error percentage of 0.66% between GRA and ANN prediction.

Towards Industry 4.0 and Sustainable Manufacturing Applying Environmentally Friendly Machining of a Precipitation Hardened Stainless Steel Using Hot Turning Process

This study aims to address the aforementioned challenges, solutions and implementation perspectives with regard to sustainable manufacturing. In this research, the conventional and hot turning of AISI630 hardened stainless steel have been investigated using PVD-(Ti,Al)N/(Al,Cr)2O3 coated carbide cutting tools at various feed rates and cutting speeds. The high hardness of AISI630, along with the low thermal conductivity, has made it one of the most difficult-to-cut materials, and consequently, its machining is associated with high tool wear and poor workpiece surface quality. AISI630 stainless steel is used in the manufacture of pressure vessels and components exposed to high-stress and corrosive environments in the oil and gas industries. In the present research work, tool flank wear and crater wear mechanisms have been studied in different cutting conditions as well as different preheating temperatures using SEM microscopy. Experimental results showed that hot turning operation at temperatures up to 300 °C reduces flank wear by 33% and improves machined surface roughness by 23%. In addition, FEM simulation has been developed to predict tool tip temperature and cutting forces during turning processes. Experimental and FEM analysis shows that cutting force reduction at a preheating temperature of 300 °C is one of the reasons that reduces tool wear compared to conventional turning. Moreover, it has been shown that by increasing preheating temperature in hot turning, the hardness of the carbides in the workpiece decreases more than the hardness of the tool substrate and reduces coating materials, consequently reducing cutting tool abrasion wear phenomenon.

Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses.

Improving material removal rate and surface roughness in macro electrolyte jet machining of TC4 titanium alloy using tools with a converging-diverging Laval-type electrolyte channel

Improving material removal rate and surface roughness in macro electrolyte jet machining of TC4 titanium alloy using tools with a converging-diverging Laval-type electrolyte channel

Experimental investigations of impact behavior and milling performance of T700-CFRP under cryogenic conditions

The carbon fiber reinforced plastic (CFRP) composite is a reliable body material for modern airplanes, and extremely difficult to cut owing to its complex structure and special mechanical characteristics. In cutting CFRP, fiber breakage and delamination defects very frequently appear on the machined surface. The use of cryogenic cooling in CFRP cutting has proven to be an advanced approach to improving the machined surface quality of CFRP composites. To reveal the mechanism of milling CFRP under cryogenic conditions, this study investigates the impact behavior and cutting performance of T700-CFRP at low temperatures. The impact test of T700-CFRP unidirectional laminates with 0°, 45°, and 90° fiber directions was performed at various temperatures ranging from 20 °C to −196 °C, to analyze the impact toughness and the fracture morphology. Side milling experiments of CFRP laminates of 0°, 45°, and 90° fiber directions with the PCD endmill were conducted at the same temperatures used in the impact test. Experimental findings revealed that for 0° specimens, the impact toughness increased at lower temperatures, and the impact damage worsened as the temperature dropped from −120 °C to −196 °C. There were no evident changes in the impact toughness or impact damage of 45° and 90° specimens with the decrease in temperature. The feed force and radial force increased as the cooling medium temperature lowered during milling CFRP. The machined surface of 45° workpieces was the roughest among all the workpieces and covered with numerous pits and craters. The machined surface roughness Ra of 0° workpieces decreased as the cooling medium temperature was lowered from 20 °C to −160 °C and then increased at −196 °C, while the Ra value of both 45° and 90° workpieces rose as the temperature dropped from 0 °C to −196 °C.

Surface morphology improvement and residual stress distribution of SiCp/Al composites using high-power fiber laser-CNC milling cooperative machining strategy

This work proposes a novel high-power fiber laser-CNC milling cooperative strategy for cutting SiCp/Al composites, which aims to machine MMCs with high efficiency and limited damage, and achieve compressive residual stress distribution within machined surface layer in order to subsequently improve service performance of composite components. Experimental results shows that the cooperative machining strategy has a sufficient improvement in machining efficiency with limited surface defects compared to conventional methods. Completely removal of laser cutting induced surface defects, including striations, combustion and cataphracted dross, was achieved under high speed milling with small cutting depth down to 0.2 mm. Additionally, it was found that the roughness of final machined surface was impacted by the parameters of both laser cutting and milling process. The surface roughness decreased by 35 % with the laser power increased from 4.0 kW to 4.5 kW, while decreased by 60 % when milling speed decreased from 180 m/min to 90 m/min. Compressive residual stress was prevalent within the surface layer during fiber laser-CNC milling cooperative machining process. The experimental findings in this work verified the feasibility and advantages of the cooperative machining strategy for processing difficult-to-cut metal matrix composites.