Selection Of Machining Parameters Research Articles

Investigation of Machining Parameters for Turning Process

Manufacturing industries mainly concentrates on how to minimize the cost of the products. The process planers have to prepare the process plan of the product based on the availability of the recourses. All the information is available in the plan including inspection and delivery date of the product. Based on the recommendation of the manufacturing and inspection methods, components are produced at affordable cost. Most of the research work concentrated on selection of machining parameters using optimization techniques and also prove that the machining time / cost were minimized for the particular components / operations. In this work, tool nomenclature is to be considered for the investigation for selection of machining parameters. Work presents an experimental investigation of the influence of the three most important machining parameters of depth of cut, feed rate and spindle speed on surface roughness during turning of aluminium alloy. In this study, the design of experiment which is a powerful tool for experimental design is used to optimize the machining parameters for effective machining of the work piece. L9 orthogonal array experimental design method as well as analysis of variance (ANOVA) is used to analyse the influence of machining parameters on MRR & Machining Time. Two different grade of aluminium alloy i.e. AL 6063 & AL 6068 were machined with input parameters of depth of cut and speed. The output parameters are MRR and machining time. Based on the results empirical equations are formed and optimized results are validation .The optimal results are recommended to manufacturing industries.

Evaluation and Performance Improvement of Environmentally Friendly Sustainable Turning of 6063 Aluminum Alloy in Dry Conditions Using Grey Relational Analysis

Sustainable machining has gained importance in recent years due to its environmental, economic, and societal implications. Aluminium (Al) 6063 alloy involves turning operation to make it suitable for various applications. The work's novelty is assessing the machining characteristics along with sustainability indicators. This study aims to find the best-turning parameters for machining Al 6063 alloy. The turning parameters considered were cutting speed, feed rate, and depth of cut. A cutting speed of 200 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.25 mm were the best parameter combinations for achieving a good machining response. From the response value of mean grey relational grade (GRG) and analysis of variance (ANOVA), the depth of cut ranks one with 34.38%, which is the most dominating parameter in achieving the sustainable machining of Al 6063 alloy. Through grey relational analysis, optimized machining parameters resulted in a 72.84 percent reduction in carbon emissions, 72.82 percent reduction in energy consumption, 18.58 percent reduction in cutting power, and 6.83 percent reduction in surface roughness considering the initial parameter settings and best machining parameters. The enhancement in total GRG was 0.1702, indicating improvement in the desired responses. As a result of this study, it is clear that appropriate machining parameter selection aids sustainable machining of Al 6063 alloy.

Surface defects formation mechanism of Mg2Si/Al composites by micro cutting

Mg2Si/Al composites have wide application prospects in aerospace and automotive industries. However, many defects often remain on the machined surface after micro cutting. In this paper, the formation mechanism of surface defects of Mg2Si/Al composites by micro cutting was studied by finite element (FE) simulations and machining experiments. Considering the real microstructure characteristics of Mg2Si particles, 2D FE simulations of Mg2Si/Al composites in micro cutting were carried out and then verified by corresponding cutting experiments. The simulation results and experimental data reveal the relationship between damage and deformation behavior of Mg2Si particles and formation mechanism of surface defects. In addition, the effects of cutting parameters such as depth of cutting (DoC), cutting speed and volume fraction of particles on residual defects of machined surfaces were studied. The results show that with lower DoC and higher cutting speed, the residual defects and surface roughness are significantly reduced, especially machining the composites with smaller particle size and lower volume fraction. The research findings provide a theoretical basis for reasonable selection of machining parameters and improving the machinability of Mg2Si/Al.

Scientific Basis for the Substantiation of Process Regulations for the Micro-Cutting of Hardened Gears

Abstract The paper considers scientific principles of technological procedure development for the selection and assignment of parameters of velocity machining by blade tool at gear milling with due regard to the required parameters of the gear surface layer. Optimum micro-cutting conditions are determined when full participation in stock removal of all mill teeth is provided. Technological regulation of selection and setting of machining parameters is spread for gear hobbing of the spur gear wheels with fine-modular and coarse-modular hardness (220...320 HV) and after heat treatment (HRC46...63). Correlation with essential parameters of the surface layer, cutting depth, feed, speed and radius of rounding of the cutting edge of milling cutters’ teeth is established. It is scientifically grounded, tested, and proved at what modes of cutting, the radius of rounding of cutting edge of the cutting tool is made metal removal at oncoming and passing high-speed micro milling. The process of determining of minimal sliding angle, or maximum value (without the use of coolant and with coolant), in which there is no micro-cutting process taking into account variation of variable parameters of cutting conditions and geometrical parameters of hobbing cutters is modelled. For the first time, we ascertained the relation between the sliding angle and the maximum contact angle of the mill tooth with the work surface, which allowed determine the efficiency of the shaping process through coefficient (without coolant and with coolant).

Additive manufacturing: Application and validation of machine learning-based process-structure-property linkages in Ti-6Al-4V

In the field of metal additive manufacturing (AM), various processes and heat treatments can yield unique grain morphologies, thereby influencing material properties and machining behavior. In this study, a novel workflow using a machine learning-based approach that combines statistical descriptors of textured AM-process induced microstructure, cutting force model (as a material response), and a data-mining method is established. It is proven to be a valid method for creating process-structure-property linkages for metal AM. This study focuses on two highly varied metal AM processes: Powder bed fusion (PBF, e.g., laser PBF and electron beam PBF) and directed energy deposition (DED, e.g., wire-fed plasma-directed energy deposition). The study also accounted for the effects of post-AM heat treatment and build orientation. It was found that the accuracy of material behavior predictions is highly correlated with AM processing conditions, building orientations, and machining conditions. Specifically, while initially applying PBF training data to DED samples resulted in a 15% root mean square prediction error, this error was subsequently reduced to <1% through cross-training using combined microstructure training data sets. This discrepancy could be attributed to the significantly different thermal cycling conditions in L-PBF and DED, which resulted in highly varied textured microstructures. Residual stresses generated during AM processing and the selection of machining parameters exert the highest impact on the machining behavior. The implications of these findings extend to the use of statistically descriptive microstructures for various AM processing conditions and build orientations in computational methods and other machining learning approaches.

Study on wavy-edge plunge milling cutter with chip split and cutting load reduction function

The plunge milling feeds along the cutter axis, and the cutting load is concentrated in the cutter axis with greater rigidity, which is a rough machining process that allows greater machining processing parameters. At present, due to the cutting performance of plunge milling cutter, its efficiency advantage is difficult to give full play, and the application scope is very limited. For this reason, this paper introduces the chip split principle of wavy edge into plunge milling, and develop a high-performance plunge milling cutter that can reduce the cutting force and cutting vibration. The mechanism for wavy edge to reduce load is revealed from the perspective of the geometric characteristics of cutting layer, and the cutter structure of radial dislocation matching axial dislocation is proposed. The critical condition for effective chip split is derived, which provides a theoretical basis for cutter design and machining parameter selection. Considering the rough machining characteristics, the model designing, material selection and manufacturing process for the insert blade, cutter head and cutter body of the wavy-edged plunge milling cutter are proposed, and the reasonable value range of its key cutter geometric parameters are clarified. The arranged plunge milling experiment of 40Cr stainless steel shows that the vibration amplitude of wavy edged cutter is reduced by more than 90 % compared with straight edge cutter, and the machining stability is higher. At the same time, the cutting force is reduced by about 50 %, allowing larger feed per tooth.

On discontinuous stress and strain evolutions in machining of dissimilar laser cladded workpiece

During subsequent machining of laser cladded workpiece, the stress transferred in depth direction is different with conventional cast workpiece due to the geometrical discontinuity, resulting in the stress difference and strain discontinuity at the interface between the cladding layer and substrate. Elastic and plastic deformation will occur in the cladding layer and substrate, respectively, which ultimately reduces the service life of the cladded workpiece. In this study, the stress and strain evolution in subsequent machining of laser cladded workpiece was investigated using an orthogonal cutting model by finite element method. A dimensionless thickness ratio between uncut chip thickness and cladding layer thickness was set and the maximum thickness ratio without equivalent plastic strain (PEEQ) discontinuity at the interface was determined. In addition, the results indicated that the maximum thickness ratio first decreased and then increased with the increase of cutting speed, while increases with the increase of the yield strength of substrate. The synergetic effects of thickness ratio, cutting speed and yield strength of substrate on the strain discontinuity at the interface were also investigated based on Taguchi method. The optimal combination of parameters is determined based on the regression model and divided into areas with considering machining efficiency as well. The map of cutting configurations facilitates the selection of proper machining parameters in order to estimate the strain discontinuity in machining laser cladded workpiece, ensuring a better service life of the workpiece.

Real-time reliability analysis of micro-milling processes considering the effects of tool wear

The development of micro-milling technology has promoted the high-precision machining of complex and small-size parts in modern manufacturing. Accurate cutting force control and reasonable machining parameters selection are conducive to the sustainable machining of micro-milling. In this work, a micro-milling mechanical model considering the change of tool wear, tool runout, and chip separation status with cutting time is proposed with the improved Gated Recurrent Unit with Multi-objective Dandelion Optimizer and mathematical modeling. The modeling accuracy is verified by experiments on Al6061. Combined with the established framework and the practical physical information, the posterior distribution of process parameters is inverted using the Bayesian updating method. Finally, the reliability calculation of machining system parameters is efficiently realized utilizing the posterior information and the High-dimensional Model Representation with the Stochastic configuration network method. The influence degree of the change of cutting parameters on the extreme value of micro-milling force is evaluated. The research results can provide analytical methods and theoretical guidance to ensure safe processing.

Multi-objective optimization for improving machining benefit based on WOA-BBPN and a Deep Double Q-Network

To scientifically and reasonably set and adjust the machining parameters, and improve the machining benefit of the batch machining process including machining quality, performance and efficiency, this paper proposes a low cost and high accurate multi-objective optimization framework by integrating intelligent technologies from three perspectives of physical experiment design, objective function construction and multi-objective optimization. In this framework, multilayer design (MLD) which is a computer experiment design method with low redundancy, good uniformity and filling is applied to the machining physics experiment design to reduce the experiment cost. Whale optimization algorithm (WOA) optimized backpropagation neural network (BBPN) is utilized to construct the optimization objective functions to improve the accuracy of functions. And Deep Double Q-Network (DDQN) is employed to achieve multi-objective optimization of machining quality, efficiency, and performance. The effectiveness of MLD, WOA-BBPN and DDQN in multi-objective optimization of machining process are verified by comparisons of common modeling and multi-objective optimization algorithms based on the milling data. These techniques provide a good guidance for the selection of machining parameters to improve the machining benefit and demonstrates some advantages. The research of this paper also offers a good example of applying intelligent technology in machining.

Tool–workpiece separation characteristic and surface generation in ultrasonic assisted milling

Ultrasonic assisted machining is an eco-friendly surface texture fabrication method with low machining cost, high-accuracy precision, and efficiency. This study analyzes the surface texture formation in feed direction ultrasonic vibration assisted milling (FUVAM) and elliptical ultrasonic vibration assisted milling (EUVAM). The trajectories of the single and adjacent tool tips in FUVAM are analyzed. Furthermore, the tool–workpiece separation mechanism in EUVAM is discussed. Moreover, a 3D surface topography model of EUVAM is applied to study the influences of machining parameters on the ultrasonic machining topography. The experiments of conventional milling (CM) and EUVAM are carried out and different types of surface textures are successfully fabricated on Ti-6Al-4V using EUVAM. The results of the response surface indicate that the surface roughness shows an increasing trend with higher spindle speed, but decreases with improved feed speed and cutting depth. Compared with CM, the chips generated in EUVAM have the characteristics of small width and low bending degree. This work is expected to provide some guidance for the selection of machining and vibration parameters during ultrasonic assisted milling.

Assessment of sustainability of machining Ti-6Al-4V under cryogenic condition using energy map approach

Sustainability of a manufacturing system is of significant importance to its overall efficiency and output. The issue becomes more pertinent during the machining of hard to cut titanium alloys which draws excessive amount of energy. Process map are effective tools that readily provides machinability information of materials of manufacturing importance to process planners. Titanium alloys, owing to their usage in high volumes, are well-mapped but only under dry conditions. The current research focuses on the development of energy consumption map of titanium alloy Ti-6Al-4V employing single point turning under cryogenic conditions. Cryogenic cooling is known for its convenience for green manufacturing as opposed to conventional coolant. Cutting speed and feed rate are used as variable machining parameters over a wide spectrum using full factorial design of experimentation. Energy consumption is represented by specific cutting energy due to its machine-tool independent nature. The map region was demarcated into low, moderate and high energy zones. Analysis of the developed novel map revealed underlying mechanics of machining and provided deeper insight into the factors effecting energy consumption. Energy consumption was found to be more dependent on cutting speed than feed rate. Tool chip contact length was found to decrease due to the reduction of slip region under cryogenic conditions. Analysis of chip morphology revealed that chip shear angle and compression ratio decreased with increase in cutting speed resulting in higher energy consumption. The significance of cryogenic map is also underscored in its utility on the shop floor for selection of optimum machining parameters for higher productivity in addition to its sustainable and environmental friendly nature. In comparison with dry energy map, a reduction in energy up to 16% is observed which highlights the sustainability of cryogenic energy map. The utility of cryogenic energy map can also be ascertained from the fact that productivity can be increased by 156% in terms of material removal rate by careful selection of appropriate machining parameters. Overall, the developed cryogenic map is a vital asset of manufacturing systems as it collectively serves the purpose of sustainability and cleaner production.

Research on three-dimensional ultrasonic vibration-assisted turning cutting force

Three-dimensional ultrasonic vibration-assisted turning with a dual-frequency drive can improve the machinability of workpieces. The output path of the vibrating tool nose in this method is a complex Lissajous closed curve, which results in the change of the instantaneous cutting force generated by the vibrating tool at different times. Therefore, when evaluating the instantaneous cutting force of this method, it is necessary to determine the equivalent plane at the cutting point at different times, and the orthogonal transient cutting force model is established in the equivalent plane. The effects of ultrasonic vibration and machining parameters on the cutting force generated by three-dimensional ultrasonic vibration-assisted turning are analyzed. It shows that the reasonable selection of ultrasonic vibration displacement and machining parameters can obtain lower cutting force. Among them, the vibration displacement in the cutting speed direction and the cutting depth have the largest contribution to the cutting force. Finally, a single-factor comparison experiment was carried out for different turning methods. It shows that when the cutting parameters are changed, the development trends of the cutting forces generated by the four turning methods are roughly the same. It also shows that the vibrating tool with a complex Lissajous curve trajectory can effectively weaken the cutting force. The research results of the manuscript provide a theoretical basis and guidance for the development of three-dimensional ultrasonic vibration-assisted cutting methods.

Improved milling stability analysis for chatter-free machining parameters planning using a multi-fidelity surrogate model and transfer learning with limited experimental data

Decision-making on chatter-free machining parameters is essential for process planning since chatter significantly affects production quality and efficiency. Stability lobe diagram (SLD) is commonly used for selecting chatter-free machining parameters, but its analytical prediction often has poor accuracy and experiment-based prediction is time-consuming. This paper proposes a multi-fidelity (MF) surrogate model and transfer learning-based method to improve the milling stability analysis. Firstly, an analytical stability model is constructed to predict low-fidelity (LF) SLDs for key combinations of radial cutting width (a e) and feed rate per tooth (ft ). A few spindle speeds (n s) are selected from each key LF SLD to detect high-fidelity (HF) stability limits (ap lim) through milling experiments. Subsequently, sufficient LF and limited HF combinations of n s, a e, ft , and ap lim are taken to construct additive scaling function-based MF stability models. Predicted MF combinations of n s, a e, ft , and ap lim are combined with limited HF combinations to construct more accurate stability models through transfer learning. Then, a neural network is ultimately trained to predict ap lim values for arbitrary combinations of n s, a e, and ft . A detailed experimental validation indicates that the proposed method can provide more accurate lobe boundaries for machining parameters selection by introducing fewer experimental samples.

Overview of Machinability of Titanium Alloy (Ti6Al4V) and Selection of Machining Parameters

Abstract Machining of titanium alloy Ti6Al4V is a challenging task for the industry; however, there are some solutions to overcome these difficulties. One of those is optimizing the machining parameters. Machining of Ti6Al4V made by additive manufacturing is an emerging future and is even more difficult when comparing to standard Ti6Al4V alloy. There is lot of invention on Ti6AL4V 3D printed samples, but influence of machining post-printing is lacking. In additive manufacturing of Ti6Al4V alloy, it is necessary to make a finishing operation to improve the surface quality and to ensure precise geometry tolerances. During this process, it may affect the workpiece properties such as microhardness, microstructure, internal defect distribution, internal stresses. During printing there are lots of stresses created, heat treatment is done to normalize the parts. Machining (using milling machine) also causes internal stresses which can damage the surface and part itself. Optimisation of machining parameters and printing parameters can solve this issue. This study gives an overview of selection of machining parameters by considering all the previous relevant research.

Finite Element Analysis and Experimental Investigation of Tool Chatter in Ultra-Precision Diamond Micro-Milling Process

Ultra-precision milling with an aerostatic high-speed spindle and a single-crystal diamond micro-tool is promising for the fabrication of miniaturized complex parts. While tool chatter occurring in milling processes has a substantial effect on the machined surface formation, a fundamental understanding of the tool chatter behavior in ultra-precision milling is essentially required for achieving an ultra-high surface finish. In this paper, through a combination of finite element simulations and experimental validations, the machining mechanisms of the ultra-precision diamond micro-milling of a copper workpiece are revealed, in which the tool chatter behavior and its correlation with the machined surface morphology are emphatically studied. Specifically, the correlation between the tool chatter and the transient depth of cut is analytical established. Subsequently, we first establish a finite element model of diamond micro-milling with the consideration of milling tool deformation and material removal to reveal the tool chatter behavior during the milling process. Furthermore, a corresponding micro-milling experiment is also conducted to validate the simulation results in terms of the milling force, chip profile and morphology of machined surfaces. Finally, the effect of spindle speed on the milling process in particular tool chatter is investigated by FE simulations, through which a linear relationship between the spindle speed and microscopic roughness Rz of a machined surface is obtained. The research findings provide a theoretical basis for understanding the origination of tool chatter in the diamond micro-milling process, as well as the rational selection of machining parameters for suppressing the tool chatter.

Multiphysics Numerical Simulation of the Transient Process in Electrochemical Machining

To fully understand the electrochemical machining profile, a multi-physics coupling simulation model including flow-electric-temperature-structure field were established to analyze the corrosion process of the anode material and the change trend of temperature,hydrogen volume fraction, electrolyte conductivity and current density in the processing gap.The analysis results show that the temperature and hydrogen content gradually increase along the process direction.The current density and material removal showed a parabolic trend of the upper opening and the lower opening, respectively.The simulation of the different physical field changes in the electrochemical machining blade profile can not only better understand the complex physical phenomena in the machining, but also provide a theoretical basis for the selection of actual electrochemical machining parameters.

Selection of Robotic Machining Parameters with Pneumatic Feed Force Progression

The subject matter of the above paper presents part of the research carried out as part of the robotization of the manufacturing processes of aircraft engine components. The paper concerns robotic deburring of the V2500 diffuser’s sharp edges. The diffuser is a precision casting characterized by a slight variation in the geometry of the workpiece depending on the accuracy of the casting molds and the phenomenon of shrinkage during solidification. Due to the small degree of deburring and thus low cutting forces, robotic machining with the FDB150 tool was used. This tool is characterized by compliance with adjustable force, which enables machining of workpieces with a randomly changing shape. The authors of the paper propose a procedure for carrying out work allowing for the selection of suboptimal process parameters. In the analyzed case, these parameters are the speed of movement of the characteristic point of the tool (TCP) and the tool/workpiece contact force. The proposed procedure for determining the parameters of the force and speed of movement allowed for indicating a set of parameters ensuring the performance of the product in accordance with the requirements defined in the documentation. The proposed solution is of an engineering nature and is not a classic search for the extreme of a function from the point of view of the adopted criteria (quality indicators). Its advantage is simplicity, which is very important from the point of view of an industrial application.

High-speed face milling of AZ91 Mg alloy: Surface integrity investigations

Magnesium (Mg) alloys are popular in the aerospace and automotive sector owing to their light-weight aspects. Amongst various Mg alloys, AZ91 alloy behaviour under machining has been trending and needs to be completely explored. The selection of optimal machining parameters is an important decision making process to achieve highest quality along with reduced cost and time. In this regard, this article describes experimental investigations to evaluate the performance of face milling operations on the surface characteristics of AZ91 magnesium alloy. The experiments were carried out with uncoated and PVD (Physical Vapour Deposition) coated carbide inserts at three levels of cutting speed (500, 700 and 900 m/min), feed rate (0.1, 0.2 and 0.3 mm/teeth) and depth of cut (0.5, 1.0 and 1.5 mm) under dry machining conditions. Major surface integrity indicators, such as roughness, hardness, residual stresses and microstructure are analysed. Chip morphology is also analysed and the correlation between chips and machined surface roughness is established. Face milling operation significantly improved surface roughness and microhardness of this alloy. Roughness improvement up to 85% (0.067 μm) and hardness improvement up to 33% (91.8 HV) is observed from the use of uncoated carbide inserts. Whereas, from PVD coated inserts, roughness improvement up to 81% (0.083 μm) and hardness improvement up to 60% (111.2 HV) is achieved. A similarity in behaviour between the two types of insert conditions are observed with increase in roughness from feed increase and decrease in hardness from cutting speed increase. Microstructural analysis showed PVD coated insert conditions producing surface with no defects, when compared to the crack observed in the surface from the use of uncoated carbide inserts. Marginally higher compressive residual stresses are detected at the surfaces from use of the uncoated inserts. Overall, due to no surface defect and the significant improvement in hardness and roughness from the PVD coated inserts, they are recommended for use in face milling operation for the cutting conditions investigated in this study.

Surface prediction in laser-assisted grinding process considering temperature-dependent mechanical properties of zirconia ceramic

Zirconia ceramics are widely applied in many advanced applications in various fields, including biology, aerospace, manufacturing, owing to its outstanding biocompatibility, excellent wear and chemical resistance, and improved toughness over traditional ceramics. Laser-assisted grinding (LAG) is considered as a potential hybrid machining process for low-damage machining of hard and brittle materials (HBMs). In the meanwhile, surface topography and roughness are important indicators to evaluate the surface integrity during the grinding operation. In this study, a grinding wheel model considering stochastic process is established. The thermal distribution of laser irradiation is calculated, and thus the theory for calculation of the critical depth of plastic region (dc) under the influence of laser, which also considers the changes of temperature-dependent mechanical properties is described systematically. Based on the aforementioned models, the predictive model of the surface topography for LAG process was proposed. Subsequently, a series of experiments were designed and implemented. The results indicate that the experimental results have a good agreement with that of the simulated surface topography. The error rates of surface roughness Rz and Ra between the simulated and experimental results are both <8 %. In the meantime, as the laser power increases, plastic mode starts to dominate material removal, and thus the surface integrity is obviously improved. The depth of median crack is also significantly reduced, which can help to reduce the sub-surface damage. Lastly, average power density (Iave) is defined to evaluate the selection of machining parameters.

Development and testing of a wireless multi-axis toolholder dynamometer for milling and drilling process

The cutting force provides important information such as tool condition, chips information, and machining parameters selection. Thus, many researchers have proposed some methods for cutting force monitoring. However, most of them are only applied in the laboratory due to lower flexibility and complex installation. In this paper, a split multi-axis toolholder dynamometer is proposed and developed, which can equip different types of machining spindle and fit for multi-size tool. It consists of six capacitive sensors that can sense four deformations in vertical direction and two deformations in circumferential direction. Meanwhile, the distance between two plates in the vertical and circumferential capacitive sensors was adjusted separately. An acquisition and wireless transmission unit was designed to detect the capacitive variation and then transfer data to upper computer. Finally, a standard tool holder and a force sensing unit, a bottom socket, an acquisition and wireless transmission unit, a lithium battery, a cover were installed and debugged as a whole system. To validate its static and dynamic characteristics, various tests are conducted. The result demonstrates that the toolholder dynamometer can accurately and reliably monitor four-axis cutting force in the milling and drilling operations.