On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

The commonly used trial-and-error method of biodegradable Zn alloys is costly and blindness.In this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is Zn-0.4Mn-0.4Li-0.05Mg.Through tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion rate.Finally,influences of the selected features on UTS and CR are discussed in detail.The combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications

Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over time.This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy,ZX10.Utilizing rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal treatment.Employing Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion resistance.Additionally,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated variables.Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)Mg_(6)Zn_(3)phase in corrosion behavior.This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases.This thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.

High-throughput studies and machine learning for design of β titanium alloys with optimum properties

Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.

Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.

Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

The aim of this work is to predict,for the first time,the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models,random forest(RF)and artificial neural network(ANN).With that purpose,a ZK30 magnesium alloy was friction stir processed(FSP)using three different severe conditions to obtain fine grain microstructures(with average grain sizes between 2 and 3μm)prone to extensive superplastic response.The three friction stir processed samples clearly deformed by grain boundary sliding(GBS)deformation mechanism at high temperatures.The maximum elongations to failure,well over 400% at high strain rate of 10^(-2)s^(-1),were reached at 400℃ in the material with coarsest grain size of 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,the superplastic response decreased at 350℃ and 400℃ due to thermal instabilities and grain coarsening,which makes it difficult to assess the operative deformation mechanism at such temperatures.This work highlights that the machine learning models considered,especially the ANN model with higher accuracy in predicting flow stress values,allow determining adequately the superplastic creep behavior including other possible grain size scenarios.

Powder mixed electrochemical discharge process for micro machining of C103 niobium alloy

This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes are involved simultaneously i.e.spark erosion,chemical etching,and abrasive grinding for removal of material while the classical electrochemical discharge machining process involves double actions i.e.spark erosion,and chemical etching.The powder-mixed electrolyte process resulted in rapid material removal along with a better surface finish as compared to the classical microelectrochemical discharge machining(MECDM).Further,the results are optimized through a multiobjective optimization approach and study of the surface topography of the hole wall surface obtained at optimized parameters.In the selected range of experimental parameters,PMECDM shows a higher material removal rate(MRR) and lower surface roughness(R_(a))(MRR:2.8 mg/min and R_(a) of 0.61 μm) as compared to the MECDM process(MRR:2.01 mg/min and corresponding Raof 1.11 μm).A detailed analysis of the results is presented in this paper.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.

A machine learning approach for accelerated design of magnesium alloys. Part A:Alloy data and property space

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.

Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.

Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning

In the present work,we have employed machine learning(ML)techniques to evaluate ductile-brittle(DB)behaviors in intermetallic compounds(IMCs)which can form magnesium(Mg)alloys.This procedure was mainly conducted by a proxy-based method,where the ratio of shear(G)/bulk(B)moduli was used as a proxy to identify whether the compound is ductile or brittle.Starting from compounds information(composition and crystal structure)and their moduli,as found in open databases(AFLOW),ML-based models were built,and those models were used to predict the moduli in other compounds,and accordingly,to foresee the ductile-brittle behaviors of these new compounds.The results reached in the present work showed that the built models can effectively catch the elastic moduli of new compounds.This was confirmed through moduli calculations done by density functional theory(DFT)on some compounds,where the DFT calculations were consistent with the ML prediction.A further confirmation on the reliability of the built ML models was considered through relating between the DB behavior in MgBe_(13) and MgPd_(2),as evaluated by the ML-predicted moduli,and the nature of chemical bonding in these two compounds,which in turn,was investigated by the charge density distribution(CDD)and electron localization function(ELF)obtained by DFT methodology.The ML-evaluated DB behaviors of the two compounds was also consistent with the DFT calculations of CDD and ELF.These findings and confirmations gave legitimacy to the built model to be employed in further prediction processes.Indeed,as examples,the DB characteristics were investigated in IMCs that might from in three Mg alloy series,involving AZ,ZX and WE.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Constitutive Modeling for Ti-6Al-4V Alloy Machining Based on the SHPB Tests and Simulation

A constitutive model is critical for the prediction accuracy of a metal cutting simulation. The highest strain rate involved in the cutting process can be in the range of 104-106 s 1. Flow stresses at high strain rates are close to that of cutting are difficult to test via experiments. Split Hopkinson compression bar （SHPB） technology is used to study the deformation behavior of Ti-6Al-4V alloy at strain rates of 10 -4-10 4s- 1. The Johnson Cook （JC） model was applied to characterize the flow stresses of the SHPB tests at various conditions. The parameters of the JC model are optimized by using a genetic algorithm technology. The JC plastic model and the energy density-based ductile failure criteria are adopted in the proposed SHPB finite element simulation model. The simulated flow stresses and the failure characteristics, such as the cracks along the adiabatic shear bands agree well with the experimental results. Afterwards, the SHPB simulation is used to simulate higher strain rate（approximately 3 × 10 4 s -1） conditions by minimizing the size of the specimen. The JC model parameters covering higher strain rate conditions which are close to the deformation condition in cutting were calculated based on the flow stresses obtained by using the SHPB tests （10 -4 - 10 4 s- 1） and simulation （up to 3 × 10 4 s - 1）. The cutting simulation using the constitutive parameters is validated by the measured forces and chip morphology. The constitutive model and parameters for high strain rate conditions that are identical to those of cutting were obtained based on the SHPB tests and simulation.

Review on non-conventional machining of shape memory alloys

Shape memory alloys （SMAs） are the developing advanced materials due to their versatile specific properties such as pseudoelasticity, shape memory effect （SME）, biocompatibility, high specific strength, high corrosion resistance, high wear resistance and high anti-fatigue property. Therefore, the SMAs are used in many applications such as aerospace, medical and automobile. However, the conventional machining of SMAs causes serious tool wear, time consuming and less dimensional deformity due to severe strain hardening and pseudoelasticity. These materials can be machined using non-conventional methods such as laser machining, water jet machining （WJM） and electrochemical machining （ECM）, but these processes are limited to complexity and mechanical properties of the component. Electrical discharge machining （EDM） and wire EDM （WEDM） show high capability to machine SMAs of complex shapes with precise dimensions. The aim of this work is to present the consolidated references on the machining of SMAs using EDM and WEDM and subsequently identify the research gaps. In support to these research gaps, this work has also evolved the future research directions.

Dissolution Characteristics of New Titanium Alloys in Electrochemical Machining

We focus on the electrochemical dissolution characteristics of new titanium alloys such as near-αtitanium alloy Ti60,α+βtitanium alloy TC4andβtitanium alloy Ti40 which are often used for aerospace industry.The experiments are carried out by electrochemical machining tool,and the surface morphology of the specimens is observed by the scanning electron microscope(SEM)and three-dimensional video microscope(DVM).The appropriate electrolyte is selected and the relationships between surface roughness and current density are achieved.The results show that the single-phase titanium alloy Ti40 has a better surface roughness after ECM compared with theα+βtitanium alloy TC4 and the near-αtitanium alloy Ti60.The best surface roughness is Ra 0.28μm when the current density is 75A/cm2.Furthermore,the surface roughness of the near-αtitanium alloy Ti60 is the most sensitive with the current density because of the different electrochemical equivalents of substitutional elements and larger grains than TC4.Finally,the suitable current density for each titanium alloy is achieved.

Comparison of electrochemical behaviors of Ti-5Al-2Sn-4Zr-4Mo-2Cr-1Fe and Ti-6Al-4V titanium alloys in NaNO_(3) solution

The Ti-5Al-2Sn-4Zr-4Mo-2Cr-1Fe(β-CEZ)alloy is considered as a potential structural material in the aviation industry due to its outstanding strength and corrosion resistance.Electrochemical machining(ECM)is an efficient and low-cost technology for manufacturing theβ-CEZ alloy.In ECM,the machining parameter selection and tool design are based on the electrochemical dissolution behavior of the materials.In this study,the electrochemical dissolution behaviors of theβ-CEZ and Ti-6Al-4V(TC4)alloys in NaNO3solution are discussed.The open circuit potential(OCP),Tafel polarization,potentiodynamic polarization,electrochemical impedance spectroscopy(EIS),and current efficiency curves of theβ-CEZ and TC4 alloys are analyzed.The results show that,compared to the TC4 alloy,the passivation film structure is denser and the charge transfer resistance in the dissolution process is greater for theβ-CEZ alloy.Moreover,the dissolved surface morphology of the two titanium-based alloys under different current densities are analyzed.Under low current densities,theβ-CEZ alloy surface comprises dissolution pits and dissolved products,while the TC4 alloy surface comprises a porous honeycomb structure.Under high current densities,the surface waviness of both the alloys improves and the TC4 alloy surface is flatter and smoother than theβ-CEZ alloy surface.Finally,the electrochemical dissolution models ofβ-CEZ and TC4 alloys are proposed.

Micro electrical discharge machining of small hole in TC4 alloy

Aiming at machining deeply small holes in TC4 alloy,a series of experiments were carried out on a self-developed multi-axis micro electrical discharge machining(micro-EDM)machine tool.To improve machining efficiency and decrease relative wear of electrode in machining deeply small hole in TC4 alloy,many factors in micro-EDM,such as polarity,electrical parameters and supplying ways of working fluid were studied.Experimental results show that positive polarity machining is far superior to negative polarity machining;it is more optimal when open-circuit voltage,pulse width and pulse interval are 130 V,5μs and 15μs respectively on the self developed multi-axis micro-EDM machine tool;when flushing method is applied in micro-EDM,the machining efficiency is higher and relative wear of electrode is smaller.

Microstructure of fast tool servo machining on copper alloy

The development of the fast tool servo (FTS) for precision machining was investigated.The micron machining performance of a piezoelectric-assisted FTS on copper alloy was evaluated.The results indicate that the quality of the microstructure depends mainly on two important factors:the cutting speed (or spindle speed) and the driving frequency of the FTS.The excessive driving frequency increases the formation of burrs.The effect of the clearance angle of the diamond tool on the microstructure machining precision was also investigated.

Electrical discharge machining of 6061 aluminium alloy

The wire electrical discharge machining（EDM） of 6061 aluminium alloy in terms of material removal rate,kerf/slit width,surface finish and wear of electrode wire for different pulse on time and wire tension was studied.Eight experiments were carried out in a wire EDM machine by varying pulse on time and wire tension.It is found that the material removal rate increases with the increase of pulse on time though the wire tension does not affect the material removal rate.It seems that the higher wire tension facilitates steady machining process,which generates low wear in wire electrode and better surface finish.The surface roughness does not change notably with the variation of pulse on time.The appearance of the machined surfaces is very similar under all the machining conditions.The machined surface contains solidified molten material,splash of materials and blisters.The increase of the pulse on time increases the wear of wire electrode due to the increase of heat input.The wear of wire electrode generates tapered slot which has higher kerf width at top side than that at bottom side.The higher electrode wear introduces higher taper.