Experimental Study And Modeling Of Mechanical Micro-machining Of Particle Reinforced Heterogeneous Materials

This study focuses on developing explicit analytical and numerical process models for mechanical micro-machining of heterogeneous materials. These models are used to select suitable process parameters for preparing and micro-machining of these advanced materials. The material system studied in this research is Magnesium Metal Matrix Composites (Mg-MMCs) reinforced with nano-sized and micro-sized silicon carbide (SiC) particles. This research is motivated by increasing demands of miniaturized components with high mechanical performance in various industries. Mg-MMCs become one of the best candidates due to its light weight, high strength, and high creep/wear resistance. However, the improved strength and abrasive nature of the reinforcements bring great challenges for the subsequent micro-machining process. Systematic experimental investigations on the machinability of Mg-MMCs reinforced with SiC nano-particles have been conducted. The nanocomposites containing 5 Vol.%, 10 Vol.% and 15 Vol.% reinforcements, as well as pure magnesium, are studied by using the Design of Experiment (DOE) method. Cutting forces, surface morphology and surface roughness are characterized to understand the machinability of the four materials. Based on response surface methodology (RSM) design, experimental models and related contour plots have been developed to build a connection between different materials properties and cutting parameters. Those models can be used to predict the cutting force, the surface roughness, and then optimize the machining process. An analytical cutting force model has been developed to predict cutting forces of MgMMCs reinforced with nano-sized SiC particles in the micro-milling process. This model is iv different from previous ones by encompassing the behaviors of reinforcement nanoparticles in three cutting scenarios, i.e., shearing, ploughing and elastic recovery. By using the enhanced yield strength in the cutting force model, three major strengthening factors are incorporated, including load-bearing effect, enhanced dislocation density strengthening effect and Orowan strengthening effect. In this way, the particle size and volume fraction, as significant factors affecting the cutting forces, are explicitly considered. In order to validate the model, various cutting conditions using different size end mills (100 µm and 1 mm dia.) have been conducted on Mg-MMCs with volume fraction from 0 (pure magnesium) to 15 Vol.%. The simulated cutting forces show a good agreement with the experimental data. The proposed model can predict the major force amplitude variations and force profile changes as functions of the nanoparticles’ volume fraction. Next, a systematic evaluation of six ductile fracture models has been conducted to identify the most suitable fracture criterion for micro-scale cutting simulations. The evaluated fracture models include constant fracture strain, Johnson-Cook, Johnson-Cook coupling criterion, Wilkins, modified Cockcroft-Latham, and Bao-Wierzbicki fracture criterion. By means of a user material subroutine (VUMAT), these fracture models are implemented into a Finite Element (FE) orthogonal cutting model in ABAQUS/Explicit platform. The local parameters (stress, strain, fracture factor, velocity fields) and global variables (chip morphology, cutting forces, temperature, shear angle, and machined surface integrity) are evaluated. Results indicate that by coupling with the damage evolution, the capability of Johnson-Cook and Bao-Wierzbicki can be further extended to predict accurate chip morphology. Bao-Wierzbiki-based coupling model provides the best simulation results in this study. v The micro-cutting performance of MMCs materials has also been studied by using FE modeling method. A 2-D FE micro-cutting model has been constructed. Firstly, homogenized material properties are employed to evaluate the effect of particles’ volume fraction. Secondly, micro-structures of the two-phase material are modeled in FE cutting models. The effects of the existing micro-sized and nano-sized ceramic particles on micro-cutting performance are carefully evaluated in two case studies. Results show that by using the homogenized material properties based on Johnson-Cook plasticity and fracture model with damage evolution, the micro-cutting performance of nano-reinforced Mg-MMCs can be predicted. Crack generation for SiC particle reinforced MMCs is different from their homogeneous counterparts; the effect of micro-sized particles is different from the one of nano-sized particles. In summary, through this research, a better understanding of the unique cutting mechanism for particle reinforced heterogeneous materials has been obtained. The effect of reinforcements on micro-cutting performance is obtained, which will help material engineers tailor suitable material properties for special mechanical design, associated manufacturing method and application needs. Moreover, the proposed analytical and numerical models provide a guideline to optimize process parameters for preparing and micro-machining of heterogeneous MMCs materials. This will eventually facilitate the automation of MMCs’ machining process and realize high-efficiency, high-quality, and low-cost manufacturing of composite materials

Grain refinement mechanism of nickel-based superalloy by severe plastic deformation - Mechanical machining case

© 2019 Acta Materialia Inc. This paper studied the formation mechanism of white layer of a next generation nickel-based superalloy formed under severe plastic deformation induced by a mechanical material removal process. A graded microstructure of the white layer in the nickel-based superalloy has been revealed for the first time, which is composed of (i) a “dynamic recrystallisation” layer formed by nanocrystalline (∼200 nm) grains at the vicinity of the surface and (ii) a “dynamic recovery” layer with subgrain microstructures extending further into the subsurface. The mechanism of surface grain refinement was identified based on the results obtained via crystallographic and chemical analysis, as well as in-situ micro-mechanics experiments in the scanning electron microscope. It is found that in the top surface layer not only grain refinement but also the γ′ phase dissolution occurs, changing drastically from the bulk material. Furthermore, it is shown how the high plastic strain and cutting temperature along the subsurface causes grain refinement in the white layer and grain elongation in the subsurface. The γ′ precipitates in the recrystallisation layer are dissolved during the machining process, while the ultra-high cooling rate suppresses the further precipitation of this phase, resulting in the supersaturation of γ grains or minimized γ′ precipitates in the top surface layer. Hence, the grain refinement does not result in an increase of mechanical stiffness but a deterioration of mechanical properties due to the dissolution of the strengthening phase γ’, which leads to a lower strength and increased ductility. Machining is generally treated as a cold-working process. However, according to our findings hot-working with dynamic recrystallisation and recovery, as well as phase evolution, occurs in the white layer of nickel-based superalloys

PREDICTION OF SUBSURFACE DAMAGE DURING MACHINING NICKEL-BASED SUPERALLOYS

Nickel-based superalloys are widely utilized in hostile environments such as jet engines and gas turbines due to their high resistance to oxidation, high corrosion resistance, good thermal fatigue-resistance and fracture toughness. Subsurface damage is typically generated during the machining of these materials, and in particular, ã\u27-strengthened nickel-based superalloys. The depth of the subsurface damage is a critical requirement specified by the customer. Therefore, it is critical to predict, measure and control subsurface damage. This research specifically targets the development of a model to predict subsurface damage during the machining of ã\u27-strengthened nickel-based superalloys. To accomplish this, a modified Johnson-Cook model is developed to represent the plasticity behavior of the material using elevated temperature tests. The proposed model integrates a piece-wise method, strain hardening function, thermal sensitivity function, and flow softening function accurately model anomalous strength behavior. Material subroutines are developed for finite element analysis (FEA) simulation and applied with the ABAQUS/Explicit solver. Orthogonal cutting experiments are conducted to verify FEA results. Recrystallization techniques are utilized for estimation of the depth of subsurface damage. By comparing the subsurface damage between experimental and FEM simulation results, a threshold value is established for determining the depth of subsurface damage. A high agreement between FEA simulation and experimental results is observed. From the cutting force aspect, the agreement is more than 90% for unaggressive cutting inputs. On the other hand, the model agreement is slightly lower, 85%, for aggressive machining conditions. This is due to the fact that the severe rake face wear cannot be comprehensively represented in the FEA simulation. In addition, the depth of subsurface damage predicted from the FEA simulations reached an agreement of 95% when compared to experimental findings. Therefore, a subsurface damage model between cutting inputs and depth of subsurface damage has been established based on the results derived from FEA simulations

Evolution of surface grain structure and mechanical properties in orthogonal cutting of titanium alloy

In this study, a mesoscale dislocation simulation method was developed to study the orthogonal cutting of Titanium alloy. The evolution of surface grain structure and its effects on the surface mechanical properties were studied by using two-dimensional climb assisted dislocation dynamics technology. The motions of edge dislocations such as dislocation nucleation, junction, interaction with obstacles and grain boundaries, and annihilation were tracked. The results indicated that the machined surface has a microstructure composed of refined grains. The fine-grains bring appreciable scale effect and a mass of dislocations are piled up in the grain boundaries and persistent slip bands. In particular, dislocation climb can induce a perfect softening effect, but this effect is significantly weakened when grain size is less than 1.65 μm. In addition, a Hall-Petch type relation was predicted according to the arrangement of grain, the range of grain sizes and the distribution of dislocations

The role of microstructural characteristics of additively manufactured Alloy 718 on tool wear in machining

This study aims to provide a fundamental understanding of the role of microstructural characteristics influencing tool wear when machining Alloy 718 fabricated using Powder Bed Fusion (PBF). The effects of preferred crystallographic orientation (texture), shape and distribution of grains, local misorientation, type and amount of precipitates as well as the type, size and amount of abrasive carbides, nitrides and oxides on tool wear are investigated in as-built condition and after the standard solutionising and double-aging treatment. The microstructures of workpiece materials and the surfaces of worn tools were examined using different material characterisation techniques, including Scanning Electron Microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). A dislocation-based approach was used to reveal the cumulative effects of the microstructural characteristics on deformation behaviour and the thermo-mechanical loads on the tools during cutting. The analyses suggest that texture and the extent of material work-hardening prior to the onset of crack formation markedly influence the amount of plastic work and thus heat generation when machining Electron Beam Powder Bed Fusion (EB-PBF) material. The higher heat generation in the cutting zones provokes thermally-induced wear mechanisms like diffusion-dissolution and oxidation. In addition, the larger amount of hard oxide inclusions present in EB-PBF material leads to higher wear by abrasion. In contrast to the prevailing experimental approaches in this field, the present investigation is built on a physics-based framework to understand the fundamental aspects that govern material deformation and heat generation in cutting and, consequently, tool wear mechanisms. This framework can be used for machinability assessment of any alloy manufactured by different additive manufacturing (AM) technologies and for optimising the process-chain, including printing strategies and thermal post-treatments, to improve the machinability of AM alloys by tailoring their microstructure

In-SEM micro-machining reveals the origins of the size effect in the cutting energy

High-precision metal cutting is increasingly relevant in advanced applications. Such precision normally requires a cutting feed in the micron or even sub-micron dimension scale, which raises questions about applicability of concepts developed in industrial scale machining. To address this challenge, we have developed a device to perform linear cutting with force measurement in the vacuum chamber of an electron microscope, which has been utilised to study the cutting process down to 200 nm of the feed and the tool tip radius. The machining experiments carried out in-operando in SEM have shown that the main classical deformation zones of metal cutting: primary, secondary and tertiary shear zones—were preserved even at sub-micron feeds. In-operando observations and subsequent structural analysis in FIB/SEM revealed a number of microstructural peculiarities, such as: a substantial cutting force related to the development of the primary shear zone; dependence of the ternary shear zone thickness on the underlaying grain crystal orientation. Measurement of the cutting forces at deep submicron feeds and cutting tool apex radii has been exploited to discriminate different sources for the size effect on the cutting energy (dependence of the energy on the feed and tool radius). It was observed that typical industrial values of feed and tool radius imposes a size effect determined primarily by geometrical factors, while in a sub-micrometre feed range the contribution of the strain hardening in the primary share zone becomes relevant

Predictive Modeling for Ductile Machining of Brittle Materials

Brittle materials such as silicon, germanium, glass and ceramics are widely used in semiconductor, optical, micro-electronics and various other fields. Traditionally, grinding, polishing and lapping have been employed to achieve high tolerance in surface texture of silicon wafers in semiconductor applications, lenses for optical instruments etc. The conventional machining processes such as single point turning and milling are not conducive to brittle materials as they produce discontinuous chips owing to brittle failure at the shear plane before any tangible plastic flow occurs. In order to improve surface finish on machined brittle materials, ductile regime machining is being extensively studied lately. The process of machining brittle materials where the material is removed by plastic flow, thus leaving a crack free surface is known as ductile-regime machining. Ductile machining of brittle materials can produce surfaces of very high quality comparable with processes such as polishing, lapping etc. The objective of this project is to develop a comprehensive predictive model for ductile machining of brittle materials. The model would predict the critical undeformed chip thickness required to achieve ductile-regime machining. The input to the model includes tool geometry, workpiece material properties and machining process parameters. The fact that the scale of ductile regime machining is very small leads to a number of factors assuming significance which would otherwise be neglected. The effects of tool edge radius, grain size, grain boundaries, crystal orientation etc. are studied so as to make better predictions of forces and hence the critical undeformed chip thickness. The model is validated using a series of experiments with varying materials and cutting conditions. This research would aid in predicting forces and undeformed chip thickness values for micro-machining brittle materials given their material properties and process conditions. The output could be used to machine brittle materials without fracture and hence preserve their surface texture quality. The need for resorting to experimental trial and error is greatly reduced as the critical parameter, namely undeformed chip thickness, is predicted using this approach. This can in turn pave way for brittle materials to be utilized in a variety of applications.Ph.D.Committee Chair: Liang, Steven; Committee Co-Chair: Li, Xiaoping; Committee Member: Garmestani, Hamid; Committee Member: Griffin, Paul; Committee Member: Melkote, Shreyes; Committee Member: Neu, Richar

Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

Exploring Linear Rake Machining In 316L Austenitic Stainless Steel for Microstructure Scale-Refinement, Grain Boundary Engineering, and Surface Modification

Thermo-mechanical processing plays an important role in materials property optimization through microstructure modification, required by demanding modern materials applications. Extreme grain size refinement, grain boundary engineering, and surface modification have been explored to establish enhanced performance properties for numerous metals and alloys in order to meet challenges associated with improving degradation resistance and increasing lifetime in harsh environments. Due to the critical role of austenitic stainless steels, such as 316L, as structural components in harsh environments, e.g. in nuclear power plants, improved degradation resistance is desirable. Linear raking, a novel two dimensional plane strain machining process, has shown promise achieving significant grain size refinement through severe plastic deformation (SPD) and imparting large strains in the surface and near surface regions of the substrate in various metals and alloys, imparting enhanced properties. Here, the effects of linear rake machining on the microstructure and related properties of 316L are investigated systematically for the first time. The controlled variation of linear raking processing parameters in combination with detailed micro-characterization using analytical electron microscopy, x-ray diffraction and associated property measurements enables the determination of the influence of changes in strain and strain rate on the developing deformation microstructure and related properties. Varying the linear raking process parameters, and consequently the strain and strain rate, affects the volume fractions of deformation induced α’-martensite and the degree of grain refinement, to the nanoscale, through SPD in the chips produced. Additionally, linear raking is identified as a way to produce surface modified structures in the specimen substrate surface of 316L, with observations of various degrees of deformation and strain up to a depth of 150m. This research clearly demonstrates that materials property modification can be achieved effectively by linear raking processing, and that resulting surface modified structures provide significant stored energy for recovery and recrystallization. This study provides a fundamental understanding of linear raking as a thermo-mechanical processing technique, which may in the future be capable of creating grain boundary engineered surface modified components for use in harsh environments like those in commercial nuclear power plants