A theoretical assessment of surface defect machining and hot machining of nanocrystalline silicon carbide

In this paper, a newly proposed machining method named “surface defect machining” (SDM) [Wear, 302, 2013 (1124-1135)] was explored for machining of nanocrystalline beta silicon carbide (3C-SiC) at 300K using MD simulation. The results were compared with isothermal high temperature machining at 1200K under the same machining parameters, emulating ductile mode micro laser assisted machining (µ-LAM) and with conventional cutting at 300 K. In the MD simulation, surface defects were generated on the top of the (010) surface of the 3C-SiC work piece prior to cutting, and the workpiece was then cut along the <100> direction using a single point diamond tool at a cutting speed of 10 m/sec. Cutting forces, sub-surface deformation layer depth, temperature in the shear zone, shear plane angle and friction coefficient were used to characterize the response of the workpiece. Simulation results showed that SDM provides a unique advantage of decreased shear plane angle which eases the shearing action. This in turn causes an increased value of average coefficient of friction in contrast to the isothermal cutting (carried at 1200 K) and normal cutting (carried at 300K). The increase of friction coefficient however was found to aid the cutting action of the tool due to an intermittent dropping in the cutting forces, lowering stresses on the cutting tool and reducing operational temperature. Analysis shows that the introduction of surface defects prior to conventional machining can be a viable choice for machining a wide range of ceramics, hard steels and composites compared to hot machining.

Topical Review: The current understanding on the diamond machining of silicon carbide

The Glenn Research Centre of NASA, USA (www.grc.nasa.gov/WWW/SiC/, silicon carbide electronics) is in pursuit of realizing bulk manufacturing of silicon carbide (SiC), specifically by mechanical means. Single point diamond turning (SPDT) technology which employs diamond (the hardest naturally-occurring material realized to date) as a cutting tool to cut a workpiece is a highly productive manufacturing process. However, machining SiC using SPDT is a complex process and, while several experimental and analytical studies presented to date aid in the understanding of several critical processes of machining SiC, the current knowledge on the ductile behaviour of SiC is still sparse. This is due to a number of simultaneously occurring physical phenomena that may take place on multiple length and time scales. For example, nucleation of dislocation can take place at small inclusions that are of a few atoms in size and once nucleated, the interaction of these nucleations can manifest stresses on the micrometre length scales. The understanding of how stresses manifest during fracture in the brittle range, or dislocations/phase transformations in the ductile range, is crucial in understanding the brittle–ductile transition in SiC. Furthermore, there is a need to incorporate an appropriate simulation-based approach in the manufacturing research on SiC, owing primarily to the number of uncertainties in the experimental research that includes wear of the cutting tool, poor controllability of the nano-regime machining scale (effective thickness of cut), and coolant effects (interfacial phenomena between the tool, workpiece/chip and coolant), etc. In this review, these two problems are combined together to posit an improved understanding on the current theoretical knowledge on the SPDT of SiC obtained from molecular dynamics simulation.

Diamond machining of silicon: A review of advances in molecular dynamics simulation

Molecular dynamics (MD) simulation has enhanced our understanding about ductile-regime machining of brittle materials such as silicon and germanium. In particular, MD simulation has helped understand the occurrence of brittle–ductile transition due to the high-pressure phase transformation (HPPT), which induces Herzfeld–Mott transition. In this paper, relevant MD simulation studies in conjunction with experimental studies are reviewed with a focus on (i) the importance of machining variables: undeformed chip thickness, feed rate, depth of cut, geometry of the cutting tool in influencing the state of the deviatoric stresses to cause HPPT in silicon, (ii) the influence of material properties: role of fracture toughness and hardness, crystal structure and anisotropy of the material, and (iii) phenomenological understanding of the wear of diamond cutting tools, which are all non-trivial for cost-effective manufacturing of silicon. The ongoing developmental work on potential energy functions is reviewed to identify opportunities for overcoming the current limitations of MD simulations. Potential research areas relating to how MD simulation might help improve existing manufacturing technologies are identified which may be of particular interest to early stage researchers.

Performance and wear of coated carbide drill in machining of carbon fibre reinforced composite/titanium stack

In this research we investigate the performance of drilling process in carbon fibre reinforced composite (CFC) material, titanium alloy and the hybrid stack of these two materials, using coated carbide drill bit. We study the effect of the process parameters such as the feed rate and speed on the induced forces and torques, also on the wear of drill and surface roughness of the holes. In the composite material the percentage of surface damage in both drilling CFC on its own and drilling in stack form is estimated. Also, the effect of worn drill on the surface damage is identified. In the titanium, the burr formation in stack and non-stack form is investigated. The wear of the drill results in increased forces and torques required for drilling. This increases the surface delaminations substantially at the entrance in drilling of CFC. However, the surface roughness of the holes reduces with the wear of the drill in CFC drilling. Also, the surface delamination and surface roughness of the holes in the CFC whilst drilled in hybrid form reduces significantly. This is despite the increase of the forces and torques required in drilling CFC in stack form. Copyright © 2012 Inderscience Enterprises Ltd.

Investigation of Machining Parameters for Multiple Response Optimization in Microelectrodischarge Milling

This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R a ), maximum peak-to-valley roughness height (R y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R a , R y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R a , R y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min−1, respectively for 4.79 μm s−1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.

Enabling ultra high precision on hard steels using surface defect machining

This paper is an extension to an idea coined during the 13th EUSPEN Conference (P6.23) named "surface defect machining" (SDM). The objective of this work was to demonstrate how a conventional CNC turret lathe can be used to obtain ultra high precision machined surface finish on hard steels without recourse to a sophisticated ultra precision machine tool. An AISI 4340 hard steel (69 HRC) workpiece was machined using a CBN cutting tool with and without SDM. Post-machining measurements by a Form Talysurf and a Scanning Electron Microscope (FEI Quanta 3D) revealed that SDM culminates to several key advantages (i) provides better quality of the machined surface integrity and offers (ii) lowering feed rate to 5μm/rev to obtain a machined surface roughness of 30 nm (optical quality).

Molecular dynamics simulation (MDS) to study nanoscale machining processes

Molecular Dynamics Simulations (MDS) are constantly being used to make important contributions to our fundamental understanding of material behaviour, at the atomic scale, for a variety of thermodynamic processes. This chapter shows that molecular dynamics simulation is a robust numerical analysis tool in addressing a range of complex nanofinishing (machining) problems that are otherwise difficult or impossible to understand using other methods. For example the mechanism of nanometric cutting of silicon carbide is influenced by a number of variables such as machine tool performance, machining conditions, material properties, and cutting tool performance (material microstructure and physical geometry of the contact) and all these variables cannot be monitored online through experimental examination. However, these could suitably be studied using an advanced simulation based approach such as MDS. This chapter details how MD simulation can be used as a research and commercial tool to understand key issues of ultra precision manufacturing research problems and a specific case was addressed by studying diamond machining of silicon carbide. While this is appreciable, there are a lot of challenges and opportunities in this fertile area. For example, the world of MD simulations is dependent on present day computers and the accuracy and reliability of potential energy functions [109]. This presents a limitation: Real-world scale simulation models are yet to be developed. The simulated length and timescales are far shorter than the experimental ones which couples further with the fact that contact loading simulations are typically done in the speed range of a few hundreds of m/sec against the experimental speed of typically about 1 m/sec [17]. Consequently, MD simulations suffer from the spurious effects of high cutting speeds and the accuracy of the simulation results has yet to be fully explored. The development of user-friendly software could help facilitate molecular dynamics as an integral part of computer-aided design and manufacturing to tackle a range of machining problems from all perspectives, including materials science (phase of the material formed due to the sub-surface deformation layer), electronics and optics (properties of the finished machined surface due to the metallurgical transformation in comparison to the bulk material), and mechanical engineering (extent of residual stresses in the machined component) [110]. Overall, this chapter provided key information concerning diamond machining of SiC which is classed as hard, brittle material. From the analysis presented in the earlier sections, MD simulation has helped in understanding the effects of crystal anisotropy in nanometric cutting of 3C-SiC by revealing the atomic-level deformation mechanisms for different crystal orientations and cutting directions. In addition to this, the MD simulation revealed that the material removal mechanism on the (111) surface of 3C-SiC (akin to diamond) is dominated by cleavage. These understandings led to the development of a new approach named the “surface defect machining” method which has the potential to be more effective to implement than ductile mode micro laser assisted machining or conventional nanometric cutting.

Advances in the surface defect machining (SDM) of hard steels

This paper reports the realisation of precision surface finish (Ra 30 nm) on AISI 4340 steel using a conventional turret lathe by adapting and incorporating a surface defect machining (SDM) method [Wear, 302, 2013 (1124-1135)]. Conventional ways of machining materials are limited by the use of a critical feed rate, experimentally determined as 0.02 mm/rev, beyond which no appreciable improvement in the machined quality of the surface is obtained. However, in this research, the novel application of an SDM method was used to overcome this minimum feed rate limitation ultimately reducing it to 0.005 mm/rev and attaining an average machined surface roughness of 30 nm. From an application point of view, such a smooth finish is well within the values recommended in the ASTM standards for total knee joint prosthesis. Further analysis was done using SEM imaging, white light interferometry and numerical simulations to verify that adapting SDM method provides improved surface integrity by reducing the extent of side flow, microchips and weldments during the hard turning process.

Cutting forces and wear analysis of Si3N4 diamond coated tools in high speed machining

Si3N4 tools were coated with a thin diamond film using a Hot-Filament Chemical Vapour Deposition (HFCVD) reactor, in order to machining a grey cast iron. Wear behaviour of these tools in high speed machining was the main subject of this work. Turning tests were performed with a combination of cutting speeds of 500, 700 and 900 m min−1, and feed rates of 0.1, 0.25 and 0.4 mm rot−1, remaining constant the depth of cut of 1 mm. In order to evaluate the tool behaviour during the turning tests, cutting forces were analyzed being verified a significant increase with feed rate. Diamond film removal occurred for the most severe set of cutting parameters. It was also observed the adhesion of iron and manganese from the workpiece to the tool. Tests were performed on a CNC lathe provided with a 3-axis dynamometer. Results were collected and registered by homemade software. Tool wear analysis was achieved by a Scanning Electron Microscope (SEM) provided with an X-ray Energy Dispersive Spectroscopy (EDS) system. Surface analysis was performed by a profilometer.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Technique for micro-machining millimetre-wave rectangular waveguide

A new technique is reported for micro-machining millimetre-wave rectangular waveguide components. S-parameter measurements on these structures show that they achieve lower loss than those produced using any other on-chip fabrication technique, have highly accurate dimensions, are physically robust, and are cheap and easy to manufacture.

Low cost, patterning of human hNT brain cells on parylene-C with UV & IR laser machining

This paper describes the use of 800nm femtosecond infrared (IR) and 248nm nanosecond ultraviolet (UV) laser radiation in performing ablative micromachining of parylene-C on SiO2 substrates for the patterning of human hNT astrocytes. Results are presented that support the validity of using IR laser ablative micromachining for patterning human hNT astrocytes cells while UV laser radiation produces photo-oxidation of the parylene-C and destroys cell patterning. The findings demonstrate how IR laser ablative micromachining of parylene-C on SiO2 substrates can offer a low cost, accessible alternative for rapid prototyping, high yield cell patterning.

Influence of chromium content on the dry machining performance of cathodic arc evaporated TiA1N coatings

Physical vapour deposition (PVD) titanium aluminium nitride coated cutting tools are used extensively in global manufacturing for reducing production costs and improving productivity in a number of aggressive metal-cutting operations, namely, dry and high-speed machining. In this investigation, the performance of Ti1−xAlxN and Ti1−x−yAlxCryN coatings was assessed on Co-HSS twist drills used to machine grey cast iron. The failure criterion for drills was defined as a critical sized flank wear land at the outer corners of the drills. Using this criterion, the average tool life of uncoated twist drills was increased by factors of 2.5, 3.0 and 3.0 by Ti0.59Al0.41N, Ti0.27Al0.19Cr0.54N and Ti0.21Al0.14Cr0.65N coatings, respectively. Notwithstanding the similar increase in average tool life, the Ti1−x−yAlxCryN coatings produced more consistent results than the Ti1−xAlxN coated drills with standard deviations of 67, 3 and 19 holes, respectively. This result has significant practical implications in manufacturing, since drills are not replaced on an individual basis, but rather on a preset tool change frequency. The present paper discusses the performance of Ti1−xAlxN and Ti1−x−yAlxCryN coated drills in terms of average and practical drill life and concludes with remarks on the characterisation of PVD coatings and their significance on the performance of Co-HSS twist drills when dry machining grey cast iron.