Modelling and simulation on the tool wear in nanometric cutting

Tool wear is a significant factor affecting the machined surface quality. In this paper, a Molecular Dynamics (MD) simulation approach is proposed to model the wear of the diamond tool in nanometric cutting. It includes the effects of the cutting heat on the workpiece property. MD simulation is carried out to simulate the nanometric cutting of a single crystal silicon plate with the diamond tip of an Atomic Force Microscope (AFM). The wear mechanism is investigated by the calculation of the temperature, the stress in the diamond tip, and the analysis of the relationship between the temperature and sublimation energy of the diamond atoms and silicon atoms. Microstrength is used to characterize the wear resistance of the diamond tool. The machining trials on an AFM are performed to validate the results of the MD simulation. The results of MD simulation and AFM experiments all show that the thermo-chemical wear is the basic wear mechanism of the diamond cutting tool

An investigation on the mechanics of nanometric cutting and the development of its test-bed

The mechanics of machining at a very small depth of cut (100 nm or less) is not well understood. The chip formation physics, cutting forces generation, resulting temperatures and the size effects significantly affect the efficiency of the process and the surface quality of the workpiece. In this paper, the cutting mechanics at nanometric scale are investigated in comparison with conventional cutting principles. Molecular Dynamics (MD) is used to model and simulate the nanometric cutting processes. The models and simulated results are evaluated and validated by the cutting trials on an atomic force microscope (AFM). Furthermore, the conceptual design of a bench-type ultraprecision machine tool is presented and the machine aims to be a facility for nanometric cutting of threedimensional MEMS devices. The paper concludes with a discussion on the potential and applications of nanometric cutting techniques/equipment for the predictabilty, producibility and productivity of manufacturing at the nanoscale

An investigation on the mechanics of nanometric cutting and the development of its test-bed

The mechanics of machining at a very small depth of cut (100 nm or less) is not well understood. The chip formation physics, cutting forces generation, resulting temperatures and the size effects significantly affect the efficiency of the process and the surface quality of the workpiece. In this paper, the cutting mechanics at nanometric scale are investigated in comparison with conventional cutting principles. Molecular Dynamics (MD) is used to model and simulate the nanometric cutting processes. The models and simulated results are evaluated and validated by the cutting trials on an atomic force microscope (AFM). Furthermore, the conceptual design of a bench-type ultraprecision machine tool is presented and the machine aims to be a facility for nanometric cutting of threedimensional MEMS devices. The paper concludes with a discussion on the potential and applications of nanometric cutting techniques/equipment for the predictabilty, producibility and productivity of manufacturing at the nanoscale

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Soft computing for intelligent data analysis

Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. The paper briefly looks at some of the key issues in intelligent data analysis, discusses the opportunities for soft computing in this context, and presents several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies

Saline and Alkaline tolerance of wetland plants — what are the most representative evaluation indicators?

The increasing discharge of wastewater containing inorganic salts, sometimes accompanied by high pH, has been a worldwide environmental problem. Constructed wetlands (CWs) are considered a viable technology for treating saline and/or alkaline wastewater provided that saline-alkaline tolerant plant species are selected and applied. The influence of both saline and alkaline stress on four wetland plant species during their seed germination, early growth, vegetative propagation and continued growth stages was evaluated by three experiments. Principal component analysis (PCA) was conducted for selecting representative indicators for evaluating the saline and alkaline tolerance of plants during vegetative propagation and plant growth stages. The saline and alkaline stress inhibited the vegetative propagation and plant growth of all tested plant species to varying degrees, therein the influences of saline-alkaline stress on plants were more marked than saline stress. The length of new roots, Na+ accumulation in plant tissue, Na+/K+ ratios in aerial tissue and the total dry biomass were selected as most representative indicators for evaluating the saline and alkaline tolerance of plants. Iris sibirica and Lythrum salicaria showed better saline and alkaline tolerance ability among tested species and could be grown in CWs for treating saline and/or alkaline wastewater

The effects of machining process variables and tooling characterisation on the surface generation: modelling, simulation and application promise

The paper presents a novel approach for modelling and simulation of the surface generation in the machining process. The approach, by integrating dynamic cutting force model, regenerative vibration model, machining system response model and tool profile model, models the complex surface generation process. Matlab Simulink is used to interactively perform the simulation in a user-friendly, effective and efficient manner. The effects of machining variables and tooling characteristics on the surface generation are investigated through simulations. CNC turning trials have been carried out to evaluate and validate the approach and simulations presented. The proposed approach contributes to comprehensive and better understanding of the machining system, and is promising for industrial applications with particular reference to the optimisation of the machining process based on the product/component surface functionality requirements

Combining Genome Wide Association Studies and Differential Gene Expression Data Analyses Identifies Candidate Genes Affecting Mastitis Caused by Two Different Pathogens in the Dairy Cow

Mastitis is a costly disease which hampers the dairy industry. Inflammation of the mammary gland is commonly caused by bacterial infection, mainly Escherichia coli, Streptococcus uberis and Staphylococcus aureus. As more bacteria become multi-drug resistant, one potential approach to reduce the disease incidence rate is to breed selectively for the most appropriate and potentially protective innate immune response. The genetic contribution to effective disease resistance is, however, difficult to identify due to the complex interactions that occur. In the present study two published datasets were searched for common differentially expressed genes (DEGs) with similar changes in expression in mammary tissue following intra-mammary challenge with either E. coli or S. uberis. Additionally, the results of seven published genome-wide association studies (GWAS) on different dairy cow populations were used to compile a list of SNPs associated with somatic cell count. All genes located within 2 Mbp of significant SNPs were retrieved from the Ensembl database, based on the UMD3.1 assembly. A final list of 48 candidate genes with a role in the innate immune response identified from both the DEG and GWAS studies was further analyzed using Ingenuity Pathway Analysis. The main signalling pathways highlighted in the response of the bovine mammary gland to both bacterial infections were 1) granulocyte adhesion and diapedesis, 2) ephrin receptor signalling, 3) RhoA signalling and 4) LPS/IL1 mediated inhibition of RXR function. These pathways comprised a network regulating the activity of leukocytes, especially neutrophils, during mammary gland inflammation. The timely and properly controlled movement of leukocytes to infection loci seems particularly important in achieving a good balance between pathogen elimination and excessive tissue damage. These results suggest that polymorphisms in key genes in these pathways such as SELP, SELL, BCAR1, ACTR3, CXCL2, CXCL6, CXCL8 and FABP may influence the ability of dairy cows to resist mastitis

Non-linear vortex dynamics and transient effects in ferromagnetic disks

We report a time resolved imaging and micromagnetic simulation study of the relaxation dynamics of a magnetic vortex in the non-linear regime. We use time-resolved photoemission electron microscopy and micromagnetic calculations to examine the emergence of non-linear vortex dynamics in patterned Ni80Fe20 disks in the limit of long field pulses. We show for core shifts beyond ~20-25% of the disk radius, the initial motion is characterized by distortions of the vortex, a transient cross-tie wall state, and instabilities in the core polarization that influence the core trajectories.Comment: 11 pages, 3 figures, submitted to Phys. Rev. Let