Environmental friendly cutting fluids and cooling techniques in machining: a review

Atmospheric-pressure plasmas (APPs) have attracted great interest and have been widely applied in biomedical applications, as due to their non-thermal and reactive properties, they interact with living tissues, cells and bacteria. Various types of plasma sources generated at atmospheric pressure have been developed to achieve better performance in specific applications. This article presents an overview of the general characteristics of APPs and a brief summary of their biomedical applications, and reviews a wide range of these sources developed for biomedical applications. The plasma sources are classified according to their power sources and cover a wide frequency spectrum from dc to microwaves. The configurations and characteristics of plasma sources are outlined and their biomedical applications are presented.

https://iopscience.iop.org/article/10.1088/0963-0252/21/4/043001/pdf

Atmospheric-pressure plasma sources for biomedical applications

The generalization of the Local Density Approximation (LDA) method for the systems with strong Coulomb correlations is presented which gives a correct description of the Mott insulators. The LDA+U method is based on the model hamiltonian approach and allows to take into account the non-sphericity of the Coulomb and exchange interactions. parameters. Orbital-dependent LDA+U potential gives correct orbital polarization and corresponding Jahn-Teller distortion. To calculate the spectra of the strongly correlated systems the impurity Anderson model should be solved with a many-electron trial wave function. All parameters of the many-electron hamiltonian are taken from LDA+U calculations. The method was applied to NiO and has shown good agreement with experimental photoemission spectra and with the oxygen Kα X-ray emission spectrum.

http://absimage.aps.org/image/MAR06/MWS_MAR06-2005-007233.pdf

First-principles calculations of electronic structure and spectra of strongly correlated systems: the LDA+U method

Study on microstructure, mechanical properties and machinability of efficiently additive manufactured AISI 316L stainless steel by high-power direct laser deposition

Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the field of aerospace, automotive and marine applications owing to their excellent improved properties. These materials are of much interest to the researchers from few decades. These composites initially replaced Cast Iron and Bronze alloys but owing to their poor wear and seizure resistance, they were subjected to many experiments and the wear behavior of these composites were explored to a maximum extent and were reported by number of research scholars for the past 25 years. In this paper an attempt has been made to consolidate some of the aspects of mechanical and wear behavior of Al-MMCs and the prediction of the Mechanical and Tribological properties of Aluminum MMCs.

/pdf/mechanical-and-tribological-behavior-of-particulate-4x05huiioi.pdf

Mechanical and Tribological Behavior of Particulate Reinforced Aluminum Metal Matrix Composites – a review

Laser cladding, which can increase the hardness and wear resistance of the used components, is widely used in remanufacture and sustainable manufacturing field. Generally, laser cladding layer should be machined to meet the function as well as the assembly requirements. Milling is an effective means for precision machining. However, there exist great differences of physical and mechanical performances between laser cladding layer and substrate material, such as microstructure, hardness, mechanical properties, etc. This produces some new milling problems for laser cladding layer. An insightful understanding of milling mechanism of laser cladding layer is inevitable. There still lacks the research on this subject, such as chip morphology and mechanical behavior, vibration during laser cladding layer milling process, etc. Thus, the change of chip morphology depending on the cutting parameters and microhardness variation was studied. Signal analysis methods of time and frequency domains of cutting forces and machining vibration were used to evaluate the milling characteristics of laser cladding layer. The microstructural analysis indicates that shear-induced lamella structures are the basic features for the chip free surface. The height-to-thickness ratio of saw-tooth chips increases with increasing cutting speeds and feeds. Microhardness profiles on the top surfaces of machined chip decrease from the back surface to the bulk chip, and the shear band shows increased hardness. The cutting force and machining vibration acceleration of laser cladding layer are higher than those of the KMN steel substrate at the same cutting parameters. The machining vibration is characterized by high vibration in the intermediate position of each layer and low vibration in the joint surfaces between layers.

Study on chip morphology and milling characteristics of laser cladding layer

Machining Behaviour, surface integrity and tool wear analysis in environment friendly turning of Inconel 718 alloy

Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. A robust and efficient tool condition monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality, and benefit the cost control of manufacturers. This paper presents a systematic singularity analysis approach of cutting force and vibrations for feature extraction of TCM in milling. The singularity of sensory signals is estimated by Holder Exponents (HE), which are determined by wavelet transform modulus maxima (WTMM). A comprehensive wavelet basis selection approach is proposed to choose the appropriate wavelet basis for different sensory signals. A de-noising algorithm based on WTMMs' estimation was used as a pre-processing technique to improve noise reduction and preserve the singularities. The mutual information method was employed to rank HE features. The effectiveness of the singularity analysis approach is validated through the Support Vector Machine (SVM) models trained by these ranked features. The estimating results of case studies confirm the efficacy and efficiency of the proposed approach.

/pdf/singularity-analysis-of-cutting-force-and-vibration-for-tool-6cxo7nicj3.pdf

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

Tool condition monitoring (TCM) is extremely important to ensure production efficiency and workpiece quality. It is crucial to extract and select suitable features from raw signals to improve the robustness and feasibility of TCM systems. This paper introduces a cutting force singularity analysis approach for TCM in milling, which correlates tool wear states with force waveform variations. The Holder exponents (HEs) were selected as the index of singularities. HEs are calculated by wavelet transform modulus maxima (WTMM). The raw signal is de-noised based on WTMM estimation, which can effectively preserve singularities compared with traditional filters. Fisher’s discriminant ratio (FDR) is employed to rank the discriminant capability of statistical features of HEs. It is found that means of HEs and quantities of singular points estimated from feed forces Fx show the strongest class-discriminant ability. Then, these features were chosen as training samples to propose a TCM approach based on the support vector machine (SVM). Experimental results indicate that this approach provides reliable and effective advice for tool change.

Jie Sun

Papers

Study on chip morphology and milling characteristics of laser cladding layer

Machining Behaviour, surface integrity and tool wear analysis in environment friendly turning of Inconel 718 alloy

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

Tool condition monitoring in milling using a force singularity analysis approach

Design, modeling and control of a reconfigurable variable stiffness actuator