Arunachalam N

Deep hole drilling process is extensively used in demanding applications, namely plastic injection moulding, oil and gas industry and automobile engines. This drilling process encounters problems such as improper chip evacuation, poor surface finish, roundness variation, and high tool wear, which affects the hole quality. To overcome these limitations, ultrasonic vibration-assisted deep hole drilling (UVADD) is performed in this work. The high-frequency vibrations were imparted into the ASTM A36 steel (workpiece) to induce reciprocating motion, which is mounted over a transducer. A special fixture arrangement was fabricated to hold the transducer in the drill bed. This helps to transfer the vibration to the workpiece. The machining performance of UVADD was analyzed by means of cutting force, tool wear, torque, hole quality, surface roughness, machining time and chip morphology, and the performances were also compared with the conventional deep hole drilling (CDD) process. Results suggested that a reduction in cutting force and torque was observed for UVADD. This is due to small discontinuous chips generated. Hole quality showed a drastic decrease in burr formation and surface roughness along with a uniform radius around the periphery. From the tool wear examination, it was also evident that no built-up edge formation occurred along the cutting edge owing to effective penetration of coolant and reduction in cutting temperature. This comprehensive analysis revealed that UVADD provided an enhancement in machining performance as compared to CDD.

Appropriate lighting is one of the indispensable elements in inspection using machine vision system. Illumination variation affects the accuracy and robustness of an inspection method that employs a machine vision system. The lighting inhomogeneity is the disturbing signal that needed to be suppressed to achieve accuracy and consistency in surface roughness quantification. In this work, the illumination compensation techniques are used for ground surface roughness evaluation by statistical texture parameters using machine vision method. The three-dimensional (3-D) surface roughness parameters are compared with the texture parameters. The experimental results are based on the ground surface images that are machined at different machining parameters. After the grinding process, the images are captured under halogen lighting. The acquired images of ground specimens are used for illumination compensation using: homomorphic filtering, Discrete Cosine Transform (DCT) based filtering and Fourier Transform (FT) based filtering techniques. This helps to suppress the low frequency components and amplify the high frequency components in order to extract the texture information. Owing the fact that the ground surfaces were weaker anisotropic surfaces, the second order statistical evaluation methods are used to extract the changes in the image texture due to the variation in surface roughness of the component. The texture parameters evaluated using these methods are correlated with the 3-D surface roughness parameters measured using an optical profiler. The texture parameters showed better correlation with the measured roughness values and this can be an integral part of any grinding system to inspect the machined components. In order to establish the homogeneity achieved after compensation of images, the inhomogeneity indicator and harmonic distortion values are calculated for the ground images.

Super duplex stainless steels (SDSS) are widely used in marine environments because of their excellent mechanical properties and corrosion resistance. The presence of different alloying elements and their two phase microstructure makes it a difficult-to- machine material. The use of multilayer coated cutting tools is an effective strategy to improve the cutting performance during dry machining of this material. In this work, the performance of four different coated tools made either by PVD or CVD has been investigated during dry turning of SDSS. Their performances were evaluated in terms of tool wear, cutting force, cutting temperature and surface integrity. Results indicated that [MT-TiCN]-Al2 O3 coating provided relatively better performance than other coatings in terms of tool wear, cutting force and surface integrity. Their combined properties of higher hardness and oxidation stability make them an effective coating during machining The TiN-[MT-TiCN]-Al2 O3 coating exhibited higher tool wear, poor surface finish and also less tensile residual stress in comparison with surfaces machined using other coated tools. This may be due to the dominance of plastic deformation by mechanical load over temperature effects during machining.

This paper describes a non-contact technique to assess the differences in surface characteristics of the ground components. The computer vision based system is used to analyze the pattern of scattered light from the surface to asses the surface roughness of the component. The ground specimens were manufactured using varying machining parameters. The images of the specimens are captured using a CCD camera. The image parameters based on the wavelet transform are evaluated. Then, the evaluated parameters along with the cutting parameters were used to train the artificial neural network to predict the surface roughness parameters Ra, which is measured using the stylus instrument. The comparison of stylus Ra and that predicted using ANN are presented and analyzed in this paper. Copyright© (2006) by the International Measurement Federation (IMEKO).

The poor machining characteristics of super duplex stainless steels (SDSS) affect the surface integrity as it induces tensile residual stress in the parts. When this machined part is exposed to hostile chloride conditions, it results in the stress corrosion crack (SCC) formation, thereby impacting their service life. To understand the cracking phenomenon involved, it is important to study the distinct characteristics of cracks formed in compliance with the machining (turning) process. This study focusses on studying the effects of machined surface integrity on the stress corrosion cracking of SDSS. Following machining, the specimens were subjected to SCC examination in the chloride condition at different immersion time of 1 h, 2 h, and 5 h. The testing was done in pursuant with ASTM G36 standard. The result indicated that major cracks generated along both parallel as well as the perpendicular direction of machining marks due to the high magnitude of biaxial tensile residual stress. With the increase in immersion time (5 h), new forms of crack and localized corrosion were noticed on the surface. At 1 h and 2 h, only the major cracks were evident, along with minor pit formation on the surface. But at 5 h, selective attack/selective leaching occurred on the turned surface along with the formation of network of cracks. Analysis of the transition pit/crack behaviour of revealed that different pits merge to form a detrimental crack with more intensity than the major cracks. The crack density continue to grow with immersion time of 5 h and also there is no evidence was observed on reduction of crack density. However, the quantification of crack density was not possible at 5 h due to the complex network of high density crack formation. The microstructure analysis revealed that the ferrite phase was more vulnerable to SCC than the austenite phase in chloride environment.

In the stamping process, tools are prone to an adhesive wear mode called galling. This galling wear mode on the stamping tool results in an abrasive wear modes like ploughing and cutting on the workpiece. To study the adhesive and abrasive wear modes relevant in sheet metal stamping processes, scratch tests were performed under controlled conditions where galling, ploughing and cutting can be observed. Two sets of scratch tests were performed to study the galling behaviour using force and acoustic emission sensors. In the first test set, scratch tests were performed at a different depth of penetration to segregate the non-galling and galling conditions. In the second test set, scratch tests were performed at a different sliding distances to understand the influence of galling on the abrasive wear modes. To study the galling behaviour, acoustic emission and force features from both of the test sets were correlated with profilometry wear measurement features like profile depth, wear index, attack angle and volume measurement of galling. From the quantitative measurement of galling on the indenter, a minimal lump was observed on the indenter when the cutting at the edges of scratch was observed. A much larger lump was observed on the indenter for conditions when fracture was observed on the workpiece at the centre of the scratch. The acoustic emission burst signal and unstable force behaviour was mainly observed when the signficant lump was observed on the indenter. The methodology adopted to investigate galling wear in this study lays the strong foundation to develop real-time monitoring systems to observe the transition from non-galling to galling conditions.

Shape memory effects and superelastic nature of Nitinol alloy has been exploited in various biomedical applications. However, prolonged usage of this implant material is restricted due to the toxic effect of released Ni-ions (Ni2+) into tissue environment. This reduces the biocompatibility of the material. The purpose of this study was therefore to assess the effect of graphene coatings on the biocompatibility of Nitinol wires used as dental braces. The graphene coating was prepared on Nitinol substrate through cathodic electrophoretic deposition (EPD). Isopropyl alcohol and magnesium nitrate hexahydrate (Mg(NO3)2•6H2O) were used as a dispersion medium and charging agent for the EPD process respectively. The surface morphology, structure, surface roughness and corrosion resistance behavior of as-deposited and annealed graphene coatings were investigated. As-deposited sample presents a rough surface with visible microcracks and fine pores, while annealed sample possesses a smooth surface with no visible microcracks and pores. The annealed sample showed better results than the as-deposited sample in terms of mechanical strength as observed from Nanoindentation test. Furthermore, Potentiodynamic polarization test was conducted in a simulated body fluid environment to evaluate the corrosion resistance behavior of bare Nitinol substrate and graphene coatings. From the test results, we have found that corrosion potential of a bare Nitinol substrate, as-deposited and annealed sample is around -510 mV, -375 mV and -261 mV respectively. This positive shift in corrosion potential highlighted the improvement in corrosion resistance property of Nitinol substrate by graphene coatings. This indicates the suitability of EPD coated graphene as an effective coating material for biomedical applications.

Grinding is an expensive and complex machining process, characterized by cutting grits undergoing non-uniform wear. The worn out grits influence the surface finish of the part, necessitating timely dressing. Conventionally, the dressing interval is decided either based on the wheel life end criteria viz. visual identification of the workpiece burn mark, chatter occurrence and deterioration in the part finish or on the number of parts produced. Improper dressing interval increases auxiliary machining time and grinding wheel wastage. Prevailing demands towards next generation smart manufacturing include product and process related benefits such as low operational cost, better customer service support, operation optimization and control. In the present work, we propose a low cost, process non-intrusive sensor technology with IoT enabled operational intelligence platform to estimate the redress life of grinding wheel based on wheel condition. Traverse grinding tests were carried out in a CNC surface grinding machine installed with Al2O3 wheel against D2 tool steel under wet condition. During experimentation, the spindle motor current, grinding forces and grinding wheel surface images were acquired using the Hall-effect sensor, dynamometer, and CCD camera respectively. Data acquisition, network connectivity, and cloud communication were empowered by serial output. Statistical time, frequency and wavelet domain features signifying the wheel life characteristic were extracted. To show the usefulness of motor current signals, the extracted features thereof were confirmed against grinding forces and wheel surface images. A time series Auto-Regressive Moving Average (ARMA) predictive model was developed to estimate the grinding wheel redress life using the selected root mean square (RMS) feature of a current signal. An android application was also developed for a graphical visualization of dressing time based on the RMS value of the spindle motor current signal. The developed methodology thus, allows operators and machines with sensors to communicate with each other and facilitates real-time traceability, visibility and control over the dressing action to perform automatic dressing before the wheel reaches its end of life.

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process that uses electrical discharge erosion technique for conductive materials. A high potential difference across the inter-electrode gap between the wire electrode and the workpiece in the presence of dielectric fluid leads to the pulsed discharges. Several sensors have been investigated for adaptive control and monitoring of the WEDM process by detecting and classifying these discharges. The present work discusses the use of a magnetic near-field probe to detect radio frequency emissions during the machining process. Numerical modeling, fabrication, and probe characterization using Device-under-Test (DUT) have been done. The fabricated probe is able to detect the radio emissions corresponding to the pulsed discharge occurring in the inter-electrode gap.

After stress corrosion crack testing in a MgCl2 environment, turned surface of super duplex stainless steel showed four types of cracks. The residual stress and surface defects such as feed marks and long grooves generated after machining played a significant role in the direction and orientation of these cracks. The generated cluster of cracks acted as a precursor to the formation of pits. The biaxial tensile stress in the surface promoted successive branching of cracks, which led to high crack density. The subsurface results showed that cracks passing perpendicular to feed marks found more detrimental than the other types.