Study of surface roughness and hardness of low carbon nickel-chromium based alloy steels under high temperature (AISI sae8630, 3140 & 9310)
High temperature Tribology is an interesting and challenging area which deserves more attention on wear resistance. The present investigation relates to understanding the surface roughness and hardness by the effect of different operational conditions like wear pressure, sliding speed and ambient temperature on AISI SAE 8630, 3140, and 9310. Dry sliding wear tests have been carried out on the materials AISI SAE 8630, 3140, and 9310 under room temperature and also at high temperatures like 200°C, 400°C and 600°C on pin on disc type wear testing machine for 10,000 m sliding distance. The wear tests were carried out under the operational conditions of three sliding speeds like 1, 3 and 5 m/s, under the wear pressures of 0.125 MPa, 0.375 MPa and 0.625 MPa. Weight loss was recorded after each wear test. Hardness after wear was measured by micro hardness tester under 100 gm wear pressure. Roughness of the worn-out surface along the parallel & perpendicular was measured by roughness instrument.
 The present investigation was undertaken keeping in view the wear behaviour of metallic material influenced by the frictional force, which is turn, is governed by the hardness & oxidation kinetics of the mating surface on the high temperature wear. Nickel Chromium based alloys have been reported to be widely used as they combine several advantages such as abrasion, erosion & resistance to high temperature corrosive atmospheres. In this article a brief review of the performance of Nickel Chromium based alloy at different speeds, loads and temperatures has been made and compared with the room temperature.
 It is observed that the hardness of the worn out surface increased with an increase in the load and sliding speed due to work hardening at room temperature. Under low operational conditions, two body abrasive, under high operational conditions oxidative and under moderate operational conditions adhesive wear mechanisms are observed.
An experimental study to corelate water jet impingement erosion resistance and properties of metallic materials and coatings
Abstract This paper describes the water jet impingement erosion characteristics of titanium alloy (Ti6Al4V), Hadfield steel, laser hardened, plasma nitrided and pack borided 12Cr steel along with most commonly used steels in hydro turbines. Round samples as per ASTM G73-98 were tested for water jet impingement erosion study. While testing, in the incubation period, plasma nitrided and pack borided 12Cr steel performed much better than 12Cr and 13Cr4Ni steels. Plasma nitrided 12Cr steel performed much better than pack borided 12Cr steel. This is due to the integrity of plasma nitrided layers and their ability to absorb shocks due to jet impingement. During incubation as well as in the long run, Hadfield steel and laser hardened 12Cr steel performed exceptionally well followed by 17Cr4Ni ‘PH’ steel. Based on this experimental study, a suitable criterion based on ultimate resilience (UR) for metallic materials and a composite modified resilience (CMR) for hard metallic coatings has been discussed. Water jet impingement erosion test results along with the mechanical properties of materials and coatings, and their scanning electron microstructural details are reported in this paper.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.