Journal articles on the topic 'Nimonic 90'

Purpose In the current exploration, the machinability of three different nickel-based super-alloy materials (Inconel 625, Inconel 718 and Nimonic 90) was experimentally investigated by using a die-sinking electrical discharge machining (EDM). The effect of changing important input process parameters such as pulse on time (Ton), off time (Toff), peak current (Ip) and tool rotation (TR) was investigated to get optimum machining characteristics such as material removal rate, roughness, electrode wear rate and overcut. Design/methodology/approach Experimentation has been performed by using Taguchi L9 orthogonal design. An integrated route of fuzzy and grey relational analysis approach with Taguchi’s philosophy has been intended for the simultaneous optimization of machining output parameters. Findings The most approbatory factors for machining setting have been attained as: (Ton = 100 µs, Toff = 25 µs, Ip = 14 A, TR = 725 rpm) for machining of Inconel 625 and Inconel 718; and (Ton = 100 µs, Toff = 75 µs, Ip = 14 A, TR = 925 rpm) for machining of the Nimonic 90 material. Peak current has been observed as an overall influencing factor to achieve better machining process. Microstructural study through SEM has also been carried out to figure out the surface morphology for the EDMed Ni-based super alloys. Originality/value The proposed machining variables and methodology has never been presented for Nimonic 90 alloy on die-sinking EDM.

The thermo-mechanical fatigue (TMF) behaviour of the Nimonic 90 Nickel base superalloy has been investigated within two laboratories. In-phase-tests (IP) where the maximum mechanical strain occurs at the maximum temperature (850°C), and 180°-out-of-phase-tests (180° OP) where the maximum mechanical strain coincides with the minimum temperature (400°C) have been applied. All tests were carried out at varying mechanical strain ranges with a constant strain ratio of Rε = - 1. A temperature rate of 5 K/s was used throughout the whole cycle without any additional cooling system during decreasing temperature. The fatigue life of 180° OP tests is longer compared to identical IP tests. The stress / mechanical strain hysteresis loops are completely different and some characteristic values are compared to each other. The fracture surfaces observed show that fatigue crack (or cracks) starts on the external surface and propagates inwards. The fractures of 180° OP tests are transgranular showing the presence of fatigue striations, while the fractures of IP tests are mixed transgranular and intergranular with no fatigue striations.

The primary goal of this study was to investigate the formability of Nimonic 90 sheet which performs well at high temperatures and pressures, making it ideal for applications in the aerospace, processing, and manufacturing industries. In this present study, finite element analysis (FEA) and optimization of process parameters for formability of Nimonic 90 in sheet hydroforming were investigated. The material’s mechanical properties were obtained by uniaxial tensile tests as per the standard ASTM E8/E8M. The sheet hydroforming process was first simulated to obtain maximum pressure (53.46 MPa) using the FEA and was then validated using an experiment. The maximum pressure obtained was 50.5 MPa in experimentation. Since fully experimental or simulation designs are impractical, the Box–Behnken design (BBD) was used to investigate various process parameters. Formability was measured by the forming limit diagram (FLD) and maximum deformation achieved without failure. Analysis of variance (ANOVA) results also revealed that pressure and thickness were the most effective parameters for achieving maximum deformation without failure. Response surface methodology (RSM) optimizer was used to predict optimized process parameter to achieve maximized response (deformation) without failure. Experimental validation was carried out for the optimized parameters. The percentage of error between experimental and simulation results for maximum deformation was less than 5%. The findings revealed that all the aspects in the presented regression model and FEM simulation were effective on response values.

Powder mixed electrical discharge machining (PM-EDM) is a technological advancement in electrical discharge machining (EDM) processes where fine powder is added to dielectric to improve the machining rate and surface quality. In this paper, machining of Nimonic-90 was carried out using fabricated PM-EDM, setup by adding silicon powder to kerosene oil. The influence of four input process parameters viz. powder concentration (PC), discharge current (IP), spark on duration (SON), and spark off duration (SOFF) has been investigated on surface roughness and recast layer thickness. L9 Taguchi orthogonal and grey relational analysis have been employed for experimental design and multi-response optimization, respectively. With the addition of silicon powder to kerosene oil, a significant decrease in surface roughness and recast layer thickness was noticed, as compared to pure kerosene. Spark on duration was the most significant parameter for both surface roughness and the recast layer thickness. The minimum surface roughness (3.107 µm) and the thinnest recast layer (14.926 μm) were obtained at optimum process parameters i.e., PC = 12 g/L, IP = 3 A, SON = 35 μs, and SOFF = 49 μs using grey relational analysis.

Purpose In the current exploration, the machining of a Nimonic 90 superalloy material was carried out in a die-sinking electric discharge machine. Experimentation was performed to investigate the impact of three input machining factors – current (I), pulse on time (Ton) and pulse off time (Toff) – on various response characteristics such as material removal rate (MRR), surface roughness (Ra) and electrode wear rate (EWR). Design/methodology/approach A Taguchi L9 design and ANOVA were used to assess machine response characteristics. The study also involved a grey relational analysis (GRA) multi-objective technique of optimization. Findings For single-objective performance, the most appropriate machining factors for achieving the best performance were attained as: MRR (I = 20 A, Ton = 200 µs and Toff = 45 µs), Ra (I = 14 A, Ton = 100 µs and Toff = 25 µs) and EWR (I = 17 A, Ton = 150 µs and Toff = 45 µs). The proposed grey relational approach provided the optimal settings (i.e. 14 A I, 100 µs Ton and 25 µs Toff) for the variables used to calculate the predicted and experimental results. Also, a confirmation test indicated that the final experimental grey relational grade value was enhanced when the experimentation was performed at optimal setting. Originality/value To the best of the authors’ knowledge, the present work is the first to examine the proposed machining variables (i.e. current, pulse on time and pulse off time) in relation to the optimization technique of GRA for a Nimonic 90 alloy using a die-sinking electric discharge machining method.

Nickel-based superalloys are widely used in the aerospace, automotive, marine and medical sectors, owing to their high mechanical strength and corrosion resistance. However, they exhibit poor machinability due to low thermal conductivity, high shear modulus, strain hardening, etc. Various modifications have been incorporated into existing machining techniques to address these issues. One such modification is the incorporation of ultrasonic assistance to turning operations. The assisted process is popularly known as ultrasonic assisted turning (UAT), and uses ultrasonic vibration to the processing zone to cut the material. The present article investigates the effect of ultrasonic vibration on coated carbide tool wear for machining Nimonic-90 under dry and wet conditions. UAT and conventional turning (CT) were performed at constant cutting speed, feed rate and depth of cut. The results show that the main wear mechanisms were abrasion, chipping, notch wear and adhesion of the built-up edge in both processes. However, by using a coolant, the formation of the built-up edge was reduced. CT and UAT under dry conditions showed an approximate reduction of 20% in the width of flank wear compared to CT and UAT under wet conditions. UAT showed approximate reductions of 6–20% in cutting force and 13–27% in feed force compared to the CT process. The chips formed during UAT were thinner, smoother and shorter than those formed during CT.

Effect of remelting processes on the microstructure and mechanical properties of as-cast Nimonic 90 superalloy was investigated by OM (optical micrograph), SEM (scanning electron microscopy), EDS (energy-dispersive X-ray) and tensile tests. The results indicated that the microstructure of the as-cast alloy was mainly composed of γ, γ' and carbides which contain Ti and Cr elements. The average grain size of the alloy tends to increase with mould shell temperature ranging from 350°C to 950°C. The strength of the as-cast alloy decreased with the increasing of mold shell temperature, with constant elongation as the mold shell temperature changes.

The total photon cross sections in five nickle-based alloys, viz., Superimphy, Inconel, Nimonic-90, Invar, and stainless steel, are measured at photon energies of 32.1, 52.0, 72.1, 84.3, 145.4, 279.2, 320.0, and 661.6 keV in a good geometric setup using two NaI(Tl) scintillation detectors for low- and medium-energy photons. Experimental total photon cross sections are compared with the theoretical values of Storm and Israel and good agreement is found. Using the subtraction method, we obtain photoelectric and scattering cross sections at appropriate photon energies and compare them with the available theoretical values. The total and partial effective atomic numbers are obtained and presented.

A new method for processing large flat compositionally graded metal-ceramic parts with connected interpenetrating metal and ceramic network is described. Based on powder metallurgical methods, a metal foam is obtained by slip casting of metal powder slurries on a polyurethane foam, and used as preform to achieve a metallic interpenetration within the composite. The porous metallic preform is infiltrated with a ceramic slip and co-sintered. The metallic part is made from Ni-Cr-alloy, or the P/M superalloy Saratherm 2 and Nimonic 90, the ceramic consists of pure 8Y-ZrO2 or zirconia mixed with ZrSiO4. Composites of nominal same composition sintered without the metal foam preform show no metallic interpenetration.

These days, power consumption and energy related issues are very hot topics of research especially for machine tooling process industries because of the strict environmental regulations and policies. Hence, the present paper discusses the application of such an advanced machining process i.e., ultrasonic assisted turning (UAT) process with the collaboration of nature inspired algorithms to determine the ideal solution. The cutting speed, feed rate, depth of cut and frequency of cutting tool were considered as input variables and the machining performance of Nimonic-90 alloy in terms of surface roughness and power consumption has been investigated. Then, the experimentation was conducted as per the Taguchi L9 orthogonal array and the mono as well as bi-objective optimizations were performed with standard particle swarm and hybrid particle swarm with simplex methods (PSO-SM). Further, the statistical analysis was performed with well-known analysis of variance (ANOVA) test. After that, the regression equation along with selected boundary conditions was used for creation of fitness function in the subjected algorithms. The results showed that the UAT process was more preferable for the Nimconic-90 alloy as compared with conventional turning process. In addition, the hybrid PSO-SM gave the best results for obtaining the minimized values of selected responses.

A PADS (Plasma Assisted Debinding and Sintering) reactor developed by Lupatech S.A. has been employed to MIM process two Ni-based superalloys under Argon, at temperatures in the 1280 -1310 °C range, and for 2 to 3 hours. Both materials have chemical compositions similar to that of standard Nimonic® 90 but differ considerably in their powder characteristics. One type of powder was gas-atomized whereas the other was water-atomized. Samples of both materials in as-sintered states as well as subject to different HIP and heat treating conditions have been characterized mechanically in tensile tests and by HV measurements. The best overall results are attained by the water-atomized material sintered at lower temperatures. The PADS processing of these superalloys shows marked advantages over more conventional PM processing technologies. These results are particularly relevant to the development of turbine components for the automotive and aerospace industries.

Introduction Researchers around the globe are utilizing crowdsourcing tools to reach respondents for quantitative and qualitative research (Chambers & Nimon, 2019). Many social science and business journals are receiving studies that utilize crowdsourcing tools such as Amazon Mechanical Turk (MTurk), Qualtrics, MicroWorkers, ShortTask, ClickWorker, and Crowdsource (e.g., Ahn, & Back, 2019; Ali et al., 2021; Esfahani, & Ozturk, 2019; Jeong, & Lee, 2017; Zhang et al., 2017). Even though the use of these tools presents a great opportunity for sharing large quantities of data quickly, some challenges must also be addressed. The purpose of this guide is to present the basic ideas behind the use of crowdsourcing for survey research and provide a primer for best practices that will increase their validity and reliability. What is crowdsourcing research? Crowdsourcing describes the collection of information, opinions, or other types of input from a large number of people, typically via the internet, and which may or may not receive (financial) compensation (Hargrave, 2019; Oxford Dictionary, n.d.). Within the behavioral science realm, crowdsourcing is defined as the use of internet services for hosting research activities and for creating opportunities for a large population of participants. Applications of crowdsourcing techniques have evolved over the decades, establishing the strong informational power of crowds. The advent of Web 2.0 has expanded the possibilities of crowdsourcing, with new online tools such as online reviews, forums, Wikipedia, Qualtrics, or MTurk, but also other platforms such as Crowdflower and Prolific Academic (Peer et al., 2017; Sheehan, 2018). Crowdsourcing platforms in the age of Web 2.0 use remote labor recruited via the internet to assist employers complete tasks that cannot be left to machines. Key characteristics of crowdsourcing include payment for workers, their recruitment from any location, and the completion of tasks (Behrend et al., 2011). They also allow for a relatively quick collection of data compared to data collection in the field, and participants are rewarded with an incentive—often financial compensation. Crowdsourcing not only offers a large participation pool but also a streamlined process for the study design, participant recruitment, and data collection as well as integrated participant compensation system (Buhrmester et al., 2011). Also, compared to other traditional marketing firms, crowdsourcing makes it easier to detect possible sampling biases (Garrow et al., 2020). Due to advantages such as reduced costs, diversity of participants, and flexibility, crowdsourcing platforms have surged in popularity for researchers. Advantages MTurk is one of the most popular crowdsourcing platforms among researchers, allowing Requesters to submit tasks for Workers to complete (Cummings & Sibona, 2017). MTurk has been used as an online crowdsourcing platform for the recruitment of human subjects for research purposes (Paolacci & Chandler, 2014). Research has also shown MTurk to be a reliable and cost-effective tool, capable of providing representative data for research in the behavioral sciences (e.g., Crump et al., 2013; Goodman et al., 2013; Mason & Suri, 2012; Rand, 2012; Simcox & Fiez, 2014). In addition to its use in social science studies, the platform has been used in marketing, hospitality and tourism, psychology, political science, communication, and sociology contexts (Sheehan, 2018). To illustrate, between 2012 and 2017, more than 40% of the studies published in the Journal of Consumer Research used crowdsourcing websites for their data collection (Goodman & Paolacci, 2017). Disadvantages Although researchers have assessed crowdsourcing platforms as reliable and cost-effective for data collection in the behavioral sciences, they are not exempt of flaws. One disadvantage is the possibility of unsatisfactory data quality. In fact, the virtual setting of the survey implies that the investigator is physically separated from the participant, and this lack of monitoring could lead to data quality issues (Sheehan, 2018). In addition, participants in survey research on crowdsourcing platforms are not always who they claim to be, creating issues of trust with the data provided and, ultimately, the quality of the research findings (McGonagle, 2015; Smith et al., 2016). A recurrent concern with MTurk workers, for instance, is their assessment as experienced survey takers (Chandler et al., 2015). This experience is mainly acquired through completion of dozens of surveys per day, especially when they are faced with similar items and scales. Smith et al. (2016) identified two types of problems performing data collection using MTurk; namely, cheaters and speeders. As compared to Qualtrics—which has a strict screening and quality-control processes to ensure that participants are who they claim to be—MTurk appears to be less exigent regarding the workers. However, a downside for data collection with Qualtrics is more expensive fees—about $5.00 per questionnaire on Qualtrics, against $0.50 to $1.50 on MTurk (Ford, 2017). Hence, few researchers were able to conduct surveys and compare respondent pools with Qualtrics or other traditional marketing research firms (Garrow et al., 2020). Another challenge using MTurk arises when trying to collect a desired number of responses from a population targeted to a specific city or area (Ross et al., 2010). The issues inherent to the selection process of MTurk have been the subject of investigations in several studies (e.g., Berinsky et al., 2012; Chandler et al., 2014; 2015; Harms & DeSimone, 2015; Paolacci et al., 2010; Rand, 2012). Feitosa et al. (2015) pointed out that international respondents may still identify themselves as U.S. respondents with the use of fake addresses and accounts. They found that 5% to 10% of participants identifying themselves as U.S. respondents were actually from overseas locations. Moreover, Babin et al. (2016) assessed that the use of trap questions allowed researchers to uncover that many respondents change their genders, ages, careers, or income within the course of a single survey. The issues of (a) experienced workers for the quality control of questions and (b) speeders, which, for MTurk can be attributed to the platform being the main source of revenue for a given respondent, remain the inherent issues of crowdsourcing platforms used for research purposes. Best practices Some best practices can be recommended in the use of crowdsourcing platforms for data collection purposes. Workers IDs can be matched with IDs from previous studies, thus allowing researchers to exclude responses from workers who had answered previous similar studies (Goodman & Paolacci, 2017). Furthermore, proceed to a manual assignment of qualification on MTurk prior to data collection (Litman et al., 2015; Park & Park, 2020). When dealing with experienced workers, both using multiple attention checks and optimizing the survey in a way to have the participants exposed to the stimuli for a sufficient length of time to better address the questions are also recommended (Sheehan, 2018). In this sense, shorter surveys are preferred to longer ones, which affect the participant’s concentration, and may, in turn, adversely impact the quality of their answers. Most importantly, pretest the survey to make sure that all parts are working as expected. Researchers should also keep in mind that in the context of MTurk, the primary method for measurement is the web interface. Thus, to avoid method biases, researchers should ponder whether or not method factors emerge in the latent measurement models (Podsakoff et al., 2012). As such, time-lagged research designs may be preferred as predictor and criterion variables can be measured at different points in time or administered in different platforms, such as Qualtrics vs MTurk (Cheung et al., 2017). In general, the use of crowdsourcing platforms including MTurk may be appropriate according to the research question; and the quality of data is reliant on the quality-control strategies used by researchers to enhance data quality. Trade-offs between various validity types need to be prioritized according to the research objectives (Cheung et al., 2017). From our experience using crowdsourcing tools for our own research as the editorial team members of several journals and chair of several conferences, we provide the best practices as outlined below: MTurk Worker (Respondent) Selection: Researchers should consider their study population before using MTurk for data collection. The MTurk platform should be used for the appropriate study population. For example, if the study targets restaurant owners or company CEOs, MTurk workers may not be suitable for the study. However, if the target population is diners, hotel guests, grocery shoppers, online shoppers, students, or hourly employees, utilizing a sample from MTurk would be suitable. Researchers should use the selection tool in the software. For example, if you target workers only from one country, exclude responses that came from an internet protocol (IP) address outside the targeted country and report the results in the method section. Researchers should consider the demographics of workers on MTurk which must reflect the study targeted population. For example, if the study focuses on baby boomers use of technology, then the MTurk sample should include only baby boomers. Similarly, the gender balance, racial composition, and income of people on MTurk should mirror the targeted population. Researchers should use multiple screening tools that identify quality respondents and avoid problematic response patterns. For example, MTurk provides the approval rate for the respondents. This refers to how many times a respondent is rejected for various reasons (i.e., wrong code entered). We recommend using a 90% or higher approval rate. Researchers should include screening questions in different places with different type of questions to make sure that the respondents are appropriate for your study. One way is to use knowledge-based questions about the subject. For example, rather than asking “How experienced are you with accounting practices?”, a supplemental question such as “Which of the following is a component of an income statement?” should be integrated into the study in a different section of the survey. Survey Validity: Researchers should conduct a pilot survey from MTurk workers to identify and fix any potential data quality and programming problems before the entire data set is collected. Researcher can estimate time required to complete the survey from the pilot study. This average time should be used in calculating incentive payment for the workers in such a way that the payment should equate or exceed minimum wage in the targeted country. Researchers should build multiple validity-check tools into the survey. One of them is to ask attention check questions such as “please click on ‘strongly agree’ in this question” or “What is 2+2? Please choose 5” (Cobanoglu et al., 2016) Even though these attention questions are good and should be implemented, experienced survey takers or bots easily identify them and answer them correctly, but then give random answers to other questions. Instead, we recommend building in more involved validity check questions. One of the best is asking the same question in different places and in different forms. For example, asking the age of the respondent in the beginning of the survey and then asking them the year of their birth at the end of the survey is an effective way to check that they are replying to the survey honestly. Exclude all those who answered the same question differently. Report the results of these validity checks in the methodology. Cavusoglu (2019) found that almost 20% of the surveys were eliminated due to the failure of the validity check questions which were embedded in different places and in different forms in his survey. Researchers should be aware of internet bot, which is a software that runs automated tasks. Some respondents use a bot to reply to the surveys. To avoid this, use Captcha verification, which forces respondents to perform random tasks such as moving the bar to a certain area, clicking in boxes that has cars, or checking boxes to verify the person taking the survey is not a bot. Whenever appropriate, researchers should use time limit options offered by online survey tools such as Qualtrics to control the time that a survey taker must spend to advance to the next question. We found that this is a great tool, especially when you want the respondents to watch a video, read a scenario, or look at a picture before they respond to other questions. Researchers should collect data in different days and times during the week to collect a more diverse and representative sample. Data Cleaning: Researchers should be aware that some respondents do not read questions. They simply select random answers or type nonsense text. To exclude them from the study, manually inspect the data. Exclude anyone who filled out the survey too quickly. We recommend excluding all responses filled out less than 40% of the average time to take the survey. For example, if it takes 10 minutes to fill out a survey, we exclude everyone who fills out this survey in 4 minutes or less. After we separated these two groups, we compared them and found that the speeders’ (aka cheaters) data was significantly different than the regular group. Researchers should always collect more data than needed. Our rule of thumb is to collect 30% more data than needed. For example, if 500 clean data responses are wanted, collect at least 650 data. The targeted number of data will still be available after cleaning the data. Report the process of cleaning data in the method section of your article, showing the editor and reviewers that you have taken steps to increase the validity and reliability of the survey responses. Calculating a response rate for the samples using MTurk is not possible. However, it is possible to calculate active response rate (Ali et al., 2021). It can be calculated as the raw response numbers deducted from all screening and validity check question results. For example, if you have 1000 raw responses and you eliminated 100 responses for coming from IP address outside of the United States, another 100 surveys for failing the validity check questions, then your active response rate would be 800/1000= 80%.

The current experimental work on Nimonic-90 alloy presents the behaviour of hardness as a result of microstructural change occurring after deep cryogenic treatment. The results of the microstructure alterations at sub-zero temperature showed an effective change in hardness. Compared to the hardness value corresponding to a 36-h soaking period, the 24-h soaking period's hardness value was lower. One of the main factors contributing to the enhancement in Nimonic-90's hardness was the phase change and grain size reduction during the soaking period. Nimonic-90's hardness behaviour was largely controlled by tempering following deep cryogenic treatment. The maximum and minimum hardness values were corresponding to 100 °C and 200 °C, respectively. At the soaking period of 36 h, tempering temperature of 100 °C and tempering time of 1 h, and the hardness of Nimonic-90 came as 443.36 HV. With more tempering cycles and higher tempering temperatures, a corresponding decline in hardness value was observed. The sample that was subjected to a tempering temperature of 200 °C, a soaking time of 24 h, and three tempering had the lowest value of hardness out of all the deep cryogenically treated samples. According to the findings, the maximum hardness was 1.56 times the hardness of untreated sample.

Abstract NIMONIC alloy 91 is a modification of NIMONIC alloy 90 (Alloy Digest Ni-6, December 1961) with better stability and improved hot corrosion resistance. This datasheet provides information on composition, physical properties, elasticity, and tensile properties as well as creep. It also includes information on corrosion resistance as well as forming, heat treating, and machining. Filing Code: Ni-503. Producer or source: Inco Alloys International Inc.

Nickel-based alloys (Nimonic 90) are one of the most used materials for aircraft parts, gas turbine components and fasteners due to their inherent properties such as high strength at elevated temperature, good corrosion resistance, high stability, high wear resistance and low thermal conductivity. Because of the above-mentioned properties, Nimonic 90 alloy is difficult to machine, and the roughness obtained by machining of nimonic alloy is comparatively rough. The existing theoretically developed mathematical equations for roughness measurement do not consist of all the machining parameters. It lacks some of the effective roughness parameters such as depth of cut, spindle speed and cutting-edge angle. This article proposes a novel mathematical/geometrical model for the prediction of surface roughness using fundamental geometrical properties of tool and workpiece. For developing the mathematical model, the nose radius of the cutting tool insert is assumed as a straight line (arc length). The principal cutting-edge angle is introduced in the geometrically developed novel model. The developed mathematical/geometrical model comprises mainly depth of cut, principal cutting-edge angle, nose radius, spindle speed and feed. In micro turning, surface roughness increases with an increase in feed and depth of cut. A rough surface, compared to conventional turning, is produced while micro turning due to edge ploughing and rubbing when the chip thickness is lesser than the edge radius. This model is validated by conducting micro-turning experiments on nickel-based superalloy (Nimonic 90) using aluminium titanium nitride physical vapour deposition coated tungsten carbide micro inserts. The surface roughness is significantly affected when the cutting-edge comes in contact with the workpiece; it is because of the imperfect geometry of the nose of the cutting tool. A slight variation of surface roughness with the depth of cut has also been observed. A good correlation is observed between the predicted and experimentally measured roughness values.

The present study has been done to determine the effect of deep cryogenic treatment (DCT) on the hardness of Nimonic-90. From the study, it has been found that the hardness of the material is significantly affected with cryogenic treatment which includes soaking the samples at subzero temperature (−196 °C) for 24 h and 48 h and tempering operation which includes heating the samples in temperature range of 100–200 °C. Also, it has been found that as the tempering temperature decreases from 200–100 °C, the hardness of the samples increases. The number of tempering cycles affected the hardness of the samples as well. The lowest hardness value came out for the untreated sample as 284 HV. Among all the cryo-treated samples, the highest value of hardness was for the single tempering at a tempering temperature of 100 °C, soaking period of 48 h, and tempering time of 1 h which was 435.76 HV. The minimum hardness in DCT samples was for the case of 24 h soaking period, tempering temperature of 200 °C and tempering time of 3 h. which was 376.9 HV. The tremendous improvement in hardness of Nimonic-90 is because of microstructural changes at different soaking period and tempering temperature which includes phase transformation and morphological changes that includes grain size which decreased from 43 to 4.418 µm, precipitation of Cr23C6, NiC, and Cr2Ti at respective soaking period, tempering temperature, and the number of tempering cycles in each treatment.

ABSTRACTA low pressure plasma spraying technique for depositing high-Tc YBCO thick films has been developed. Films with thickness ranging 20–100 μm have been prepared using Y0.3Ba0.7CuOx powders. After post-annealing in oxygen for 1h at 930–950°C, the films, which were deposited on nimonic alloy substrate heated at 650°C during spraying, exhibited a zero resistance temperature of 90.6K with transition width (90%-10%) of 2K and a critical current density (77K, 0T) of 690 A/cm2.

Abstract The characteristics such as high hardness and shear modulus, low thermal conductivity, strain hardening of Nickel-based superalloys lead to high machining forces and temperature, poor surface quality and integrity, rapid tool wear, etc. The present article investigates the tool wear mechanism of the tungsten carbide (WC) tool in μ-turning of Nimonic 90 under dry, wet, and vegetable oil-based cutting fluid (VCF). Canola oil is used as vegetable oil. Three different combinations of cutting speed, feed rate, and depth of cut are considered for analysis. The tool wear is characterized using optical and scanning electron microscopy. Machining with VCF shows an approximate reduction of flank wear width in the range of 12–52% compared to dry and wet conditions. The main wear mechanisms observed on the tool flank and rake face are abrasion, built-up edge adhesion, and edge chipping. The VCF considerably reduces the adhesion and abrasion and, hence, increases tool life. The chips produced in dry conditions are found fractured and uneven, whereas, it had an uneven lamella structure in wet conditions. The VCF found reducing the plastic deformation in each cutting condition, as a result, producing fine lamella structured chips.