Multi-Criteria Evaluation of Cryogenic Holding Time Effects on AA7075-T6 Milling Performance

Sliding wear behaviour of salt bath nitrided 316LN austenitic stainless steel

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.

To enhance resolution and reduce artifacts in imaging from incomplete projections, a novel imaging model and algorithm to imaging from incomplete projections—multi-criterion fuzzy optimization approach is presented. This model combines fuzzy theory and multi-criterion optimization approach. The membership function is used to substitute objective function and the minimum operator is taken as fuzzy operator. And a novel resolution method was proposed. The result reconstructed from computer-generated noisy projection data is shown. Comparison of the reconstructed images indicates that this algorithm gives better results both in resolution and smoothness over analytic imaging algorithm and conventional iterative imaging algorithm.

An ultrasound-aided deep rolling process (UADR) for anti-fatigue applications was developed and used for surface enhancement of titanium alloy specimens. The rotating bending fatigue test was performed for the UADR-treated and untreated fatigue specimens. Fractography of the fatigue-fractured specimens was investigated via scanning electronic microscopy (SEM). Surface and subsurface residual stress distributions after UADR treatment were measured by X-ray diffraction method. Surface morphology and roughness were observed and measured via SEM micrograph and a Talysurf roughness tester, respectively. The results showed that a deep layer of residual compressive stress developed and surface roughness was reduced after UADR treatment. Fatigue strength of the titanium alloy specimens was substantially improved. The fractographic examination of the fatigue-fractured specimens showed that the UADR-treated specimens developed finer fatigue striations than the untreated specimen.

Improvement in tribological behavior of commercial pure titanium (CP-Ti) by surface mechanical attrition treatment (SMAT)

Background and objective:Chemical cleansing by denture cleansers is first choice for denture plaque control. The most common problems while using denture cleansers are hardening, porosity, odor sorption, water sorption, solubility, and colour change, bacterial and fungal growth. Chemical cleansing procedures have been found to have an effect on the physical and mechanical properties of denture liners. Thus, this study was conducted to evaluate the effect of commercially available denture cleansers on surface hardness and roughness of acrylic and silicon based denture liners at various time interval.Method:Two autopolymerising denture liners Kooliner (acrylic) and GC reline soft (silicon) were tested with two commercially available denture cleansers, polident and efferdent plus. Total of 120 specimens were prepared and all the specimens were divided into six groups based on the relining materials and denture cleansers used. Surface hardness and surface roughness was tested using Shore A durometer and profilometer respectively at the end of day 1, day 7, day 30 and day 90. All the specimens were stored in artificial saliva throughout the study. Cleanser solution was prepared daily by adding Polident and Efferdent plus denture cleanser tablet into 250ml of enough very warm (not hot) water. Acrylic and silicon liner groups were cleansed in a solution of denture cleanser and water for 15 minutes daily, rinsed with water and stored in artificial saliva at room temperature. The data was analyzed with one way ANOVA and independent t-test.Result:The acrylic soft lining showed gradual hardening and increase in surface roughness after immersion in denture cleanser and also with time. Acrylic liner material showed maximum hardness and roughness with Polident followed by Efferdent plus and water (control group). Silicone lining material showed a slight difference in hardness and roughness between the test group and control group. There was a slight increase in hardness in all the groups with time. Very slight increase in mean surface roughness of all the silicon liner groups from day 1 to day 90 was observed. A statistically significant change was noted between and within the all silicon liner groups on day 7, day 30 and day 90.Conclusion:The average surface hardness and surface roughness were lower in silicon liner material than acrylic liner material. Maximum surface roughness was noted by Polident followed by Efferdent Plus and Water for both acrylic liner group and silicon liner group.

RF plasma nitriding of severely deformed iron-based alloys

Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach

The steel surface (ХГС) was irradiated with a high-current electron beam. A high-current electron beam was obtained in the TEMP-A accelerator. Steel samples were made from drum mill balls. The balls had varying degrees of wear. The mechanical and microstructural characteristics were studied. Applied methods: measurement of hardness, tensile strength, fractographic and metallographic analysis. An increase in hardness was found for all samples. An increase in the surface hardness of the samples occurred as a result of the conversion of austenite to martensite and lower bainite. A significant effect on the increase in surface hardness of the samples is also exerted by the process of the appearance of carbides.

Effects of cooling air temperature on cryogenic machining of Ti–6Al–4V alloy

Microstructure evolution and mechanical properties of nanocrystalline Ni–30Fe alloy

The present paper is focused on fabrication, characterization, and aging behavior of nanostructured Al 6061 alloy prepared by mechanical milling. The milling was performed on Al 6061 in a planetary ball mill under argon atmosphere for various times up to 30 h. Isothermal heat treatment was then carried out on 30 h milled powder at different temperatures up to 550°C. The structural evolution of milled and annealed powders was studied by X-ray diffraction, SEM observation, and hardness measurements. The results showed that the final milled product was an Al – Mg – Si supersaturated solid solution having a grain size of 35 nm and a microhardness of 157 HV. Additionally, Mg 2 Si precipitation between 250 and 300°C led to an increase in hardness with the peak-aging hardness value of 207 HV after heat treatment at 300°C for 2 h. Above this temperature, due to the dissolution of the precipitates and grain growth, the hardness values decreased to 130 HV after heat treatment at 550°C for 3 h.

Sliding wear behaviour of CP titanium laminates produced by large strain extrusion machining

The optimal design parameters of fluid-loaded shells, provided with actively controlled stiffeners, are determined using a rational multi-criteria optimization approach. The adopted approach aims at simultaneously minimizing the shell vibration, associated sound radiation, weight of the stiffening rings, the control energy, and the cost of the shell/stiffeners assembly while maximizing the controllability and observability indices. A finite element model is presented to predict the vibration and noise radiation from cylindrical shells, with active stiffeners, into the surrounding fluid domain. The production cost as well as the life cycle and maintenance costs of the stiffened shells are computed using the Parametric Review of Information for Costing and Evaluation (PRICE) model. A Pareto/min-max multi-criteria optimization approach is then utilized to select the optimal locations and dimensions of the active stiffeners. Numerical examples are presented to compare the vibration and noise radiation characteristics of me optimally designed/controlled stiffened shells with the corresponding characteristics of plain un-stiffened and uncontrolled shells. The obtained results emphasize me importance of the adopted multi-criteria optimization approach in the design of quiet, low weight and low cost underwater shells which are suitable for various critical applications.

The energy issue has given rise to a prolific research field, which branches into several strands. One of these strands focuses on the role played by building energy features in shaping property prices. Indeed, market players are expected to show a higher willingness to pay for building units characterized by higher energy performance. The study of the so-called price premium for building energy efficiency has flourished in the last decade or so; plenty of evidence is now available concerning its occurrence, although its magnitude is still debated. The literature relies on the methodological frameworks of statistical modeling and multiple regression, primarily employing hedonic price models. Lately, spatial autoregressive models have also been adopted. Here, we propose to deal with estimation of the price premium by adopting an innovative perspective. In particular, we use a methodological framework in which regression models are complemented with a multi-criteria optimization approach. Using a spatial autoregressive model first, and with D as the reference energy rating band, we find the following price premiums: 55% for A4, 42% for A3 to A, 20% for B or C, −14% for F, and −29% for G. The multi-criteria optimization approach proves efficient in estimating the price premium. The estimates above are essentially confirmed: the results converge for all the energy rating bands except for G.