A design advisor for the optimal inspection of circularity tolerance

In the wake of growing importance for quality and the need to reduce inspection costs simultaneously, the need for a scientific method of selecting an optimum inspection strategy for coordinate measuring machine (CMM) based inspection has become very important. The inspection error resulting from CMM inspection is greatly affected by the profile irregularities and the sampling strategy, which includes sample size, sampling methods, and algorithms used for form evaluation. This paper describes a system that can recommend an optimal inspection plan based on the needs of the user. A design of experiments (DOE) based approach is used to relate the inspection error with sampling strategies. Surface irregularities are included in the form of lobes formed on the profile. A new two-way model is proposed that works in both directions between the sampling strategy and the performance metrics. The results indicate that the number of lobes and the sampling method used have little impact on the inspection error, while the sample size and form evaluation algorithms have a significant influence. An inspection plan advisor is presented, which provides an inspection plan based on the estimated shape and acceptable measurement error.     

Cost-Benefit Analysis of Residential Energy-Efficiency Upgrades in Phoenix, Arizona

With worldwide energy consumption rising, there is a need to reduce energy use of the housing stock. While this general idea is well-known, there is an overwhelming amount of product data and manufacturers claims through which the homebuilder or designer must sort to make informed selections of energy-efficient upgrades. The main objective of this research was to identify cost effective–energy efficient upgrades from a group of 16 commonly available choices, and rank them according to the most beneficial for the least cost. This study was conducted in Phoenix, Arizona, and included a sample of 30 production-style homes typical of the Phoenix metropolitan area. Energy-simulation software was used to perform energy evaluations and derive the benefits of each upgrade. The cost of each upgrade was obtained from a database of national average costs and from a local context. Differences in the selection of beneficial upgrades vary dramatically with the source of the cost information. This research provides a generic methodology that can be used by builders and designers to select appropriate energy-efficient upgrades for a given geographic area that will provide high operating benefits relative to the amount of capital investment.     

Thermal stability and high-temperature wear of Ti–TiN and TiN–CrN nanomultilayer coatings under self-mated conditions

Ti–TiN and TiN–CrN nanomultilayers were thermally stable retaining uniform and sharp layer interfaces up to 24 h at 773 K, without any oxidation or phase transformation accompanying each individual layer. Decreasing the multilayer spacing resulted in an increase in the hardness in both cases. The coating hardness was found to be independent of the substrate type, when applied on HS718, Ti64 and HCHCr substrates. In scratch testing, the multilayers displayed a better resistance to the onset of failure, as compared to the monolayer TiN. The substrate plasticity played an important role in determining the coating failure mode. Self-mated wear tests revealed the CrN–TiN system to exhibit the best wear behaviour, both at room temperature and at 773 K. The Ti–TiN coatings are more accommodative with all three substrates, as compared to TiN–CrN and TiN.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Study of automatic text summarization approaches in different languages

Artificial Intelligence Review - Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing...     

Design of Dual-Gate P-type IMOS Based Industrial Purpose Pressure Sensor

Silicon - In this treatise, we have proposed a Single Material Gate–Dual Gate Impact Ionization Metal Oxide Semiconductor (SMG DG-IMOS) based Pressure Sensor. The pressure sensor has the most...