Defect Index of Timberwork in House, Korea

Wood is a material that is familiar to humans and environment-friendly, and it is used widely as a building material. However, as the dispute over housing defects have increased in Korea, various defects have occurred in timberwork and have become disputes. Notwithstanding, efforts to analyze defects in timberwork systematically to reasonably solve the problem are lacking. In this study, defects in timberwork from housing complexes in Korea were standardized, and critical defects were selected to suggest a method as a management standard. The standard for defects includes time, types of facility work, location and subject, and defect phenomenon. The defect time is categorized into before handover and after handover, whereas facility work is divided into woodwork, door and window work, finishing work, and miscellaneous work. Location and subject are categorized into 13 areas, such as ceiling, floor, and door, and phenomena concerned are of 14 types, including faults and no installation. Therefore, the standardized defect items according to such criteria are classified into a total of 63 types. Ten defect items, whose numbers of defect occurrences per defect and defect repair cost ratio above the average, were selected, including discoloration and breakage of the wooden floor. The repair cost ratio of these defect items accounted for 85.62% of the total repair costs. On the contrary, the repair cost for the defects from the timber work outlined in the Construction Appraisal Practice, a representative defect standard in Korea, was 54.54% of the total. Meanwhile, according to the Defect Judgment Standard, the defect repair cost attributed 45.54% of the total. Therefore, since the 10 defect items proposed in this study can explain the defects in the timberwork compared with other standards, it would be reasonable to designate these 10 defect items as essential defects.

Improving the robustness of DI and PVI further using fast guided filter on radiographic images

To further improve the robustness of the weld defect index (DI) and peak-valley index (PVI), which are key indices for detecting weld defects in radiographic testing (RT) images accurately and reliably, a robust improvement method is proposed, in which a fast guided filter (Fast-GF) is introduced and its effect on the DI and PVI is analysed. In this paper, the principle of the proposed robust improvement method, the related theory of Fast-GF, the definition and the calculational method of the DI and PVI are systematically analysed. Taking some practical RT images from industrial welding as an example, smoothing experiments with different filters and comparative computational experiments for the DI and PVI both with and without Fast-GF are carried out. The experimental results show that the robustness of the DI and PVI is further improved by the proposed robust improvement method, which is a desirable outcome. More specifically, the values of the DI and PVI are computed accurately and reliably regardless of some non-uniform distribution of grey levels, noise, irregular surfaces and artefacts in the RT images.

Effect of Cold Working and Long-Term Heating in Air on the Stress Corrosion Cracking Growth Rate in Commercial TT Alloy 690 Exposed to Simulated PWR Primary Water

The authors have previously reported that the number of cavities at or near grain boundary (GB) carbides in commercial thermally treated (TT) Alloy 690 increases with increasing cold work reduction ratio and with heating temperature in air. In the present work after very long-term heating in air, the number of cavities at or near GB carbides in cold worked commercial TT Alloy 690 was observed to saturate, and the shape and size of the cavities changed. The shape and size of cavities and cracks were categorized, and a GB defect index number was defined as a function of their number, shape and size. Stress corrosion cracking growth rates in a commercial TT Alloy 690 with various levels of cold work exposed to simulated PWR primary water at 633 K (360 °C) have been measured and correlated with the defined GB defect index number. Cavities and cracks in the same materials before and after long-term heating in air have also been correlated with the defined GB defect index number. For the heavily cold worked (≥ 15 pct) commercial TT Alloy 690, a good correlation has been observed between the PWSCCGR and the GB defect index number. By contrast, for lightly cold worked (≤ 10 pct) commercial TT Alloy 690, the SCCGR in the simulated PWR primary water was very low and the GB defect index number was usually zero, regardless of cold working reduction ratio ≤ 10 pct. It is concluded that the mechanism of SCCGR for lightly cold worked TT Alloy 690 in PWR primary water is likely to be different from that for heavily cold worked TT Alloy 690.

Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016

We apply the concept of the data defect index to study the potential impact of systematic errors on the 2020 pre-election polls in 12 presidential battleground states. We investigate the impact under the hypothetical scenarios that (1) the magnitude of the underlying nonresponse bias correlated with supporting Donald Trump is similar to that of the 2016 polls, (2) the pollsters’ ability to correct systematic errors via weighting has not improved significantly, and (3) turnout levels remain similar to 2016. Because survey weights are crucial for our investigations but are often not released, we adopt two approximate methods under different modeling assumptions. Under these scenarios, which may be far from reality, our models shift Trump’s estimated two-party voteshare by a percentage point in his favor in the median battleground state, and increases twofold the uncertainty around the voteshare estimate.

Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys

Friction stir welding process has been studied extensively in the last decades since its early stage. Most of the research done so far is related to the process development including tool design, material weldability, post-weld mechanical behavior, and microstructural properties. More recently, in-line process monitoring and artificial intelligence algorithms are introduced into this process, but mainly to specific material configuration and joint thicknesses. This study will focus on the evaluation of different machine learning approaches including principle component analysis, K-nearest neighbor, multilayer perceptron, single vector machine, and random forest methods on a friction stir welding cell environment. The input variables provided from this cell environment are namely divided into two groups: one group refers to the application variables and the other group is related to the friction stir welding process variables. The application variables target the aluminum alloys, joint configuration, sheet thicknesses, initial mechanical properties, and their chemical composition. The friction stir welding process variables dictate the rotational speed, travel speed, forging force, longitudinal and transverse forces, torque, and specific energy. The output response to model from these machine learning algorithms is the defect index, which has been quantified using high-resolution immersed bath ultrasounds. This nondestructive evaluation technique has been described previously, which can detect defects ≥150 µm in thin sheets. The defect index has been classified into five classes, which is distinguished by the nature of defect, cold weld, or hot weld, as well as the width of the internal volumetric defect upon ultrasound C-scan result. The dataset, which is composed of around 500 various process conditions, has been generated over the last few years and the variables were taken exclusively in constant weld regime and in the force control mode using the output average values. This paper compares the best resulting machine learning methods applied on a friction stir welding cell basis, which is the K-nearest neighbor and multilayer perceptron algorithms. The K-nearest neighbor model reaches a deviation of 0.55 on the defect index in comparison with the experimental values, which is slightly better than the multilayer perceptron model, which obtains a score of 0.69. Over the initial 59 available model parameters, 10 and 15 of them were retained in the final algorithm using these techniques. The main predictors include the material thickness, base material ultimate tensile stress, rotational speed, travel speed, weld forces, and specific energy. The K-nearest neighbor model was able to provide a map of defect indices with regard to rotational speed and travel speed but was only possible when a higher density of data was found within the prediction area. A data density score was also included within the model to inform the end-user about the prediction reliability. The machine learning models are mainly about differentiating various cases rather than representing the physical phenomena as determined using the finite element analysis. That being said, in order to improve the prediction reliability as well as the machine learning models, the data twinning concept, which consists of generating simulated friction stir welding process conditions by finite element analysis, is briefly discussed.

Design and Simulation of Sensor Based on One Dimensional Photonic Crystal Metallodielectric with Two Defect

In this paper we report characteristics of a 1D metallodielectric photonic crystal with a two defect rod. Simulations of the field propagation for the corresponding Transverse Electric (TE) modes were carried out using Finite Difference Time Domain (FDTD) technique on Maxwell’s Equation. The results show that for certain chosen parameters of the transmittance varies if the refractive index of the defect rod is changed and that approximate linier changes for an refractive index range of 1.3 – 1.5 with a slight increase for a second defect change and a relatively steep decrease for a first defect change. A second defect index change can be used as the first defect sensitivity control The characteristics change due to variation of the defect rod radius is also considered for sensor design optimization

Maximization and Control of Nodular Iron Melt’s Self-Feeding Characteristics to Minimize Shrinkage

This paper describes one possible method to anticipate and control the development of solidification shrinkage, during solidification of nodular cast iron melts, based upon industrial trials made using special designed test castings and closed volume thermal analysis cartridges.The methodology considers both the solidification morphology and solidification shrinkage critical size, which is always a difficult component of analysis, along with a developed contraction defect index, that allows the application to several types of molten metal and inoculation practices.The use of thermal analysis allows the recognition of unique melt characteristics, in real time, that are not accessed by more traditional measurement equipment. This allows the definition of thermal analysis patterns that characterize the best melt quality for self-feeding. This is a practical to use and powerful tool for modern foundries, taking advantage of new metric, data collection and data analysis. We aim to contribute to scientific knowledge and simultaneously to provide information that can be useful for foundries to improve their process efficiency.

Spectral problems of nonself-adjoint singular discrete Sturm-Liouville operators

In this study we construct a space of boundary values of the minimal symmetric discrete Sturm-Liouville (or second-order difference) operators with defect index (1, 1) (in limit-circle case at ±∞ and limit-point case at ∓∞), acting in the Hilbert space