Rapid determination of the fracture toughness of metallic materials using circumferentially notched bars

Three types of material whose fracture toughness tests were previously performed by using circumferentially notched bars, namely (1) a dual-phase steel with three different morphologies; (2) an Al-Zn-Mg-Cu-wrought alloy; and (3) Al-Si-cast alloys with three different Si contents, were investigated in terms of accuracy and reliability of the testing method. Also, the advantages of using circumferentially notched bars for fracture toughness determination of metallic materials were discussed. With the help of stress concentration factors, which are associated with the bluntness of the notch, correction factors for the fracture toughness calculations are derived. The corrected fracture toughness values are found to be close to the uncorrected ones, implying that the testing procedure is reliable.     

Process characterization and optimization based on censored datafrom highly fractionated experiments

Censored data resulting from life-test of durable products, coupled with complicated structures of screening experiments, makes process characterization very difficult. Existing methods can be inadequate for modeling such data because important effects and factor levels might be identified wrongly. This article presents an expectation-modeling-maximization (EMM) algorithm, where censored data are imputed as pseudo-complete samples and a forward regression is used to compare all main effects and 2-factor interactions for process characterization. Then, the best combination of controllable variables is determined in order to optimize predictions from the final model. A sensitivity study of the selected models, with changes of imputation and parameter estimation methods, shows the importance of using appropriate models and estimation methods in EMM. The author's analysis of the Specht (1985) heat-exchanger life-test data indicates that E, EG, EH in the wall data and A, K, D, DJ in the corner data are the dominating factors. However, in finding the best process recipe, one might use a model with a few additional terms, which leads to more accurate predictions for better process optimization     

BATCH SEQUENCING

Consider the single machine scheduling problem where there are a number of part types to be processed. A part type is defined as follows: Two parts are of the same part type if the machine does not require a setup in between the processing of these parts. The problem investigated in this paper is to find a sequence of batches of parts (if there are any) where all the requirements for parts are met. A heuristic and an exact algorithm are developed, and computational analysis is performed to measure the performance of the heuristic. The time complexity function of the heuristic is O(n²), and the exact algorithm runs in polynomial time given a fixed upper bound on the number of setups.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

A variational approach to problems in calibration of multiple cameras

This paper addresses the problem of calibrating camera parameters using variational methods. One problem addressed is the severe lens distortion in low-cost cameras. For many computer vision algorithms aiming at reconstructing reliable representations of 3D scenes, the camera distortion effects will lead to inaccurate 3D reconstructions and geometrical measurements if not accounted for. A second problem is the color calibration problem caused by variations in camera responses that result in different color measurements and affects the algorithms that depend on these measurements. We also address the extrinsic camera calibration that estimates relative poses and orientations of multiple cameras in the system and the intrinsic camera calibration that estimates focal lengths and the skew parameters of the cameras. To address these calibration problems, we present multiview stereo techniques based on variational methods that utilize partial and ordinary differential equations. Our approach can also be considered as a coordinated refinement of camera calibration parameters. To reduce computational complexity of such algorithms, we utilize prior knowledge on the calibration object, making a piecewise smooth surface assumption, and evolve the pose, orientation, and scale parameters of such a 3D model object without requiring a 2D feature extraction from camera views. We derive the evolution equations for the distortion coefficients, the color calibration parameters, the extrinsic and intrinsic parameters of the cameras, and present experimental results.     

The hydro-abrasive erosion wear behavior of duplex-treated surfaces of AISI H13 tool steel

The influence of duplex surface treatments consisting of a DC-pulsed plasma nitriding process and subsequent coatings of CrN and TiAlN deposited by physical vapor deposition(PVD)on AISI H13 tool steel was studied in this article.The treated samples were characterized using metallographic techniques,SEM,EDS,and microhardness methods.Hydro-abrasive erosion wear tests were performed in a specifically designed wear tester in which the samples were rotated in a wear tank containing a mixture of distilled water and ceramic abrasive chips with a fixed rotational speed.The wear rates caused by the abrasive particle impacts were assessed based on accumulated weight loss measurements.The worn surfaces were also characterized using optical microscopy,SEM,and EDS.Microhardness measurements indicated a significant increase in the surface hardness of the duplex-treated samples.The surfaces of the samples with the TiAlN coating were approximately 15 times harder than that of the untreated samples and 3 times that of the plasma nitrided samples.Hydro-abrasive erosion wear results showed that the duplex surface treatments,especially the CrN coating,displayed the highest erosion wear resistance.