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.     

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.     

A non-contact method for part-based process performance monitoring in end milling operations

Surface response to excitation (SuRE) method was originally developed for structural health monitoring (SHM) applications. SuRE was used to evaluate the performance of completed milling operations. The method generates surface waves on the plate and studies the spectrum changes at selected points to detect defects and change of compressive forces. In this study, the length, depth, and width of a slot were changed step by step. The surface of the aluminum plate was excited in the 20–400 kHz range with a piezoelectric element. A laser scanning vibrometer was used to monitor the vibrations at the predetermined grid points after the dimensions of the slot were changed methodically. The frequency spectrums of measured vibrations were calculated by using the Fast Fourier Transformation (FFT). The sums of the squares of the differences (SSD) of the spectrums were calculated to evaluate the change of the spectrums. The SuRE method was able to determine if the dimensions were changed in each case at all the selected points. The scanning laser vibrometer is not feasible to be used at the shop floor. However, the study demonstrated that a piezoelectric element attached to any of the grid points would be able to evaluate the completed machining process.     

Calibration of Expert Judgments Applied to Uncertainty Assessment

Abstract:

This article contributes a methodology for eliciting expert judgment in support of decision analysis associated with the conceptual design of advanced engineering systems. To provide a basis for decision making in the presence of model and input parameter uncertainties, experts in several disciplines can be utilized to provide model parameter estimates to facilitate analyses. The judgment elicitation methodology was developed to cover a multitude of system disciplines using multiple experts. To address consistency in expert assessments, the methodology includes expert assessment calibration means. A sample application of the resultant expert judgment methodology is discussed. An engineering manager can use the methodology described in this article to assess viability of potential courses of action in high-risk or advanced state-of-the-art technology systems development ventures.     

What installed mobile applications tell about their owners and how they affect users’ download behavior

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. An experimental study was conducted through the use of a specifically designed mobile application, on users’ mobile phones. The goals were; first, to learn about the users’ personality and the applications they downloaded to their mobile phones, second to recommend applications to users via notifications through the use of experimental mobile application and learn about user behavior in mobile environment. The question of how the personality features of users affect their compliance to recommendations is explored in this study. It is found that conscientiousness is positively related with accepting recommended applications and being agreeable is related with the preference for the applications of editor’s choice. Furthermore, in this study, applications owned by the user and the composition of applications under categories and their relation with personality features are explored. It is shown that the number of user owned applications and their category differ according to gender and personality. Having similar applications and the number of applications owned under specific categories increase the probability of accepting recommended applications.     

Experimental study of a micro-scale sloped solar chimney power plant

Journal of Mechanical Science and Technology - Solar chimney power plants (SCPP) are structures that have the potential to generate a significant amount of electrical energy without harming the...