Journal of Quality Technology - July 2018 - ASQ

Journal of Quality Technology - July 2018

Volume 50 ∙ Number 3



  • Opportunities and challenges of quality engineering for additive manufacturing
    By Bianca M. Colosimo, Qiang Huang, Tirthankar Dasgupta and Fugee Tsung
    Additive manufacturing (AM), commonly known as three-dimensional printing, is widely recognized as a disruptive technology, and it has the potential to fundamentally change the nature of future manufacturing. Through building products layer by layer, AM represents a paradigm shift in manufacturing, with many industrial applications. It enables production of huge varieties of customized products with considerable geometric complexity, extended capabilities, and functional performances. Despite tremendous enthusiasm AM faces major research challenges for widespread adoption of this innovative technology. Specifically, addressing the unique challenges associated with quality engineering of AM processes is crucial to the eventual success of AM. This article presents an overview of quality-related issues for AM processes and products, focusing on opportunities and challenges in quality inspection, monitoring, control, optimization, and transfer learning as well as on building quality into the product through design.
  • Uncertainty quantification of machining simulations using an in situ emulator
    By Evren Gul, V. Roshan Joseph, Huan Yan and Shreyes N. Melkote
    Understanding the uncertainty in simulation outputs is important for careful decision-making regarding a machining process. However, Monte Carlo–based methods cannot be used for evaluating the uncertainty when the simulations are computationally expensive. An alternative approach is to build an easy-to-evaluate emulator to approximate the computer model and run the Monte Carlo simulations on the emulator. Although this approach is very promising, it becomes inefficient when the computer model is highly nonlinear and the region of interest is large. Most machining simulations are of this kind because the output is affected by several quantitative factors—such as the workpiece material properties, cutting tool parameters, and process parameters whose effects can change depending on other qualitative factors such as the type of materials, tool designs, and tool paths. Because the number of levels of the qualitative factors can range from tens to thousands, building an accurate emulator is not an easy task. This article proposes a new approach, called an in situ emulator, to overcome this problem. The idea is to build an emulator for the user-specified levels of the qualitative factors and inside the local region defined by the input uncertainty distribution of the quantitative factors. Efficient experimental design and statistical modeling techniques are used for constructing the in situ emulator. The approach is illustrated using the simulations of two solid end milling processes.
  • Phase I monitoring of optical profiles with application in low-emittance glass manufacturing
    By Qian Wu, Li Zeng and Qiang Zhou
    Low-emittance (low-E) glass is an energy-efficient glass product which can reduce heat loss by reflecting most infrared radiation. Great benefit in energy savings has made low-E glass manufacturing an important sector of the glass industry. In the manufacturing process, optical profiles collected on the surface of products are widely available quality measurements. However, the current quality inspection scheme uses simple summaries of the optical profile data and may not perform well in detecting changes. This study considers monitoring of optical profiles directly and develops a methodology for Phase I analysis of such data. The proposed method uses a piecewise polynomial random-coefficient model to characterize optical profiles and a T2 control chart to monitor estimates of random effects in each piece. A procedure to segment the profiles is provided. We also investigate the problem caused by high correlations of random effects and propose a remedy based on regressor transformation. The numerical study and case study show that the proposed method is suitable for the data and able to identify outlying profiles.
  • Surrogate model–based optimal feed-forward control for dimensional-variation reduction in composite parts' assembly processes
    By Xiaowei Yue and Jianjun Shi
    Dimension control and variation reduction are vital for composite parts' assembly processes. Due to the nonlinear properties of composites, physics-based models cannot accurately and efficiently approximate the assembly processes. In addition, conventional robust parameter design (RPD) and statistical process control (SPC) cannot actively compensate for dimensional errors or prevent defects. This article proposes a surrogate model–based optimal feed-forward control strategy for dimensional-variation reduction and defect prevention in the assembly of composite parts. The objective is accomplished by (i) developing a grouped Latin hypercube sampling approach tailored to the problem; (ii) adopting a universal Kriging model for dimensional prediction and then embedding the model into an optimal feed-forward control algorithm; and (iii) conducting a multiobjective optimization to determine the control actions. A case study reveals that the developed methodology can effectively reduce the mean and standard deviation of dimensional deviations for the assembly of composite parts.
  • A Bayesian hierarchical model for quantitative and qualitative responses
    By Lulu Kang, Xiaoning Kang, Xinwei Deng and Ran Jin
    In many science and engineering systems both quantitative and qualitative output observations are collected. If modeled separately the important relationship between the two types of responses is ignored. In this article, we propose a Bayesian hierarchical modeling framework to jointly model a continuous and a binary response. Compared with the existing methods, the Bayesian method overcomes two restrictions. First, it solves the problem in which the model size (specifically, the number of parameters to be estimated) exceeds the number of observations for the continuous response. We use one example to show how such a problem can easily occur if the design of the experiment is not proper; all the frequentist approaches would fail in this case. Second, the Bayesian model can provide statistical inference on the estimated parameters and predictions, whereas it is not clear how to obtain inference using the latest method proposed by Deng and Jin (2015 Deng, X., and R. Jin. 2015. “QQ models: Joint modeling for quantitative and qualitative quality responses in manufacturing systems.” Technometrics 57 (3):320–331). which jointly models the two responses via constrained likelihood. We also develop a Gibbs sampling scheme to generate accurate estimation and prediction for the Bayesian hierarchical model. Both the simulation and the real case study are shown to illustrate the proposed method.
  • Sequential design of an injection molding process using a calibrated predictor
    By Po-Hsu Allen Chen, María G. Villarreal-Marroquín, Angela M. Dean, Thomas J. Santner, Rachmat Mulyana and José M. Castro
    This article optimizes an injection molding process using an efficient sequential design methodology. The goal is to set the process control variables to minimize the shrinkages of a selected collection of injection molded parts. This multiobjective optimization problem is solved by finding those process control variable settings that are Pareto minimizing values (i.e., process settings for which none of the shrinkages of the parts can be decreased by an alternative process setting without increasing the shrinkages of other parts). The sequential design uses an expected improvement criterion to guide updates. The shrinkages are estimated by a calibrated predictor of the process mean shrinkage. The calibration is based on observations of the manufacturing process supplemented by computer runs of a commercial simulator code that mimics the manufacturing process.


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