Quality Control via Model-Free Optimization for a Type of Batch Process with a Short Cycle Time and Low Operational Cost

Kong, Xiangping; Yang, Yi; Chen, Xi; Shao, Zili; Gao, Furong

doi:10.1021/ie1016927

Cited by 25 publications

(31 citation statements)

References 28 publications

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“…Using the scheme developed in previous parts, in this section, we present the results from implementing the part weight optimization and compare its performance against a method proposed by Kong et al (2011). Under this situation, initial set-point of packing pressure and packing time are randomly set to 38bar and 5s, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…6, the part weight results obtained from the proposed scheme is compared with Kong's algorithm (Kong et al, 2011). With Kong's method, the optimal part weight is 11.73g.…”

Section: Resultsmentioning

confidence: 99%

“…However, this algorithm relied on the strong assumption that several identical units were available, which was unrealistic for industrial process. Further, Kong et al (2011) presented an optimization method whose gradient information can be obtained through stochastic perpetuation of the process variables without the limitation of physical equipment. However, the above methods are implemented under the implicit precondition that the set-point signal can be reached quickly.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Optimization Based on Transition Tracking Analysis for Batch Processes

2015

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Process optimization is an important issue for raising product quality and ensuring safety in batch processes and it is usually conducted at the point only when set-points are reached. In this work, process dynamic shift results from set-point tracking control, termed as transition tracking process, is detailedly analyzed in an integrated optimization framework, which also synthesizes the multi-stage characteristic of batch processes. Establishment of the regression relationship between process variables and quality indexes becomes possible and gradient directions are updated iteratively. The effacy of the proposed scheme is illustrated on the injection molding, which is a typical multi-stage batch process.

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Section: Resultsmentioning

confidence: 99%

“…6, the part weight results obtained from the proposed scheme is compared with Kong's algorithm (Kong et al, 2011). With Kong's method, the optimal part weight is 11.73g.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated Optimization Based on Transition Tracking Analysis for Batch Processes

2015

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“…In practice, after the design stage, quality control of insulators is mainly achieved via the tuning of process parameters. Traditional quality control methods for batch processes can be categorized into three different types, that is, trial-and-error, design of experiments (DOE), simulation optimization based on product quality models (i.e., model-based optimization, MBO) [5]. Generally speaking, the trial-and-error and the DOE are inefficient, inaccurate, and experience-dependent.…”

Section: Introductionmentioning

confidence: 99%

Quality Control for Medium Voltage Insulator via a Knowledge-Informed SPSA Based on Historical Gradient Approximations

et al. 2020

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Medium voltage insulators are essential and versatile components in electrical engineering. Quality control of the manufacturing process for the insulators has a significant role in their economic production and reliable operation. As the quality of medium voltage insulator is mainly affected by the process parameters of the automatic pressure gelation process (APG), the optimal process settings are required to achieve a satisfactory quality target. However, traditional process parameters’ optimization methods are often cumbersome and cost-consuming. Moreover, the operational cost of APG for insulator production is relatively high. Therefore, the determination of the optimal settings becomes a significant challenge for the quality control of insulators. To address the above issues, an idea of knowledge-informed optimization was proposed in this study. Based on the above idea, a knowledge-informed simultaneous perturbation stochastic approximation (SPSA) methodology was formulated to reduce the optimization costs, and thus improve the efficiency of quality control. Considering the characteristics of SPSA, the historical gradient approximations generated during the optimization process were utilized to improve the accuracy of gradient estimations and to tune the iteration step size adaptively. Therefore, an implementation of a quality control strategy of knowledge-informed SPSA based on historical gradient approximations (GK-SPSA) was thus constructed. In this paper, the GK-SPSA-based quality control method was applied to the weight control of a kind of post insulators. The experimental simulation results showed that the revised knowledge-informed SPSA was effective and efficient on quality control of medium voltage insulators.

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“…The MFO has been successfully applied to control and optimize part weight, part dimensions, and the focal length of molded products. Kong et al [18] and Zhu et al [19] proposed a novel MFO method for batch processes with short cycle times and low operational costs. In order to eliminate defects such as shrinkage and flash, Yang et al [20] proposed a systematic method that combined digital image processing and MFO to solve such problems, and the MFO used online measurement as feedback to determine the optimal settings.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

Liu

Chen

Lin

et al. 2017

Mathematical Problems in Engineering

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Injection molding process parameters (IMPP) have a significant effect on the optical performance and surface waviness of precision plastic optical lens. This paper presents a set of procedures for the optimization of IMPP, with haze ratio (HR) reflecting the optical performance and peak-to-valley 20 (PV 20 ) reflecting the surface waviness as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the results were analyzed by ANOVA to screen out the IMPP having a significant effect on the objectives. Then, the 3 4 full-factor experiment was conducted on the key IMPP, and the experimental results were used as the training and testing samples. The BPNN algorithm and the M-SVR algorithm were applied to establish the mapping relationships between the IMPP and objectives. Finally, the multiple-objective optimization was performed by applying the nondominated sorting genetic algorithm (NSGA-II), with the built M-SVR models as the fitness function of the objectives, to obtain a Pareto-optimal set, which improved the quality of plastic optical lens comprehensively. Through the experimental verification on the optimization results, the mean prediction error (MPE) of HR and PV 20 is 7.16% and 9.78%, respectively, indicating that the optimization method has high accuracy.

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Quality Control via Model-Free Optimization for a Type of Batch Process with a Short Cycle Time and Low Operational Cost

Cited by 25 publications

References 28 publications

Integrated Optimization Based on Transition Tracking Analysis for Batch Processes

Integrated Optimization Based on Transition Tracking Analysis for Batch Processes

Quality Control for Medium Voltage Insulator via a Knowledge-Informed SPSA Based on Historical Gradient Approximations

Multiobjective Optimization of Injection Molding Process Parameters for the Precision Manufacturing of Plastic Optical Lens

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