注塑制品质量参数在线检测、建模与优化方法研究
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摘要
随着注塑成型技术的不断完善,人们对注塑成型的了解不断加深,对注塑制品质量的要求也不断提高。然而,在现有工艺和设备条件下,落后的注塑制品检测技术成为提高注塑制品质量的主要阻力。因此,研究快速、准确的注塑制品质量检测相关方法,获取制品的质量信息是解决上述问题的关键。注塑制品在成型过程中,经历诸多复杂的阶段,在不同的阶段,制品的质量受到不同注塑过程参数的影响。因此,研究制品在生产过程中的过程参数与制品质量之间的关系；并利用该关系获取用于生产满足用户设定的制品质量要求的注塑过程参数是提高制品质量的重要保证。基于此,本文针对注塑制品质量参数在线检测、建模以及优化等相关方法展开研究：
(1)在注塑制品质量参数在线检测方面,本文利用基于机器视觉的检测系统实现对注塑制品图像信息的获取；在此基础上,实现了对注塑制品形状缺陷以及尺寸指标的检测。在形状缺陷检测方面,针对用户对精度要求的不同,分别提出基于面积以及基于差影法的注塑制品形状缺陷检测方法；针对检测过程中注塑制品轮廓提取问题,提出一种改进主动轮廓模型的注塑制品轮廓提取方法,该方法能够有效获取到图像中注塑制品的轮廓信息；对于注塑制品图像配准问题中现有基于虚拟三角形的特征匹配方法存在的问题,提出一种改进的虚拟三角形方法,旨在提高图像配准中特征匹配方法的准确程度。在尺寸指标的检测方面,研究了摄像机针孔模型以及镜头畸变模型；并分别采用具有较强针对性的摄像机标定方法实现了对上述模型的标定；针对标定过程中模板特征点检测问题,提出一种黑白棋盘格角点检测方法,该方法能够自动获取到棋盘格图像中的角点信息。
(2)在注塑制品质量参数建模方面,本文研究了基于数据驱动的建模方法。针对基于神经网络的建模方法存在的泛化能力较差的问题,提出一种基于神经网络集成的注塑制品质量建模方法,该方法将Bagging方法与负相关学习方法相结合,能够有效提高集成后神经网络的泛化能力：针对基于数据驱动的建模方法建立在大量数据的基础上,而通过实际生产过程产生试验数据的方式存在需要付出较高试验代价的问题,提出一种新的注塑制品质量建模方法,该方法首先利用计算机辅助工程软件提供(?)数据建立基于神经网络集成的模型,在此基础上,结合基于模型迁移的建模方法(?)基本思想建立注塑过程参数与制品质量之间的关系模型,能够有效减少建模过程(?)所付出的试验代价。
(3)在注塑制品质量参数优化方面,本文研究了应用上述建立的注塑过程参数与制品(?)量之间的关系模型代替实际生产过程解决注塑过程参数优化问题。然而,由于上(?)建模方法在建模过程中考虑到模型的全局准确性,使得利用上述模型所获取到的(?)优注塑过程参数无法在实际应用中满足用户要求。针对这个问题,本文提出采用(?)泛应用于电磁优化问题的空间映射方法优化实际生产过程中的注塑过程参数；并(?)对应用过程中存在的需要付出较高试验代价的问题,提出改进空间映射方法,该(?)法能够有效降低注塑过程参数优化过程中所付出的试验代价。同时,由于注塑制(?)在生产过程中常常受到噪声等因素的影响,因此有必要通过研究鲁棒注塑过程参(?)优化方法来保证注塑制品的合格率。然而,由于现有鲁棒注塑过程参数优化方法(?)在需要付出较高试验代价等问题,因此本文提出一种基于两步法的注塑过程参数(?)化方法,从而有效减少了获取鲁棒注塑过程参数的过程中所付出的试验代价。最后,对本文主要研究工作及取得的研究成果进行了总结,并对文中所涉及的主(?)方法的未来发展进行了展望,提出作者的建议。
Due to the continuously improvements of the injection molding technology, people are more familiar with the injection molding process and require higher quality of injection molding product. On the basis of the current technology and equipment, the laggard inspection technology is the main resistance to improve the quality of injection molding product. Based on a great deal of studies, it can be overcome by using fast and accurate quality inspection methods to obtain the quality information of injection molding product online. During the injection molding process, the product quality is affected by many injection molding parameters. Base on the model between injection molding parameters and product quality, optimized injection molding parameters can be obtained to produce products whose quality satisfy customers'needs. Therefore, the dissertation focuses on researching quality parameter inspection online, modeling and optimization of injection molding product.
(1) On the research of quality parameter inspection online of injection molding product, shape defect and size index of injection molding product are detected by machine vision inspection system. An area based inspection method and a difference image algorithm based inspection method are proposed for satisfying different requirements of users. An improved active contour model algorithm is proposed to extract contour of injection molding product from its image. An improved virtual triangle method is proposed to improve the accuracy of feature matching in the process of image registration. Pin-hole model and lens distortion model are applied to construct the relation between space coordinates and image coordinates. Robust metric calibration method is applied to calibrate the camera models above. A black and white X-corner detection method is proposed to detect X-corner in chess board image automatically.
(2) Data based modeling method is studied to construct the relation between injection molding parameters and product quality. A neural network (NN) ensemble approach is proposed to improve the generalization ability of ensemble neural network by combining BAGGING and negative correlation learning algorithm with a selection strategy. Computer aided engineering (CAE) software are applied to generate data for modeling, since high experiment costs are need if the data for modeling are generated by experiments on actual production process. However, CAE software cannot substitute actual production process. In this thesis, a new modeling method is proposed to improve the accuracy of NN ensemble model by assisting only few experiments on actual production process.
(3) The model obtained by using the proposed modeling method is applied for optimizing injection molding parameters. However, the optimized parameters obtained by using the above model cannot satisfy the requirement of users. In this thesis, a space mapping algorithm is applied to optimize injection molding parameters. An improved space mapping algorithm is proposed to save experiment costs. Meanwhile, since injection molding process is often affected by disturbances, robust injection molding parameters optimization method is studied to guarantee the quality of injection molding product. Since high experiment costs should be paid by using the existing robust parameters optimization method, a two-stage approach for optimizing the robust injection molding parameters is proposed to save experiment costs.
At the end of the dissertation, the potential further research direction in the area of quality parameter inspection, modeling and optimization of injection molding product is discussed after summarizing the whole work.
(1)在注塑制品质量参数在线检测方面,本文利用基于机器视觉的检测系统实现对注塑制品图像信息的获取；在此基础上,实现了对注塑制品形状缺陷以及尺寸指标的检测。在形状缺陷检测方面,针对用户对精度要求的不同,分别提出基于面积以及基于差影法的注塑制品形状缺陷检测方法；针对检测过程中注塑制品轮廓提取问题,提出一种改进主动轮廓模型的注塑制品轮廓提取方法,该方法能够有效获取到图像中注塑制品的轮廓信息；对于注塑制品图像配准问题中现有基于虚拟三角形的特征匹配方法存在的问题,提出一种改进的虚拟三角形方法,旨在提高图像配准中特征匹配方法的准确程度。在尺寸指标的检测方面,研究了摄像机针孔模型以及镜头畸变模型；并分别采用具有较强针对性的摄像机标定方法实现了对上述模型的标定；针对标定过程中模板特征点检测问题,提出一种黑白棋盘格角点检测方法,该方法能够自动获取到棋盘格图像中的角点信息。
(2)在注塑制品质量参数建模方面,本文研究了基于数据驱动的建模方法。针对基于神经网络的建模方法存在的泛化能力较差的问题,提出一种基于神经网络集成的注塑制品质量建模方法,该方法将Bagging方法与负相关学习方法相结合,能够有效提高集成后神经网络的泛化能力：针对基于数据驱动的建模方法建立在大量数据的基础上,而通过实际生产过程产生试验数据的方式存在需要付出较高试验代价的问题,提出一种新的注塑制品质量建模方法,该方法首先利用计算机辅助工程软件提供(?)数据建立基于神经网络集成的模型,在此基础上,结合基于模型迁移的建模方法(?)基本思想建立注塑过程参数与制品质量之间的关系模型,能够有效减少建模过程(?)所付出的试验代价。
(3)在注塑制品质量参数优化方面,本文研究了应用上述建立的注塑过程参数与制品(?)量之间的关系模型代替实际生产过程解决注塑过程参数优化问题。然而,由于上(?)建模方法在建模过程中考虑到模型的全局准确性,使得利用上述模型所获取到的(?)优注塑过程参数无法在实际应用中满足用户要求。针对这个问题,本文提出采用(?)泛应用于电磁优化问题的空间映射方法优化实际生产过程中的注塑过程参数；并(?)对应用过程中存在的需要付出较高试验代价的问题,提出改进空间映射方法,该(?)法能够有效降低注塑过程参数优化过程中所付出的试验代价。同时,由于注塑制(?)在生产过程中常常受到噪声等因素的影响,因此有必要通过研究鲁棒注塑过程参(?)优化方法来保证注塑制品的合格率。然而,由于现有鲁棒注塑过程参数优化方法(?)在需要付出较高试验代价等问题,因此本文提出一种基于两步法的注塑过程参数(?)化方法,从而有效减少了获取鲁棒注塑过程参数的过程中所付出的试验代价。最后,对本文主要研究工作及取得的研究成果进行了总结,并对文中所涉及的主(?)方法的未来发展进行了展望,提出作者的建议。
Due to the continuously improvements of the injection molding technology, people are more familiar with the injection molding process and require higher quality of injection molding product. On the basis of the current technology and equipment, the laggard inspection technology is the main resistance to improve the quality of injection molding product. Based on a great deal of studies, it can be overcome by using fast and accurate quality inspection methods to obtain the quality information of injection molding product online. During the injection molding process, the product quality is affected by many injection molding parameters. Base on the model between injection molding parameters and product quality, optimized injection molding parameters can be obtained to produce products whose quality satisfy customers'needs. Therefore, the dissertation focuses on researching quality parameter inspection online, modeling and optimization of injection molding product.
(1) On the research of quality parameter inspection online of injection molding product, shape defect and size index of injection molding product are detected by machine vision inspection system. An area based inspection method and a difference image algorithm based inspection method are proposed for satisfying different requirements of users. An improved active contour model algorithm is proposed to extract contour of injection molding product from its image. An improved virtual triangle method is proposed to improve the accuracy of feature matching in the process of image registration. Pin-hole model and lens distortion model are applied to construct the relation between space coordinates and image coordinates. Robust metric calibration method is applied to calibrate the camera models above. A black and white X-corner detection method is proposed to detect X-corner in chess board image automatically.
(2) Data based modeling method is studied to construct the relation between injection molding parameters and product quality. A neural network (NN) ensemble approach is proposed to improve the generalization ability of ensemble neural network by combining BAGGING and negative correlation learning algorithm with a selection strategy. Computer aided engineering (CAE) software are applied to generate data for modeling, since high experiment costs are need if the data for modeling are generated by experiments on actual production process. However, CAE software cannot substitute actual production process. In this thesis, a new modeling method is proposed to improve the accuracy of NN ensemble model by assisting only few experiments on actual production process.
(3) The model obtained by using the proposed modeling method is applied for optimizing injection molding parameters. However, the optimized parameters obtained by using the above model cannot satisfy the requirement of users. In this thesis, a space mapping algorithm is applied to optimize injection molding parameters. An improved space mapping algorithm is proposed to save experiment costs. Meanwhile, since injection molding process is often affected by disturbances, robust injection molding parameters optimization method is studied to guarantee the quality of injection molding product. Since high experiment costs should be paid by using the existing robust parameters optimization method, a two-stage approach for optimizing the robust injection molding parameters is proposed to save experiment costs.
At the end of the dissertation, the potential further research direction in the area of quality parameter inspection, modeling and optimization of injection molding product is discussed after summarizing the whole work.
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