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An artificial neural network approach to laser-based direct part marking of data matrix symbols.

机译：一种基于人工神经网络的数据矩阵符号基于激光的直接零件标记方法。

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摘要

Certain applications have recently appeared in industry where a traditional bar code printed on a label will not survive because the item to be tracked has to be exposed to harsh environments. Laser direct-part marking is a manufacturing process used to create permanent marks on a substrate that could help to alleviate this problem. In this research, a 532 tun laser was utilized to create a direct-part marked Data Matrix symbol onto carbon steel substrates with different carbon content. The quality of the laser marked Data Matrix symbol was then evaluated according to the criteria outlined in the ISO/IEC 16022 bar code technology specification for Data Matrix.;Several experiments were conducted to explore the effects that different parameters have on the quality of the laser direct-part marked symbols. First, an experiment was conducted to investigate the effect of two different laser tool path patterns. In later experiments, parameters such as type of carbon steel, percent of laser tool path overlap, profile speed, average power and frequency were found to have significant effects on the quality of laser direct-part marked Data Matrix symbols. The analysis of the results indicated that contrast and print growth were the critical standard performance measures that limited laser direct-part marked Data Matrix symbols from achieving a higher final grade. No significant effects were found with respect to other standard performance measures (i.e., encode, axial uniformity, and unused error correction).;Next, the experimental data collected for contrast and print growth was utilized as training, validation and testing data sets in the modeling of artificial neural networks for the laser direct-part marking process. Two performance measures (i.e., mean squared error and correlation coefficient) were employed to assess the performance of the artificial neural network models. Single-output artificial neural network models corresponding to a specific performance measure were found to have good learning and predicting capabilities. The single-output artificial neural network models were compared to equivalent multiple linear regression models for validation purposes. The prediction capability of the single-output artificial neural network models with respect to laser direct-part marking of Data Matrix symbols on carbon steel substrates was superior to that of the multiple linear regression models.
机译：最近在工业中出现了某些应用，其中标签上印刷的传统条形码将无法生存，因为要跟踪的物品必须暴露在恶劣的环境中。激光直接零件打标是一种用于在基材上创建永久性标记的制造工艺，可以帮助缓解此问题。在这项研究中，使用了532 tun激光在不同碳含量的碳钢基底上创建了直接标记的数据矩阵符号。然后根据ISO / IEC 16022数据矩阵条形码技术规范中概述的标准评估激光标记的数据矩阵符号的质量。;进行了多次实验以探索不同参数对激光质量的影响直接零件标记符号。首先，进行了一项实验，以研究两种不同的激光工具路径图案的影响。在随后的实验中，发现诸如碳钢类型，激光工具路径重叠的百分比，轮廓速度，平均功率和频率等参数对激光直接零件标记的数据矩阵符号的质量有重大影响。结果分析表明，对比度和印刷增长是关键的标准性能指标，这些指标限制了激光直面零件标记的Data Matrix符号获得更高的最终等级。相对于其他标准性能指标（例如，编码，轴向均匀性和未使用的纠错），没有发现显着影响。接着，将用于对比和打印增长的实验数据用作训练，验证和测试数据集。激光直接零件打标过程的人工神经网络建模。两种性能指标（即均方误差和相关系数）用于评估人工神经网络模型的性能。发现与特定性能度量相对应的单输出人工神经网络模型具有良好的学习和预测能力。为了验证，将单输出人工神经网络模型与等效的多个线性回归模型进行了比较。单输出人工神经网络模型对碳钢基体上数据矩阵符号的激光直接打标的预测能力优于多元线性回归模型。

著录项

  • 作者

    Jangsombatsiri, Witaya.;

  • 作者单位

    Oregon State University.;

  • 授予单位 Oregon State University.;
  • 学科 Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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