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The auto body process monitoring and the root cause diagnosis based on data-driven approaches are vital ways to improve the dimension quality of sheet metal assemblies. However, during the launch time of the process mass production with an off-line measurement strategy, the traditional statistical methods are difficult to perform process control effectively. Based on the powerful abilities in information fusion, a systematic Bayesian based quality control approach is presented to solve the quality problems in condition of incomplete dataset. For the process monitoring, a Bayesian estimation method is used to give out-of-control signals in the process. With the abnormal evidence, the Bayesian network (BN) approach is employed to identify the fixture root causes. A novel BN structure and the conditional probability training methods based on process knowledge representation are proposed to obtain the diagnostic model. Furthermore, based on the diagnostic performance analysis, a case study is used to evaluate the effectiveness of the proposed approach. Results show that the Bayesian based method has a better diagnostic performance for multi-fault cases. 相似文献
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现阶段船体板材零件理料过程中完全依靠人工进行零件特征和编码识别,不利于开展理料装备自动化和智能化作业.针对该现状,提出基于视觉识别的船体板材零件理料原型系统.通过工业相机拍摄零件图像,对零件特征进行识别,获取零件类型和位置信息;对零件编码进行识别,获取零件流向信息.然后将类型、位置和流向信息传输至理料控制软件处理,机器人根据指令完成理料过程.结果表明,提出的理料原型系统及工作流程能够替代人工完成船体零件理料过程,为后续实现理料作业的自动化和智能化运行及装备集成应用奠定了基础. 相似文献
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