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Robotic Manufacturing System Throughput Prediction Methodology Development Based on Failure Data of an Automotive Assembly.

机译：基于汽车装配故障数据的机器人制造系统吞吐量预测方法开发。

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摘要

US automotive industry has seen unprecedented worst market condition in recent years. According to Harbour report (2008), even though the car companies were able to improve labor hours per vehicle (HPV) target year after year which is reduced to 20 hours, but their profit margin is not. The average labor cost is reduced from ;Based on through literature review, there is a need for the development of throughput prediction methodology for the automotive robotic manufacturing process. The primary purpose of this dissertation research is to evaluate the past plant failure data, model the series, select and improve appropriate prediction model methodologies to improve body shop throughput based on past plant failure data. The simulation model developed in this dissertation could be applied to individual equipment failure prediction. It is proved through research findings that the throughput could be improved using simulation methodology using plant MTTR and MTBF data.;This dissertation seeks to establish the feasibility of using ARMA and ANN models to improve the body shop robotic manufacturing process throughput. The key findings are that the throughput can be successfully predicted using ARMA and ANN models for the automotive assembly process based on plant failure data. Quantitative comparative research studies have been done for both ARMA and ANN model prediction accuracy using Mean absolute percentage error (MAPE) as measure. Based on the above ARMA-ANN models, this dissertation developed an optimization prediction model for plant throughput based on failure data. The optimization model prediction was verified and validated with actual plant throughput data. Based on this dissertation results, the application of the developed optimization throughput prediction model would yield high economic benefits to the company. The proposed approach could be applied in similar industrial applications.
机译：近年来，美国汽车业经历了前所未有的最坏市场情况。根据Harbor报告（2008年），尽管汽车公司能够将年均目标车辆时间（HPV）提高到20小时，但利润率却没有。平均劳动力成本从降低了；基于文献回顾，需要开发用于汽车机器人制造过程的吞吐量预测方法。本论文研究的主要目的是评估过去的工厂故障数据，对系列进行建模，选择和改进适当的预测模型方法，以基于过去的工厂故障数据提高车身车间的生产能力。本文建立的仿真模型可以应用于单个设备的故障预测。通过研究结果证明，使用工厂MTTR和MTBF数据的模拟方法可以提高生产能力。本论文旨在建立使用ARMA和ANN模型来提高车身车间机器人制造过程生产能力的可行性。关键发现是，基于工厂故障数据，可以使用ARMA和ANN模型在汽车装配过程中成功预测吞吐量。已经使用平均绝对百分比误差（MAPE）作为度量，对ARMA和ANN模型的预测准确性进行了定量的比较研究。在以上ARMA-ANN模型的基础上，本文基于故障数据建立了工厂产能优化预测模型。优化模型预测已通过实际工厂产能数据进行了验证和验证。基于本文的研究结果，所开发的优化吞吐量预测模型的应用将为公司带来很高的经济效益。所提出的方法可以应用于类似的工业应用中。

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  • 作者

    Pandian, Annamalai.;

  • 作者单位

    Lawrence Technological University.;

  • 授予单位 Lawrence Technological University.;
  • 学科 Engineering Automotive.;Engineering Robotics.;Engineering Mechanical.
  • 学位 D.E.M.S.
  • 年度 2010
  • 页码 252 p.
  • 总页数 252
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 世界史;
  • 关键词

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