定制家具板件数控钻孔作业时间预测模型

Prediction Model of Operation Time in Numerical Control Drilling of Custom Furniture Parts

【目的】为实现大规模定制家具更高效、更低碳的智能制造,有必要在自动化、连续化生产的基础上实施更精准的智慧生产决策,将生产管理由传统的批次级推进到板件级,以差异化的工序作业时间为基础开展生产调度。本研究通过分析数控钻孔作业时间的关键影响因素,构建可预测每一张板件数控钻孔作业时间的数学模型,在生产前获得较为精准的作业时间数据,解决定制家具板件差异化作业时间数据缺失的现实问题,为定制家具的板件级生产调度夯实数据基础。【方法】基于定制家具制造实际,从数控钻孔加工中心的作业逻辑着手,分析不同数据条件下影响数控钻孔作业时间的关键因素,通过MES(制造执行系统)从设备底层采集6万余条历史作业时间数据,构建解析程序提取关键影响因素,搭建使用Mish激活函数的3层人工神经网络模型,并引入动量和学习率衰减算法提高模型精度和运算效率,实现定制家具数控钻孔工序板件级作业时间的预测。【结果】不同数据条件下,人工神经网络模型可实现良好预测效果,其中基于数控程序提取的9维变量对平均作业时间的预测误差率为2.68%,具备对工序总体效能进行预测的能力;单一板件预测结果平均绝对误差为2.87 s,显著优于同等数据条件下的常规线性回归模型,对指导板件级精准调度具有实际意义。【结论】通过MES大量采集历史作业时间数据,构建解析程序,提取关键影响因素,基于制造大数据搭建3层人工神经网络模型,能够较精准预测板件级数控钻孔作业时间,为实现定制家具制造过程的设备与工艺优化、基于单张板件开展精准生产调度、构建家具制造数字孪生车间打下良好数据基础,推动智能制造在定制家具领域的实践。

【Objective】In order to achieve more efficient and low-carbon intelligent manufacturing of mass customization furniture,it is necessary to implement more accurate intelligent production decisions on the basis of automated and continuous production,promote the production management from the traditional batch level to the part level,and carry out production scheduling based on the differentiated process time.By analyzing the key influencing factors of the NC(numerical control)drilling operation time,this paper constructs a mathematical model that can predict the NC drilling operation time of each part,so as to obtain more accurate operation time data before production,solve the practical problem of the missing operation time of custom furniture,and lay a solid data foundation for the part level production scheduling of custom furniture.【Method】By conducting indepth research around the actual manufacturing of custom furniture,starting from the operation logic of the NC drilling machining center,the paper analyzes the key influencing factors of the NC drilling operation time under different data conditions,collects more than 60000 historical operation time data from the bottom of the equipment through MES(manufacturing execution system),constructs an analytic program to extract the key influencing factors,builds a three-layer artificial neural network model using the Mish activation function,and introduces momentum and learning rate attenuation algorithms to improve the model accuracy and operation efficiency.The prediction of the part level operation time in the NC drilling process of custom furniture is realized.【Result】Under different data conditions,good prediction results are achieved based on artificial neural network,among which the prediction error rate of the average working time of the 9-dimensional variables extracted based on the NC program is 2.68%,which has the ability to predict the overall efficiency of the process.The mean absolute error of single part prediction results is 2.87 s,which is significantly better than the conventional linear regression model under the same data conditions,and has practical significance for guiding the part level accurate scheduling.【Conclusion】By collecting a large amount of historical data through MES,constructing an analytic program,extracting the corresponding influencing factors,and building a three-layer artificial neural network based on the manufacturing big data,it is possible to accurately predict the part level operation time of NC drilling,thus laying a good data foundation for the optimization of equipment and process in the custom furniture manufacturing process,and promoting the practice of intelligent manufacturing in the field of custom furniture.