A Method to Evaluate Maturity of Intelligent Hobbing Machine
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摘要: 掌握智能滚齿机的智能化状态,改进智能滚齿机的薄弱环节,是提升滚齿机智能化程度的重要途径。通过借鉴能力成熟度模型集成技术,构建智能滚齿机成熟度模型并给出其主要特征以描述滚齿机智能化发展进程,根据滚齿机的智能特性建立智能滚齿机成熟度综合评价指标体系。在此基础上,提出一种区间层次分析法和模糊综合评价相结合的智能滚齿机成熟度评价方法,其量化结果能合理地评估智能滚齿机成熟度,直观得到滚齿机智能化的改进方向。通过对企业某台智能滚齿机的评价,验证所提方法的有效性。Abstract: It is an important way to improve the intelligent degree of hobbing machine by grasping the intelligent state and improving the weak link of intelligent hobbing machine. To describe the smart development process of hobbing machine, a maturity model for smart hobbing machine was built by referring capability maturity model integration technology. On the other hand, a smart hobbing machine maturity comprehensive evaluation index system was established to describe the characteristics of intelligent hobbing machine. In order to evaluate the maturity of smart hobbing machines, a method combined with interval analytic hierarchy process and fuzzy comprehensive evaluation was proposed. The quantitative evaluation results can reasonably assess the maturity of smart hobbing machines and point out the improvement direction for smarter. An example with a smart hobbing machine in a business verified the present effectiveness.
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表 1 准则层各属性对目标层的重要度及权重
p E D C I M K P [1.0, 1.0] [0.95, 1.2] [0.7, 0.9] [0.6, 0.8] [0.65, 0.85] [0.8, 1.05] [0.95, 1.2] E [1.0, 1.0] [0.75, 0.6] [0.6, 0.75] [0.8, 1.1] [0.9, 1.02] [0.85, 1.15] D [1.0, 1.0] [0.9, 1.25] [0.95, 1.15] [1.1, 1.3] [1.2, 1.3] C [1.0, 1.0] [0.9, 1.3] [1.05, 1.4] [1.25, 1.4] I [1.0, 1.0] [0.95, 1.2] [1.0, 1.3] M [1.0, 1.0] [0.95, 1.15] K [1.0, 1.0] 表 2 智能规划与决策能力指标层的重要度及权重
P1 P2 P3 P4 P1 [1.0, 1.0] [0.85, 1.05] [0.8, 1.1] [0.9, 1.05] P2 [1.0, 1.0] [0.95, 1.15] [0.8, 1.05] P3 [1.0, 1.0] [0.95, 1.2] P4 [1.0, 1.0] 表 3 智能滚齿机成熟度模糊综合评价表
评价指标 权重ω 评价因素 权重ω1 指标评价等级 V1 V2 V3 V4 V5 智能规划与决策P 0.125 加工任务管理与规划能力P1 0.241 0.5 0.2 0.2 0.1 0 滚齿工艺参数自主优化决策能力P2 0.251 0.4 0.3 0.1 0.1 0.1 数控程序自动编制能力P3 0.256 0.3 0.3 0.2 0.2 0 刀、夹具及工件自动匹配与监管能力P4 0.253 0.4 0.2 0.2 0.1 0.1 可持续制造评估与优化E 0.123 虚拟加工E1 0.358 0.3 0.3 0.2 0.1 0.1 云制造环境下资源优化选择E2 0.306 0.2 0.3 0.2 0.1 0.2 生产节能调度E3 0.336 0.3 0.3 0.1 0.2 0.1 智能检测及诊断能力D 0.167 滚齿机实时状态分析与预警D1 0.236 0.2 0.2 0.2 0.2 0.2 知识自学习与共享学习D2 0.241 0.4 0.2 0.2 0.2 0 设备维修维护知识库维护D3 0.282 0.3 0.2 0.2 0.1 0.2 故障预报分析和信息化管理D4 0.231 0.3 0.4 0.2 0.1 0 自适应优化控制能力C 0.170 负载自动检测与NC代码自动调节C1 0.184 0.2 0.3 0.3 0.1 0.1 误差实时检测及智能补偿C2 0.213 0.3 0.3 0.2 0.1 0.1 工件实时定位及碰撞预防控制C3 0.195 0.4 0.3 0.2 0.1 0 辅助装置低碳运行智能控制C4 0.195 0.3 0.2 0.2 0.2 0.1 加工过程能效监测与节能优化控制C5 0.215 0.2 0.3 0.2 0.2 集成与互联互通能力I 0.151 智能传感器与多源信息融合能力I1 0.283 0.3 0.3 0.2 0.1 0.1 网络设备层集成能力I2 0.245 0.3 0.2 0.2 0.2 0.1 信息层的集成与互联互通能力I3 0.240 0.5 0.3 0.1 0.1 0 接口开放性与互操作能力I4 0.234 0.4 0.2 0.2 0.1 0.1 故障智能维护能力M 0.136 实时故障智能检测与诊断M1 0.253 0.4 0.3 0.1 0.1 0.1 可靠性评估与故障智能预测预警M2 0.244 0.5 0.2 0.2 0.1 0 故障自修复和远程维修M3 0.266 0.2 0.2 0.2 0.2 0.2 基于3D的故障维修拆卸与组装M4 0.238 0.3 0.3 0.2 0.1 0.1 知识智能维护能力K 0.127 知识自学习与知识共享K1 0.340 0.2 0.4 0.2 0.1 0.1 故障诊断专家系统维护K2 0.322 0.3 0.3 0.3 0.1 0 调整、控制及优化算法维护K3 0.340 0.4 0.3 0.1 0.1 0.1 -
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