基于数字孪生的化妆品生产线机器人化重构框架与风险管控策略

赵丽宁; 张凯

doi:10.3969/j.issn.1007-7375.250044

工业工程 ›› 2026, Vol. 29 ›› Issue (3) : 94 -103. DOI: 10.3969/j.issn.1007-7375.250044

工业大数据与智能决策

基于数字孪生的化妆品生产线机器人化重构框架与风险管控策略

赵丽宁 ¹^,² ,
张凯 ²^,³

作者信息 +

A Digital Twin-Driven Framework and Risk Control Strategy for Robotic Reconfiguration of Cosmetic Production Lines

Lining Zhao ¹^,² ,
Kai Zhang ²^,³

Author information +

文章历史 +

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摘要

为应对机器人化智能重构化妆品生产线面临高风险、长周期、性能波动及决策复杂等严峻挑战，研究提出并验证了一种数字孪生驱动的系统性渐进式重构框架。该框架集成了全面的风险管控策略。通过构建系统的综合表征与动态过程模型 (采用“物理−逻辑”双维度表征和基于时间−属性 Petri 网的方法)，实施以迭代优化、虚拟验证和动态调整为核心的渐进式重构，并系统性集成了基于失效模式与影响分析 (FMEA) 的多层次风险管控机制，以实现重构风险的量化评估与前置干预。以某化妆品生产线为案例的半实物仿真验证 (N =20 次独立实验) 表明，与传统一次性重构方案相比，所提出的数字孪生渐进式策略 (DTPS) 显著降低了重构风险指数 (从 0.70 ± 0.05 降至 0.45 ± 0.03)、系统波动度 (从 38.3% ± 3.7% 降至 25.1% ± 2.2%) 及重构总时间 (从 72.5 ± 2.8 h 缩短至 50.3 ± 1.9 h)。同时，重构后生产线平衡率提升至 87.00% (原始 72.00%)，人工需求减至 39 人 (原始 47 人)，每分钟产能提升至 64 瓶 (原始 57 瓶)，投资回收期约为 30 ~ 32 个月。研究表明，本文提出的框架为机器人化系统重构提供了一种更为全面、风险可控且能有效平衡多重目标的解决方案。

Abstract

Robotic reconfiguration of cosmetic production lines faces significant challenges, including high risk, long implementation cycles, performance fluctuations, and complex decision-making. To address these issues, this study proposes and validates a digital twin-driven systematic progressive reconfiguration framework incorporating a comprehensive risk control strategy. By constructing comprehensive system representation and dynamic process models using physical-logical dual-dimension representation and time-attribute Petri nets, the proposed framework achieves progressive reconfiguration through iterative optimization, virtual verification, and dynamic adjustment. It also systematically integrates a multi-level risk control mechanism based on failure mode and effects analysis (FMEA) for quantitative risk assessment and proactive intervention. Semi-physical simulation validation (N=20 independent trials) on a cosmetic production line demonstrates that, compared with traditional one-time reconfiguration schemes, the proposed digital twin-driven progressive strategy (DTPS) significantly reduces the reconfiguration risk index (from 0.70±0.05 to 0.45±0.03), system fluctuation level (from 38.3%±3.7% to 25.1%±2.2%), and total reconfiguration time (from 72.5±2.8 hours shortened to 50.3±1.9 hours). In addition, the reconfigured production line achieves a balance rate of 87.00% (from 72.00%), reduces labor demand to 39 operators (from 47 operators), increases production capacity to 64 bottles/min (from 57 bottles/min), and yields an investment payback period of approximately 30~32 months. The results indicate that the proposed framework provides a more comprehensive and risk-controllable solution for robotic reconfiguration while effectively balancing multiple objectives.

关键词

数字孪生 / 渐进式重构 / 机器人化系统 / 系统框架 / 风险管控 / 失效模式与影响分析 (FMEA) / 化妆品生产

Key words

digital twin / progressive reconfiguration / robotic system / system framework / risk control / failure mode and effects analysis (FMEA) / cosmetics production

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赵丽宁,张凯. 基于数字孪生的化妆品生产线机器人化重构框架与风险管控策略[J]. 工业工程, 2026, 29(3): 94-103 DOI:10.3969/j.issn.1007-7375.250044

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