An Intelligent Product Service System for Adaptive Maintenance of Engineered-to-Order Manufacturing Equipment Assisted by Augmented Reality

被引:23
作者
Angelopoulos, John [1 ]
Mourtzis, Dimitris [1 ]
机构
[1] Univ Patras, Dept Mech & Aeronaut Engn, Lab Mfg Syst & Automat, Rion 26504, Greece
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 11期
基金
欧盟地平线“2020”;
关键词
Intelligent Product Service System; Industrial PSS; Product Service System (PSS); Augmented Reality; Engineered-to-Order; maintenance; PREDICTIVE MAINTENANCE; BUSINESS MODELS; INDUSTRY; 4.0; BIG DATA; PSS; DESIGN; FRAMEWORK;
D O I
10.3390/app12115349
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Personalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled by individualization trends and enabled by increasing digitalization, has the potential to go beyond current mass customization. Although this evolution has enabled engineers to gain useful insight for the production, the machine status, the quality of products, etc., machines have become more complex. Thus, Maintenance Repair and Overhaul (MRO) operations should be undertaken by specialized personnel. Additionally, Augmented Reality (AR) can support remote maintenance assistance to handle unexpected malfunctions. Moreover, due to advances regarding Product Service Systems (PSS), manufacturing companies are offering many services to improve user experience. Consequently, in this manuscript the design and development of a method based on the principles of servitization for the provision of an intelligent and adaptable maintenance service assisted by AR are presented. The contribution of the manuscript extends to the provision of an optimization algorithm for adapting the schedules of the stakeholders based on the energy supplier predictions. The developed method was tested and validated on an industrial case study of injection mold maintenance, achieving 11% energy reduction, 50% less time for mold inspection, and a 20% rise in on-time mold deliveries.
引用
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页数:21
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