Human-AI Collaboration in Quality Control with Augmented Manufacturing Analytics

被引：8
|
作者：
Bousdekis, Alexandros [1 ]
Wellsandt, Stefan [2 ]
Bosani, Enrica [3 ]
Lepenioti, Katerina [1 ]
Apostolou, Dimitris [1 ]
Hribernik, Karl [2 ]
Mentzas, Gregoris [1 ]
机构：
[1] Natl Tech Univ Athens NTUA, Inst Commun & Comp Syst ICCS, Informat Management Unit IMU, Athens, Greece
[2] Univ Bremen, BIBA Bremer Inst Prod & Logist GmbH, Bremen, Germany
[3] Whirlpool EMEA, Benton Harbor, MI USA
来源：
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT IV | 2021年 / 633卷
基金：
欧盟地平线“2020”;
关键词：
Quality control; Augmented analytics; Human-AI collaboration; PRESCRIPTIVE ANALYTICS; OPERATOR; 4.0; BIG DATA; MANAGEMENT; INTELLIGENCE; ASSISTANTS;
D O I：
10.1007/978-3-030-85910-7_32
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Augmented analytics is an emerging topic which deals with the enhancement of analytics with conversational interfaces as well as the exploitation of the human knowledge representation through intelligent digital assistants allowing users to easily interact with data and insights. The communication with the user by voice poses new challenges to the development and execution of data analytics services. In this paper, we outline a framework for implementing quality analytics for decision augmentation through optimized human-AI interaction. Our approach aims to reduce the number of quality issues through fast, mobile, and easy access to quality predictions for products and processes. An application case is the production of white goods is presented.
引用
收藏
页码：303 / 310
页数：8
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