Enhancing data quality and process optimization for smart manufacturing lines in industry 4.0 scenarios.

Simon Paasche; Sven Groppe

doi:10.1145/3530050.3532928

Enhancing data quality and process optimization for smart manufacturing lines in industry 4.0 scenarios.

Simon Paasche, Sven Groppe

Bosch Automotive Electronics

12 Citations (Scopus)

Abstract

An essential component of today's industry is data, which is generated during manufacturing. The goal of industry 4.0 is efficient collection, processing and analysis of this data. In our work, we address these three tasks and present an extensible system to solve them. To the best of our knowledge, the combination of a consistency checker (CC) for data preparation and a digital twin (DT) for analysis activities represents a novel approach. Consistency checking in combination with a DT leads to increased data quality, which in turn has a positive effect on analyses, like reducing errors to decrease costs, identifying relevant parameters to increase the productivity, and determining the bottleneck of a manufacturing line for enhanced production planning.

Original language	English
Pages	9:1-9:7
Number of pages	7
DOIs	https://doi.org/10.1145/3530050.3532928
Publication status	Published - 12.06.2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)
Research Area: Intelligent Systems

DFG Research Classification Scheme

4.43-03 Security and Dependability, Operating, Communication and Distributed Systems

Access to Document

10.1145/3530050.3532928

https://dblp.org/rec/conf/bidede/PaascheG22

Cite this

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Enhancing data quality and process optimization for smart manufacturing lines in industry 4.0 scenarios.

Abstract

UN SDGs

Research Areas and Centers

DFG Research Classification Scheme

Access to Document

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