Flexible Manufacturing Systems Through the Integration of Asset Administration Shells, Skill-Based Manufacturing, and OPC UA

Martins, André; Costelha, Hugo; Neves, Carlos; Cosgrove, John; Lyons, John G.

doi:10.1007/978-3-031-38165-2_56

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…A robust and reliable classification system capable of alerting most defective parts produced is the ideal method for dealing with production defects. To do this, it is mandatory to use real-time process data and if the machines do not have factory enable protocols, for example, Euromap 77 through OPC-UA, it is necessary to adapt them [9,10].…”

Section: Introductionmentioning

confidence: 99%

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Silva

Marques²,

Faustino

et al. 2022

Processes

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With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution.

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Section: Introductionmentioning

confidence: 99%

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Silva

Marques²,

Faustino

et al. 2022

Processes

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show abstract

Generating Flexible Manufacturing System Cycle Diagrams with Petri Net

Kupriyanov,

Hrinchenko,

Dichev

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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A Universal Framework for Skill-Based Cyber-Physical Production Systems

Hossfeld,

Wortmann

2024

JMMP

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In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.

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Flexible Manufacturing Systems Through the Integration of Asset Administration Shells, Skill-Based Manufacturing, and OPC UA

Cited by 3 publications

References 8 publications

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing

Generating Flexible Manufacturing System Cycle Diagrams with Petri Net

A Universal Framework for Skill-Based Cyber-Physical Production Systems

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