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Control Systems01:10

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Introduction to Statistical Process Control01:15

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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Control Systems: Applications01:25

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Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
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Open and closed-loop control systems01:17

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Run Charts01:12

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Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
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Electro-mechanical Systems01:19

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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
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Data Communication Based on MQTT in a Polymer Extrusion Process
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Advanced Control Systems in Industry 5.0 Enabling Process Mining.

Alessandro Massaro1,2

  • 1LUM Enterprise S.r.l., S.S. 100-Km.18, Parco il Baricentro, 70010 Bari, Italy.

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|November 26, 2022
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Summary
This summary is machine-generated.

This study introduces an Industry 5.0 approach using Business Process Modeling and Notation (BPMN) to integrate Artificial Intelligence (AI) in production. It enhances decision-making and optimizes industrial processes through advanced Process Mining (PM) and AI algorithms.

Keywords:
Artificial IntelligenceBPMN process workflowsIndustry 5.0Process Miningself-adaptive machine parameter setting

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Area of Science:

  • Industrial Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Industry 5.0 emphasizes human-centric, sustainable, and resilient industrial systems.
  • Integrating Artificial Intelligence (AI) into production processes is crucial for optimizing efficiency and quality.
  • Existing Business Process Modeling and Notation (BPMN) approaches need enhancement to fully leverage AI in manufacturing.

Purpose of the Study:

  • To propose an innovative approach for modeling production management in Industry 5.0.
  • To integrate AI and advanced Process Mining (PM) into BPMN for automated decision-making and process optimization.
  • To demonstrate a proof of concept for intelligent decision-making in industrial settings.

Main Methods:

  • Utilizing the Business Process Modeling and Notation (BPMN) framework.
  • Developing an advanced Process Mining (PM) model incorporating supervised and unsupervised AI algorithms.
  • Implementing a Decision Support System (DSS) engine for intelligent decision-making procedures.
  • Embedding advanced electronic sensing and actuation systems within the PM model.

Main Results:

  • A novel approach to model production management integrating AI within BPMN.
  • Demonstration of automated decision-making and optimized machine settings/maintenance through advanced PM.
  • Theoretical models applied to food processing and energy production sectors showcasing practical applicability.
  • Enhanced engineering management elements for digitalized production processes.

Conclusions:

  • The proposed framework enables intelligent decision-making and self-adapting control systems in modern industrial environments.
  • This integration of AI, PM, and BPMN facilitates improved product quality supervision and production efficiency.
  • The research provides a foundation for advancing Industry 5.0 through smart, automated production management.