Table of contents

• 1. Learning Objectives
• 2. Use Case
• 3. Learning Activities
• 4. Technology
• 5. User Experience

Design and Control of Manufacturing Systems

This learning workflow is designed to address the challenges of designing and controlling a manufacturing system using modern digital technologies, with a specific focus on Extended Reality (XR). The workflow is structured to guide students through a practical, hands-on experience by leveraging a realistic virtual environment (VE) of a manufacturing facility. This approach aims to foster a deep understanding of industrial engineering principles by integrating pedagogical and technical aspects.

The core of this workflow is the application of a framework that utilizes a digital twin of a pick&place cell. The use of open and multi-platform technologies is recommended to enhance the democratization of this XR-based learning approach in higher education.

1. Learning Objectives

The types of knowledge that are relevant for this learning workflow can be instantiated as follows:

1. Factual Knowledge

1.1 Knowledge of terminology: ability to recognize and differentiate between various types of equipment and components within a manufacturing system. This includes knowing terms such as conveyor, robot, sensor, actuator, control panel, etc.

1.2 Knowledge of specific details and elements: ability to identify the role and properties of assets, e.g. functioning states of an actuator, range of action of a sensor, source and destination of parts.

1. Conceptual Knowledge

2.1 Knowledge of classifications and categories: ability to classify equipment based on its function within the manufacturing system, such as a Cartesian robot for handling and a conveyor for transportation.

2.2 Knowledge of principles and generalizations: ability to comprehend how the parts are moved within the manufacturing system, specifically the event-driven logic where sensors and actuators interact to control the system’s response.

2.3 Knowledge of theories, models, and structures: ability to understand how the system’s behavior is defined via UML statecharts.

1. Procedural Knowledge

3.1 Knowledge of subject-specific skills and algorithms: ability to define goals and process plans of a manufacturing system; ability to apply performance evaluation techniques; ability to use a communication protocol like MQTT to control a manufacturing system; ability to program the behavior of actuators.

3.2 Knowledge of subject-specific techniques and methods: ability to model the behavior of sensors and actuators using UML statecharts; ability to design an integrated manufacturing system considering the behavior of the single assets.

3.3 Knowledge of criteria for determining when to use appropriate procedures: ability to analyze system behavior and identify when a component is not performing as expected, allowing the student to troubleshoot and apply corrective procedures.

1. Metacognitive Knowledge

4.1 Self-knowledge: ability to evaluate one’s own confidence in the responses and solutions to the exercises.

Table 1 reports the specific ILOs with the associated type of knowledge.

Table 1: ILOs with associated knowledge type

2. Use Case

The learning workflow can be applied to the use case involving a pick&place cell, an automated manufacturing system designed to execute handling, sorting, and transfer operations in a discrete production environment. This use case serves as a testbed for validating the modularity, reusability, and immersive potential of the XR learning framework.

3. Learning Activities

The learning activities are organized into two levels of increasing difficulty:

1) How a manufacturing system works and is controlled. The student is provided with an existing manufacturing system. Sensors and actuators are characterized by UML statecharts. A sequence of control actions is predefined to demonstrate the capability of the system to perform a mission, i.e. moving assets from a source position to a target position while executing operations.

2) Design a manufacturing system reconfiguration and control its behavior. The student is asked to design a reconfiguration of the existing manufacturing system to meet new requirements in terms of missions to be executed. The assets already available can be reused and new assets can be included, either duplicating existing assets or defining new asset types with related properties.

Each level consists of tasks associated with specifics ILOs as reported in Table 4.

Table 4: Tasks of learning activities with related ILO

4. Technology

Virtual reality (VR) technology has been selected to deliver the necessary level of realism, allowing students to immerse themselves in a virtual manufacturing system.

Assets and scene configuration

A digital twin of the cell has been developed to support research and teaching activities. Digital resources are available online, including the 3D models of the assets in gLFT format and the scene configuration defined in a JSON file according to a specific schema.

Visualization in VR environment

The visualization of the use case in a VR environment is supported by VEB.js, a prototype web-based VR tool, taking advantage of its reconfigurable model-driven approach.

Any browser can be used to visualize the Pick&Place cell with VEB.js.

OntoGuiWeb

Data authoring of the Pick&Place cell use case has been supported by OntoGuiWeb, a web application providing a graphical user interface for the instantiation and exploration of digital models as OWL ontologies.

MQTT Communication

MQTT (Message Queuing Telemetry Transport) is a lightweight, publish-subscribe messaging protocol that is commonly used in the Internet of Things (IoT) and other applications.

MQTT can be exploited for bi-directional communications with controller of the manufacturing system:

• sending feedback about the state of the sensors (published messages)

• receiving commands for the execution of the actuators (messages receveived to subscribed topics)

VEB.js can play the role of both publisher and subscriber, exchanging messages that are structured according to a json schema. Therefore, the virtual environment (VE) can be connected with an external controller that receives that current state of the assets in the VE and controls the actions in the VE.

5. User Experience

The user experience (UX) in this learning workflow is designed to be intuitive and immersive. The virtual environment represents a simplified version of a real-world manufacturing facility, created to provide a sense of presence and a learning experience that closely mirrors reality.

The virtual environment provides an industrial setting that students can freely explore. This immersive approach allows for learning through direct interaction with a simulated production system. A key feature of the interface is the ability to retrieve supplementary data directly from objects in the environment. Users can select various objects within the virtual environment to access detailed information. This includes the object’s name, ID, dimensions, position, a brief description, and its relationships with other components.

Users interact with the virtual environment using mouse/keyboard in the desktop mode or with VR controllers in immersive mode. The main interaction method is a virtual ray cast, which functions as a laser pointer, allowing users to select and activate buttons and switches from a distance. In addition to the ray cast, users can also grab and move virtual objects like boxes and items. To ensure precision and reduce frustration, the system may use snap zones.

The assessment of the learning task is carried out by analyzing the output of the various tasks, as defined in Table 5.

Table 5: Expected outcome of the learning tasks