Table of contents

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

Design and Analysis of Manufacturing Systems

The design and analysis of manufacturing systems is a complex engineering activity requiring multidisciplinary knowledge to satisfy production requirements. A manufacturing system comprises production, handling, transportation, storage, and control resources, each characterised by specific functions and capabilities. The way these resources are configured and integrated determines the overall capabilities and performance of the system.

Manufacturing-system design is guided by production requirements and key performance indicators (KPIs), such as throughput, lead time, resource utilisation, work in progress, and service level. Evaluating alternative system configurations therefore requires suitable modelling and analysis methods, including discrete-event simulation.

This learning workflow uses an immersive virtual representation of an industrial assembly line to help students understand its structure and behaviour, formulate a conceptual model, estimate model parameters, and evaluate system performance.

1. Learning Objectives

Following the revised taxonomy proposed by Anderson et al. (see Section 3.1), the knowledge addressed by this workflow is organised into four categories: factual, conceptual, procedural, and metacognitive knowledge.

1. Factual Knowledge

• 1.1 Knowledge of terminology: Ability to recognize and differentiate between various types of equipment.

• 1.2 Knowledge of specific details and elements: Ability to identify the properties of objects.

2. Conceptual Knowledge

• 2.1 Knowledge of classifications and categories: Ability to classify equipment based on its function, such as production resources, transportation resources, or buffers.

• 2.2 Knowledge of principles and generalizations: Ability to comprehend the movement of parts within manufacturing cells in the virtual environment.

• 2.3 Knowledge of theories, models, and structures: Ability to recognize different types of manufacturing system architectures, such as flow shop, job shop, and others.

3. Procedural Knowledge

• 3.1 Knowledge of subject-specific skills and algorithms: Ability to apply performance evaluation techniques (e.g., discrete event simulation) to model and assess the performance of a manufacturing system.

• 3.2 Knowledge of subject-specific techniques and methods: 1) Ability to analyze manufacturing system behavior and evaluate functional parameters. 2) Ability to develop a performance evaluation model based on the conceptual model of the manufacturing system.

• 3.3 Knowledge of criteria for determining when to use appropriate procedures: Ability to formulate accurate hypotheses and generate a conceptual model of the manufacturing system being studied.

4. Metacognitive Knowledge

• 4.1 Self-knowledge: Ability to evaluate one’s own confidence in responses to questions or solutions to exercises.

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

Table 1: ILOs with associated knowledge type

2. Use Case

The workflow is applied to an industrial assembly line that produces self-closing concealed cabinet hinges. The system comprises 19 workstations that perform operations such as component feeding, pick-and-place handling, screw tightening, riveting, inspection, and transportation.

The use case enables students to:

• identify products, components, and manufacturing resources;
• understand the routing and transformation of parts;
• examine the relationships among processes and resources;
• recognise the manufacturing-system architecture;
• estimate processing and reliability parameters;
• develop a conceptual and simulation model;
• evaluate the performance of the assembly line.

The components of the self-closing concealed cabinet hinge are shown in Figure 1.

Figure 1: Hinge components

3. Learning Activities

The learning activities are organised into three levels of increasing complexity:

• Level 1 – Understanding what a manufacturing system consists of

  Students explore the virtual factory to identify manufacturing equipment, products, components, and object properties. The activities establish the factual knowledge required for the subsequent analysis tasks.

• Level 2 – Understanding how a manufacturing system operates

  Students classify resources according to their functions, analyse the routing and transformation of parts, identify assembly points and workstations, and determine the architecture of the manufacturing system.

• Level 3 – Evaluating manufacturing-system performance

  Students estimate processing and reliability parameters, formulate modelling assumptions, select suitable analysis methods, develop a performance-evaluation model, and use the model to assess the manufacturing system against production requirements.

A self-assessment task accompanies the three levels and asks students to report their confidence in the answers and numerical results produced. Table 2 links each task to the relevant intended learning outcomes.

Table 2. Learning tasks and related intended learning outcomes.

4. Technology

The workflow combines a digital representation of the assembly line, a web-based virtual environment, an online learning-management system, semantic modelling tools, and discrete-event simulation.

4.1 Digital assets and scene configuration

A digital twin of the assembly line was developed to support teaching and research activities. The online resources include:

• three-dimensional asset models in glTF format;
• a scene configuration represented in JSON;
• structured descriptions of factory objects and their properties;
• operational data required by the analysis tasks.

The JSON scene configuration follows a dedicated schema.

4.2 Virtual environment

Virtual reality enables students to explore the assembly line through an immersive factory walkthrough. The workflow uses the VEB.js prototype application to render the industrial environment and provide access to information associated with products, resources, and workstations. Figure 2 illustrates how the industrial environment, including the assembly line, is rendered within VEB.js.

Figure 2: Screenshot of VEB.js application showing the assembly line use case

4.3 Learning activities and assessment

The learning tasks and corresponding assessments can be delivered through Moodle. Moodle supports several question types relevant to this workflow, including:

• multiple-choice questions;
• numerical questions;
• drag-and-drop labelling of images;
• selection of missing words;
• file submissions;
• confidence-rating questions.

4.4 Digital model

Factory objects, object properties, spatial placement, three-dimensional representations, and relationships can be defined using OntoGuiWeb. The tool provides a graphical interface for creating and exploring factory models represented through an ontology-based data model.

4.5 Discrete-event simulation

A discrete-event simulation tool can be used to generate operational events, derive performance indicators, and animate the virtual scene. One suitable implementation is Java Modelling Tools, although alternative simulation tools may be adopted if they provide the required modelling and data-exchange capabilities.

Figure 3 shows the integration of the digital tools employed to model the industrial case.

Figure 3: Integrated digital tools and data repository

5. User Experience

The virtual environment allows students to explore the industrial setting and retrieve the information required to complete the learning tasks. The graphical user interface supports direct interaction with factory objects and access to object-related resources.

Students can select objects in the virtual environment to retrieve information such as:

• object name and identifier;
• dimensions and spatial position;
• functional description;
• object class and function;
• relationships with other factory objects;
• relevant operational parameters.

These data help students understand the structure and connectivity of the manufacturing system.

Objects in the virtual environment may provide links to supporting resources, including:

• lecture slides and explanatory material;
• technical data sheets;
• three-dimensional models;
• event and failure logs;
• processing-time data;
• maintenance and production reports.

Direct access to these resources enables students to combine visual exploration with data-driven analysis.

The workflow can support:

• desktop interaction using a mouse and keyboard;
• immersive interaction using a VR headset and motion controllers;
• individual activities completed independently;
• collaborative activities supported by a conferencing or multiplayer platform.

This combination allows the workflow to be used in classroom, laboratory, blended, and remote-learning settings.

Assessment is performed through the outputs generated by the learning tasks. Moodle can be used to administer automatically scored questions, collect files, provide feedback, and record student confidence ratings. Tasks involving conceptual models, simulation models, and written conclusions require instructor review or a predefined assessment rubric.

Table 3. Evidence and assessment methods for the learning tasks.