Machine vision is concerned with image-based automatic inspection and analysis of industrial products. The module introduces the fundamental ideas to setup and deploy vision applications for a modern manufacturing environment. This module balances theory with practical applications, using industry standard software and hardware for implementation and deployment.

1. Illustrate and discuss the working principles and applications of a vision system in a modern manufacturing environment.

2. Specify and select an industrial vision system for a given application.

3. Apply a range of image processing techniques for inspection and analysis of objects.

4. Develop, implement and optimise the environment setups for an industrial vision system.

5. Assess ethical, business and technical challenges in the deployment of a machine vision system.

The objective of this module is to examine how different decision theories, decision tools and data analytical and data visualisation approaches can improve the performance of employees & organisations, and to decide the types of business problems that these theories, tools and approaches can best address. Learning materials include online videos, forum based discussions and problem based learning.

1. Critically evaluate the role of decision theory in enhancing employee and organisational performance as well ascontributingto sustainable development goals

2. Evaluate different decision-making methods, tools, visualisations and interactive dashboards

3. Contrast the different data analytical, data visualisation tools and methods used by organisations

4. Consider risk and uncertainty issues in decision making

5. Critically evaluate different methods for managing risk and uncertainty

6. Appraise how digital transformation can impact decision making and analysis

Upon completion of the module, the student will understand the transition from a traditional enterprise in-house environment to a Cloud based enterprise environment. This involves an examination of concepts such as virtualization at each layer – compute, storage, network, desktop, and application – along with business continuity in a Cloud environment. The student will understand Cloud computing fundamentals, infrastructure components, service management activities, security concerns, and considerations for Cloud adoption.

Current developments with respect to IS technologies and their impact on business models will also be examined; the student will have a knowledge of significant new technology approaches.

1. Evaluate the traditional Enterprise Infrastructure.

2. Identify and implement a Virtualized Storage solution.
3. Design and develop virtualization technology of compute, storage, network, desktop and application layers of IT infrastructure.
4. Analyse business continuity solutions in a virtual data centre

5. Analyse the key considerations for migration to the Cloud

6. Investigate the emerging technology environment for manufacturing.

This module introduce the learner to Digital Twins, which are a virtual replica of the manufacturing process, and can, not only speed up the planning of a new product, but also support training, continuous improvement and maintenance planning when combined with IIoT and other technologies like AR and VR. Learners in the module will gain practical experience on building a digital twin and extracting data to support decision-making.

1. Appraise the benefits of adopting digital twin technologies when planning a new manufacturing process

2. Discuss how Industrial Internet o Things (IIoT) and digital twin technology combine to give a livemodel of a manufacturingprocesses

3. Investigate the use of Virtual Reality (VR) and Augmented Reality (AR) for the design, optimisation and planning of manufacturing processes

4. Develop a 3D model of a manufacturingprocess

This module will look at how the product flows through the manufacturing plant. Students will learn to optimise the process using the lean methodology. They will be introduced to technologies used to trace and handle material/products through the manufacturing process.

1. Optimise the factory flow using lean engineering techniques.

2. Select the appropriate technology for optimised traceability.

3. Investigate the use of AGVs, AMRs and intelligent conveyors in process flow.

4. Select and programme a Cobot for a given application.

Students will learn to control motion systems and analog process variables using PLC technology.

1. Develop an application to control motion through a PAC/PLC.

2. Develop an application to control analog process variables using a PAC/PLC.

3. Develop an application to control a Variable Frequency Drive (VFD)using a PAC/PLC.

4. Design anautomatedapplication that uses advanced features of PAC/PLC.

This module is designed as an introduction to Database Design and Development techniques.

1. Design a relational database schema for a software application
2. Devise a set of relational tables and develop a relational database.
3. Query a relational database using SQL
4. Evaluate the use of non-relational data storage technologies
5. Develop a prototype of a non-relational database model

This module will look at the data architecture of a manufacturing plant from manufacturing floor up to ERP level in accordance with the ISA-95. Students will learn how to assess an existing data architecture and plan for a new one taking into account validation requirements. On a practical level students will build a SCADA system integrating data from various equipments.

1. Assess the existing data architecture of a manufacturing plant and its components.

2. Design specification for a data architecturebased on user requirements consideringsustainable development goals.

3. Plan horizontal and vertical integration of a data architecture in a manufacturing system.

4. Develop a data management system at SCADA level.

5. Develop anintegration plan considering validation.

This module is industry based. Students will use the knowledge, skills and competences acquired in the programme to design and implement an automated process that will be digitally integrated and support data-driven decision-making.

1. Develop a project brief that will demonstrate the knowledge acquired inthe programme.

2. Independently conduct research to support the implementation of aproject.

3. Discuss where and how aproject can support digital transformation of the company.

4. Develop a project plan which modularises the project into work packages.

5. Assessresources required to complete the project.

6. Present their work in a professional manner.