Smart Robotics in Manufacturing: AI for Perception, Planning, and Control Training Course

AI-driven predictive maintenance leverages machine learning and data analytics to anticipate equipment failures and streamline maintenance schedules. This approach shifts maintenance strategies from reactive to proactive, resulting in improved operational uptime, reduced costs, and extended asset life.

This instructor-led live training, available online or on-site, targets intermediate-level professionals looking to deploy AI-led predictive maintenance solutions within industrial settings.

Upon completion of this training, participants will be capable of:

• Grasping the distinctions between predictive maintenance and reactive or preventive strategies.
• Gathering and organizing machine data for AI-led analysis.
• Utilizing machine learning models to identify anomalies and forecast failures.
• Executing complete workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case study analysis.
• Live demonstrations and hands-on data workflows.

Course Customization Options

• For tailored training arrangements, please get in touch with us.

AI-driven Process Optimization involves utilizing machine learning and advanced data analytics to boost efficiency, enhance product quality, and increase throughput within manufacturing operations.

This instructor-led live training, available both online and onsite, targets intermediate-level manufacturing professionals eager to apply AI techniques to streamline operations, minimize downtime, and drive continuous improvement.

Upon completing this training, participants will be equipped to:

• Grasp AI concepts pertinent to manufacturing optimization.
• Gather and prepare production data for comprehensive analysis.
• Deploy machine learning models to detect bottlenecks and forecast equipment failures.
• Visualize and interpret data insights to facilitate informed, data-driven decision-making.

Course Delivery Format

• Interactive lectures coupled with group discussions.
• Ample exercises and practical practice sessions.
• Hands-on implementation exercises within a live laboratory environment.

Customization Options

• For customized training arrangements, please reach out to us to discuss your specific requirements.

Artificial Intelligence for Quality Control leverages computer vision and machine learning methodologies to pinpoint defects, irregularities, and deviations within production workflows.

This instructor-led training, available either online or on-site, is designed for quality professionals at the beginner to intermediate level who aim to utilise AI tools to automate inspection processes and enhance product quality in manufacturing settings.

Upon completion of this training, participants will be capable of:

• Grasp the application of AI in industrial quality control.
• Gather and annotate image or sensor data from production lines.
• Employ machine learning and computer vision technologies to identify defects.
• Construct basic AI models for anomaly detection and yield forecasting.

Course Format

• Engaging lectures and discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• To request tailored training for this course, please contact us to make arrangements.

Leveraging AI in Supply Chain and Manufacturing Logistics involves applying predictive analytics, machine learning, and automation to optimize inventory management, routing efficiency, and demand forecasting.

This instructor-led live training, available online or onsite, is designed for intermediate-level supply chain professionals seeking to utilize AI-driven tools to boost logistics performance, enhance demand forecasting accuracy, and automate warehouse and transport operations.

Upon completion of this training, participants will be able to:

• Comprehend the application of AI across logistics and supply chain activities.
• Utilize machine learning models for demand forecasting and inventory control.
• Analyze and optimize transport routes using AI-based techniques.
• Automate decision-making processes in warehouses and fulfillment operations.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• For customized training requests, please contact us to make arrangements.

Artificial intelligence within smart factories involves applying AI technologies to automate, monitor, and optimize industrial operations in real time.

This instructor-led training session, available both online and onsite, is designed for beginner-level decision-makers and technical leads who seek a strategic and practical introduction to leveraging AI in smart factory environments.

Upon completion of this training, participants will be able to:

• Comprehend the fundamental principles of artificial intelligence and machine learning.
• Identify key AI applications in manufacturing and automation sectors.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Evaluate the necessary steps for initiating AI-driven projects.

Course Format

• Interactive lectures and discussions.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

• To arrange customized training for this course, please contact us.

AI Use Case Implementation offers a hands-on, project-focused methodology for leveraging machine learning, computer vision, and data analytics to address real-world industrial challenges, utilizing actual or simulated datasets.

This instructor-led live training, available both online and onsite, is designed for intermediate-level cross-functional teams aiming to collaboratively deploy AI solutions that align with their operational objectives, while gaining practical experience with industrial data pipelines.

Upon completion of this training, participants will be equipped to:

• Identify and define practical AI use cases within operations, quality assurance, or maintenance functions.
• Collaborate effectively across different roles to build machine learning solutions.
• Manage, clean, and analyze varied industrial datasets.
• Demonstrate a functional prototype of an AI-driven solution based on a selected use case.

Course Format

• Interactive lectures and discussions.
• Group exercises and project-based work.
• Practical implementation in a live laboratory environment.

Customization Options for the Course

• For tailored training on this course, please reach out to us to make arrangements.

Azure Bot Service unites the strengths of the Microsoft Bot Framework and Azure Functions, delivering a robust platform for rapidly constructing smart bots.

In this instructor-led live training, participants will discover how to efficiently develop smart bots using Microsoft Azure.

Upon completing the training, participants will be able to:

Grasp the fundamental concepts underlying smart bots.

Construct smart bots using cloud-based applications.

Acquire practical insights into the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement proven bot design patterns in real-world situations.

Build and deploy their first smart bot using Microsoft Azure.

Target Audience

This course is tailored for developers, enthusiasts, engineers, and IT professionals keen on bot development.

Course Format

The training blends lectures and discussions with exercises, placing a strong emphasis on hands-on practice.

A bot, or chatbot, acts as a digital assistant designed to automate user interactions across various messaging platforms, enabling faster task completion without requiring direct human contact.

In this instructor-led live training, participants will gain foundational skills in bot development by building sample chatbots using industry-standard tools and frameworks.

Upon completion of this training, participants will be able to:

• Identify the diverse applications and use cases of bots
• Comprehend the end-to-end bot development lifecycle
• Evaluate various tools and platforms suitable for bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot utilizing the Microsoft Bot Framework

Target Audience

• Developers eager to create their own bot solutions

Course Format

• A blend of lectures, group discussions, practical exercises, and extensive hands-on practice

Digital Twins are virtual replicas of physical systems enhanced by real-time data and AI-driven intelligence.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to build, deploy, and optimize digital twin models using real-time data and AI-based insights.

By the end of this training, participants will be able to:

• Understand the architecture and components of digital twins.
• Use simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behavior and anomaly detection.

Course Delivery Structure

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Options for Tailoring the Course

• To request a customized training for this course, please contact us to arrange.

Edge AI involves deploying artificial intelligence models directly onto devices and machines at the network's edge, facilitating real-time decision-making with minimal latency.

This instructor-led, live training (available online or onsite) is designed for advanced-level embedded and IoT professionals seeking to implement AI-driven logic and control systems in manufacturing environments where speed, reliability, and offline operation are critical.

By the conclusion of this training, participants will be able to:

• Grasp the architecture and advantages of edge AI systems.
• Construct and optimise AI models for deployment on embedded devices.
• Utilise tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Format of the Course

• Interactive lecture and discussion.
• Numerous exercises and practical practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Artificial intelligence-powered industrial computer vision is revolutionizing the way manufacturers and quality assurance teams identify surface defects, verify part compliance, and automate visual inspection workflows.

This instructor-led, live training (available online or onsite) targets intermediate to advanced quality assurance teams, automation engineers, and developers eager to design and implement computer vision systems for defect detection and inspection using AI techniques.

Upon completion of this training, participants will be capable of:

• Grasping the architecture and key components of industrial vision systems.
• Developing AI models for visual defect detection using deep learning.
• Integrating real-time inspection pipelines with industrial cameras and devices.
• Deploying and optimizing AI-enabled inspection systems within production environments.

Course Format

• Interactive lectures and discussions.
• Ample exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to arrange.

A smart robot is an Artificial Intelligence (AI) system capable of learning from its surroundings and past experiences, enhancing its capabilities through that knowledge. These robots can collaborate with humans, working alongside them and learning from their actions. Furthermore, they are equipped to handle not only manual labour but also cognitive tasks. Beyond physical machines, smart robots can also exist purely as software, residing within a computer as an application without moving parts or physical interaction with the real world.

In this instructor-led live training, participants will explore the various technologies, frameworks, and techniques required to program different types of mechanical smart robots, then apply this knowledge to complete their own smart robot projects.

The course is structured into 4 sections, each comprising three days of lectures, discussions, and hands-on robot development within a live lab environment. Each section concludes with a practical hands-on project, allowing participants to practice and demonstrate their acquired knowledge.

The target hardware for this course will be simulated in 3D using simulation software. The open-source ROS (Robot Operating System) framework, along with C++ and Python, will be used for programming the robots.

By the end of this training, participants will be able to:

• Grasp the key concepts underpinning robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that support smart robots
• Build and operate a simulated mechanical smart robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans via voice
• Enhance a smart robot's ability to perform complex tasks through Deep Learning
• Test and troubleshoot a smart robot in realistic scenarios

Audience

• Developers
• Engineers

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• To customize any aspect of this course (programming language, robot model, etc.), please contact us to make arrangements.