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

AI-powered predictive maintenance leverages machine learning and advanced data analytics to anticipate equipment failures and optimize maintenance planning. This approach shifts organizations from reactive maintenance models to proactive strategies, resulting in improved equipment uptime, reduced costs, and extended asset lifespan.

This instructor-led live training (available online or on-site) is designed for intermediate-level professionals aiming to implement AI-driven predictive maintenance solutions within industrial settings.

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

• Differentiate predictive maintenance from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-driven analysis.
• Apply machine learning models to detect anomalies and forecast failures.
• Implement comprehensive workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and group discussions.
• Practical exercises and real-world case studies.
• Live demonstrations and hands-on data workflow practice.

Customization Options

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

AI-driven process optimization involves applying machine learning and advanced data analytics to boost efficiency, enhance product quality, and increase throughput in manufacturing environments.

This instructor-led live training, available either online or on-site, is designed for intermediate-level manufacturing professionals seeking to utilize AI techniques to streamline operations, minimize downtime, and advance continuous improvement efforts.

Upon completing this training, participants will be able to:

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

Course Format

• Engaging lectures coupled with interactive discussions.
• Extensive practical exercises and hands-on practice.
• Real-time implementation within a live-lab environment.

Customization Options

• For tailored training requests, please reach out to us to arrange your specific needs.

AI-driven quality control leverages computer vision and machine learning to identify defects, anomalies, and deviations within production processes.

This instructor-led live training (available online or onsite) targets beginner to intermediate quality professionals aiming to apply AI tools for automating inspections and enhancing product quality in manufacturing settings.

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

• Grasp how AI is implemented in industrial quality control.
• Gather and label image or sensor data from production lines.
• Utilize machine learning and computer vision techniques to detect defects.
• Create simple AI models for anomaly detection and yield forecasting.

Format of the Course also allows for the evaluation of participants.

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

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

The integration of AI into Supply Chain and Manufacturing Logistics leverages predictive analytics, machine learning, and automation to streamline inventory management, optimize routing, and enhance demand forecasting.

This instructor-led live training, available both online and onsite, is designed for intermediate-level supply chain professionals seeking to deploy AI-driven tools. The goal is to boost logistics performance, improve the accuracy of demand forecasts, and automate operations within warehouses and transport networks.

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

• Grasping the role of AI across various logistics and supply chain functions.
• Employing machine learning models to manage inventory and forecast demand.
• Analyzing and optimizing transport routes using AI-based methodologies.
• Automating decision-making processes in warehousing and fulfillment workflows.

Course Format

• Engaging lectures and interactive discussions.
• Extensive practical exercises and hands-on practice.
• Real-world implementation within a live laboratory environment.

Customization Options

• For a customized training solution tailored to this course, please reach out to us to make arrangements.

The integration of AI into Smart Factories involves applying artificial intelligence technologies to automate, monitor, and optimize industrial operations in real time.

This instructor-led live training, available either online or on-site, is designed for beginner-level decision-makers and technical leads who want to gain a strategic and practical understanding of how AI can be utilized within smart factory environments.

Upon completing this training, participants will be able to:

• Grasp the fundamental principles of AI and machine learning.
• Recognize key AI applications in manufacturing and automation.
• Explore how AI facilitates predictive maintenance, quality control, and process optimization.
• Assess the necessary steps for initiating AI-driven projects.

Course Format

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

Customization Options

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

Implementing AI Use Cases employs a project-based, hands-on methodology to apply machine learning, computer vision, and data analytics in addressing real-world industrial challenges, utilizing actual or simulated datasets.

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

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

• Identify and define practical AI use cases within operations, quality assurance, or maintenance.
• Collaborate effectively across different roles to develop machine learning solutions.
• Process, clean, and analyze diverse industrial datasets.
• Demonstrate a functional prototype of an AI-enabled solution derived from a selected use case.

Course Format

• Interactive lectures and discussions.
• Group-based exercises and project work.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

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

Azure Bot Service integrates the Microsoft Bot Framework with Azure Functions, offering a robust platform for the rapid development of intelligent bots.

During this instructor-led live training, participants will discover efficient methods for developing intelligent bots using Microsoft Azure.

Upon completing the training, participants will be able to:

Grasp the fundamental concepts underlying intelligent bots.

Develop intelligent bots leveraging cloud-based applications.

Acquire practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement established bot design patterns in real-world contexts.

Create and deploy their first intelligent bot utilizing Microsoft Azure.

Target Audience

This course is tailored for developers, enthusiasts, engineers, and IT professionals with an interest in bot development.

Course Format

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

A bot, or chatbot, functions as a digital assistant designed to automate user interactions across various messaging platforms. It enables tasks to be completed more efficiently, eliminating the need for direct human conversation.

In this instructor-led live training, participants will learn how to begin developing bots by creating sample chatbots using specialized development tools and frameworks.

Upon completing this training, participants will be able to:

• Identify the various use cases and applications of bots
• Grasp the entire bot development lifecycle
• Examine the tools and platforms utilized in bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

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

Digital twins serve as virtual counterparts to physical systems, augmented by real-time information and artificial intelligence capabilities.

This instructor-led training, available online or onsite, is designed for intermediate-level professionals seeking to create, implement, and optimize digital twin models leveraging real-time data and AI-driven analytics.

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

• Grasp the architecture and core components of digital twins.
• Utilize simulation tools to model complex systems and environments.
• Incorporate real-time data streams into virtual models.
• Apply AI methodologies for predictive behavior analysis and anomaly detection.

Course Format

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

Customization Options

• For information on customizing this training, please contact us to arrange your requirements.

Edge AI involves deploying artificial intelligence models directly on 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) targets advanced embedded and IoT professionals aiming to implement AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline operation are paramount.

Upon completing this training, participants will be able to:

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

Course Format

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

Course Customization Options

• To request a customized version of this course, please contact us to arrange.

The integration of AI into industrial computer vision is revolutionizing the way manufacturers and quality assurance (QA) teams identify surface flaws, verify part conformity, and automate visual inspection workflows.

This instructor-led live training, available either online or on-site, targets intermediate to advanced QA teams, automation engineers, and developers eager to design and deploy computer vision systems for defect detection and inspection using artificial intelligence techniques.

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

• Grasp the architecture and key components of industrial vision systems.
• Construct AI models for visual defect detection utilizing deep learning methods.
• Integrate real-time inspection pipelines with industrial cameras and hardware.
• Deploy and optimize AI-driven inspection systems within production environments.

Course Format

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

Course Customization Options

• For those seeking a customized training program for this course, please contact us to make arrangements.

A Smart Robot is an Artificial Intelligence (AI) system that can learn from its environment and its experience and build on its capabilities based on that knowledge. Smart Robots can collaborate with humans, working along-side them and learning from their behavior. Furthermore, they have the capacity for not only manual labor, but cognitive tasks as well. In addition to physical robots, Smart Robots can also be purely software based, residing in a computer as a software application with no moving parts or physical interaction with the world.

In this instructor-led, live training, participants will learn the different technologies, frameworks and techniques for programming different types of mechanical Smart Robots, then apply this knowledge to complete their own Smart Robot projects.

The course is divided into 4 sections, each consisting of three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section will conclude with a practical hands-on project to allow participants to practice and demonstrate their acquired knowledge.

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

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

• Understand the key concepts used in robotic technologies
• Understand and manage the interaction between software and hardware in a robotic system
• Understand and implement the software components that underpin Smart Robots
• Build and operate a simulated mechanical Smart Robot that can see, sense, process, grasp, navigate, and interact with humans through voice
• Extend 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

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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