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

AI-powered predictive maintenance applies machine learning and data analytics to forecast equipment failures and optimize maintenance schedules. It transforms reactive maintenance models into proactive strategies, enabling better uptime, cost reduction, and asset longevity.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement AI-driven predictive maintenance solutions in industrial environments.

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

• Understand how predictive maintenance differs from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-powered analysis.
• Apply machine learning models to detect anomalies and predict failures.
• Implement end-to-end workflows from sensor data to actionable insights.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises and case studies.
• Live demonstration and practical data workflows.

Course Customization Options

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

AI for Process Optimization is the application of machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing operations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level manufacturing professionals who wish to apply AI techniques to streamline operations, reduce downtime, and support continuous improvement initiatives.

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

• Understand AI concepts relevant to manufacturing optimization.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualize and interpret results to support data-driven decisions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and 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.

AI for Quality Control is the use of computer vision and machine learning techniques to identify defects, anomalies, and deviations in production processes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level quality professionals who wish to apply AI tools to automate inspections and improve product quality in manufacturing environments.

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

• Understand how AI is applied in industrial quality control.
• Collect and label image or sensor data from production lines.
• Use machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and 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.

AI in Supply Chain and Manufacturing Logistics is the application of predictive analytics, machine learning, and automation to optimize inventory, routing, and demand forecasting.

This instructor-led, live training (online or onsite) is aimed at intermediate-level supply chain professionals who wish to apply AI-driven tools to enhance logistics performance, forecast demand accurately, and automate warehouse and transport operations.

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

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyze routes and optimize transport using AI-based techniques.
• Automate decision-making in warehouses and fulfillment processes.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and 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.

AI in Smart Factories is the application of artificial intelligence to automate, monitor, and optimize industrial operations in real time.

This instructor-led, live training (online or onsite) is aimed at beginner-level decision-makers and technical leads who wish to gain a strategic and practical introduction to how AI can be leveraged in smart factory environments.

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

• Understand the core principles of AI and machine learning.
• Identify key AI use cases in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimization.
• Evaluate the steps involved in launching AI-driven initiatives.

Format of the Course

• Interactive lecture and discussion.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customization Options

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

AI Use Case Implementation is a hands-on, project-driven approach to applying machine learning, computer vision, and data analytics to solve real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (online or onsite) is aimed at intermediate-level cross-functional teams who wish to collaboratively implement AI use cases aligned with their operational goals and gain experience working with industrial data pipelines.

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

• Select and scope practical AI use cases from operations, quality, or maintenance.
• Work collaboratively across roles to develop machine learning solutions.
• Handle, clean, and analyze diverse industrial datasets.
• Present a working prototype of an AI-enabled solution based on a selected use case.

Format of the Course

• Interactive lecture and discussion.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Azure Bot Service kombinerer kraften i Microsoft Bot Framework og Azure funksjoner for å muliggjøre rask utvikling av intelligente roboter.

I denne instruktørledede, liveopplæringen, vil deltakerne lære hvordan de enkelt kan lage en intelligent bot ved hjelp av Microsoft Azure

Ved slutten av denne opplæringen vil deltakerne kunne:

• Lære grunnleggende om intelligente boter
• Lære hvordan man lager intelligente boter ved hjelp av skyapplikasjoner
• Forstå hvordan man bruker Microsoft Bot Framework, Bot Builder SDK og Azure Bot Service
• Forstå hvordan man designer boter ved hjelp av botmønstre
• Utvikle sin første intelligente bot ved hjelp av Microsoft Azure

Publikum

• Utviklere
• Hobbyister
• Ingeniører
• IT-profesjonelle

Kursets format

• Delt forelesning, del diskusjon, øvelser og mye praktisk trening

En bot eller chatbot er som en dataassistent som brukes til å automatisere brukerinteraksjoner på ulike meldingsplattformer og få ting gjort raskere uten at brukerne trenger å snakke med et annet menneske.

I denne instruktørledede, live-opplæringen vil deltakerne lære hvordan de kan komme i gang med å utvikle en bot når de går gjennom opprettelsen av eksempelchatboter ved å bruke robotutviklingsverktøy og -rammeverk.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå de forskjellige brukene og bruksområdene til roboter
• Forstå hele prosessen med å utvikle roboter
• Utforsk de forskjellige verktøyene og plattformene som brukes til å bygge roboter
• Bygg en prøve chatbot for Facebook Messenger
• Bygg en prøve chatbot ved å bruke Microsoft Bot Framework

Publikum

• Utviklere som er interessert i å lage sin egen bot

Format på kurset

• Del forelesning, del diskusjon, øvelser og tung praktisk praksis

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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and 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.

Edge AI is the deployment of artificial intelligence models directly on devices and machines at the edge of the network, enabling real-time decision-making with minimal latency.

This instructor-led, live training (online or onsite) is aimed at advanced-level embedded and IoT professionals who wish to deploy AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are critical.

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

• Understand the architecture and benefits of edge AI systems.
• Build and optimize AI models for deployment on embedded devices.
• Use tools like 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.
• Lots of exercises and 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.

Industrial computer vision with AI is transforming how manufacturers and QA teams detect surface defects, verify part conformity, and automate visual inspection processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level QA teams, automation engineers, and developers who wish to design and implement computer vision systems for defect detection and inspection using AI techniques.

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

• Understand the architecture and components of industrial vision systems.
• Build AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimize AI-powered inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and 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.

Denne instruktørledede, liveopplæringen i Norge (online eller på stedet) er rettet mot ingeniører som ønsker å lære om anvendeligheten av kunstig intelligens på mekatroniske systemer.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Få en oversikt over kunstig intelligens, maskinlæring og beregningsmessig intelligens.
• Forstå begrepene nevrale nettverk og ulike læringsmetoder.
• Velg kunstig intelligens tilnærminger effektivt for virkelige problemer.
• Implementere AI-applikasjoner i mekatronikkteknikk.

Denne instruktørledede, live-opplæringen i Norge (online eller på stedet) er rettet mot deltakere på middels nivå som ønsker å forbedre ferdighetene sine i å designe, programmere og distribuere intelligente robotsystemer for automatisering og utover.

Ved slutten av denne opplæringen vil deltakerne kunne:

• Forstå prinsippene til Physical AI og dens anvendelser innen robotikk og automatisering.
• Design og programmer intelligente robotsystemer for dynamiske miljøer.
• Implementer AI-modeller for autonom beslutningstaking i roboter.
• Utnytt simuleringsverktøy for robottesting og optimalisering.
• Ta tak i utfordringer som sensorfusjon, sanntidsbehandling og energieffektivitet.

En smart robot er et kunstig intelligens (AI)-system som kan lære av miljøet og erfaringen og bygge videre på sine evner basert på denne kunnskapen. Smart Robots kan samarbeide med mennesker, jobbe sammen med dem og lære av deres oppførsel. Videre har de kapasitet til ikke bare manuelt arbeid, men også kognitive oppgaver. I tillegg til fysiske roboter, kan Smart Robots også være rent programvarebasert, som ligger i en datamaskin som en programvareapplikasjon uten bevegelige deler eller fysisk interaksjon med verden.

I denne instruktørledede, live-opplæringen vil deltakerne lære de forskjellige teknologiene, rammeverket og teknikkene for programmering av forskjellige typer mekaniske Smart Robots, og deretter bruke denne kunnskapen for å fullføre sine egne Smart Robot-prosjekter.

Kurset er delt inn i 4 seksjoner, som hver består av tre dager med forelesninger, diskusjoner og praktisk robotutvikling i et levende labmiljø. Hver seksjon avsluttes med et praktisk praktisk prosjekt for å la deltakerne øve og demonstrere sin tilegnete kunnskap.

Målmaskinvaren for dette kurset vil bli simulert i 3D gjennom simuleringsprogramvare. ROS (Robot Operating System) åpen kildekode-rammeverket, C++ og Python vil bli brukt til å programmere robotene.

Ved slutten av denne opplæringen vil deltakerne kunne:

Forstå nøkkelbegrepene som brukes i robotteknologi Forstå og administrere samspillet mellom programvare og maskinvare i et robotsystem Forstå og implementere programvarekomponentene som ligger til grunn Smart Robots Bygg og drift en simulert mekanisk Smart Robot som kan se, sanse, behandle, gripe, naviger og samhandle med mennesker gjennom stemmen Utvid en Smart Robots evne til å utføre komplekse oppgaver gjennom Deep Learning Test og feilsøk en Smart Robot i realistiske scenarier

Publikum

Utviklere Ingeniører

Format på kurset

Del forelesning, del diskusjon, øvelser og tung praktisk praksis

Merk

For å tilpasse noen del av dette kurset (programmeringsspråk, robotmodell, etc.), vennligst kontakt oss for å avtale.