{"id":null,"code":"KTEK0070","name":{"valueFi":"Machine Learning in Digital Manufacturing","valueEn":"Machine Learning in Digital Manufacturing","valueSv":"Machine Learning in Digital Manufacturing"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534789097,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Embark on a journey into Machine Learning in Digital Manufacturing, where data-driven strategies\r\nconverge with advanced manufacturing technologies. This course provides MSc students with insight into\r\nthe expanding role of ML in process monitoring and optimization across diverse domains, including Additive\r\nManufacturing (AM), 3D printing, laser-assisted manufacturing processes, and surface engineering.\r\nLearners will develop a theoretical foundation on in-situ process monitoring, sensorization, sensors, and\r\ndata acquisition pipelines, emphasizing their application in defect detection, anomaly identification, and\r\nprocess quality assurance within ML-powered settings. The curriculum navigates through the fundamentals\r\nof neural networks (NNs), backpropagation, and deep learning, demonstrating why these core concepts are\r\ncritical for enabling real-time process evaluation and consistent product outputs—ultimately connecting\r\nthe dots across manufacturing processes. Through case studies and discussions, students will bridge theory\r\nand practice, examining how ML-driven systems enhance reliability, sustainability, and data-driven\r\ndecision-making. They will also explore how ML tools revolutionize design practices, optimize raw material\r\nuse, refine process parameters, and boost data-driven control for more robust manufacturing workflows.\r\n\r\nUpon completion of this course, students will have:\r\n- Explored in-situ process monitoring and sensorization techniques for real-time defect detection.\r\n- Investigated neural network and sensor data fundamentals to elevate manufacturing workflows.\r\n- Examined how ML-driven strategies boost sustainability and data-informed decision-making in industrial\r\ncontexts","valueEn":"Embark on a journey into Machine Learning in Digital Manufacturing, where data-driven strategies\r\nconverge with advanced manufacturing technologies. This course provides MSc students with insight into\r\nthe expanding role of ML in process monitoring and optimization across diverse domains, including Additive\r\nManufacturing (AM), 3D printing, laser-assisted manufacturing processes, and surface engineering.\r\nLearners will develop a theoretical foundation on in-situ process monitoring, sensorization, sensors, and\r\ndata acquisition pipelines, emphasizing their application in defect detection, anomaly identification, and\r\nprocess quality assurance within ML-powered settings. The curriculum navigates through the fundamentals\r\nof neural networks (NNs), backpropagation, and deep learning, demonstrating why these core concepts are\r\ncritical for enabling real-time process evaluation and consistent product outputs—ultimately connecting\r\nthe dots across manufacturing processes. Through case studies and discussions, students will bridge theory\r\nand practice, examining how ML-driven systems enhance reliability, sustainability, and data-driven\r\ndecision-making. They will also explore how ML tools revolutionize design practices, optimize raw material\r\nuse, refine process parameters, and boost data-driven control for more robust manufacturing workflows.\r\n\r\nUpon completion of this course, students will have:\r\n- Explored in-situ process monitoring and sensorization techniques for real-time defect detection.\r\n- Investigated neural network and sensor data fundamentals to elevate manufacturing workflows.\r\n- Examined how ML-driven strategies boost sustainability and data-informed decision-making in industrial\r\ncontexts","valueSv":"Embark on a journey into Machine Learning in Digital Manufacturing, where data-driven strategies\r\nconverge with advanced manufacturing technologies. This course provides MSc students with insight into\r\nthe expanding role of ML in process monitoring and optimization across diverse domains, including Additive\r\nManufacturing (AM), 3D printing, laser-assisted manufacturing processes, and surface engineering.\r\nLearners will develop a theoretical foundation on in-situ process monitoring, sensorization, sensors, and\r\ndata acquisition pipelines, emphasizing their application in defect detection, anomaly identification, and\r\nprocess quality assurance within ML-powered settings. The curriculum navigates through the fundamentals\r\nof neural networks (NNs), backpropagation, and deep learning, demonstrating why these core concepts are\r\ncritical for enabling real-time process evaluation and consistent product outputs—ultimately connecting\r\nthe dots across manufacturing processes. Through case studies and discussions, students will bridge theory\r\nand practice, examining how ML-driven systems enhance reliability, sustainability, and data-driven\r\ndecision-making. They will also explore how ML tools revolutionize design practices, optimize raw material\r\nuse, refine process parameters, and boost data-driven control for more robust manufacturing workflows.\r\n\r\nUpon completion of this course, students will have:\r\n- Explored in-situ process monitoring and sensorization techniques for real-time defect detection.\r\n- Investigated neural network and sensor data fundamentals to elevate manufacturing workflows.\r\n- Examined how ML-driven strategies boost sustainability and data-informed decision-making in industrial\r\ncontexts"}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"This course provides an in-depth study of Machine Learning (ML) in Digital Manufacturing, demonstrating\r\nhow data-driven insights and intelligent decision-making enhance process monitoring and quality\r\nassurance. It begins with exploring process quality and in-situ monitoring, highlighting how real-time data\r\nacquisition enables defect detection and precision optimization. Participants will examine sensorization\r\ntechnologies, including optical and acoustic sensors, and data acquisition methods that transform raw\r\nsensor outputs into actionable insights, ensuring accurate process control. The course covers data preprocessing and statistical techniques to manage vast manufacturing datasets, equipping learners to clean,\r\ntransform, and analyze data effectively. The curriculum then introduces ML fundamentals, including datadriven modelling and neural networks, focusing on perceptrons, backpropagation, and deep learning\r\nmethods such as Convolutional Neural Networks. Topics include automated defect detection and intelligent\r\ndecision-making, demonstrating how ML improves production flow and product consistency. Later, the\r\ncourse integrates intelligent systems, the Industrial Internet of Things (IIoT), and ML-driven process\r\nmonitoring, illustrating how sensor networks and data analytics create self-adaptive manufacturing\r\necosystems. Real-world use cases from Additive Manufacturing (AM), 3D printing, laser-assisted processes,\r\nand surface engineering provide practical context. An interactive discussion concludes the course, exploring\r\nAI-driven manufacturing trends, sustainable automation, and the evolution of intelligent digital\r\nmanufacturing.","valueEn":"This course provides an in-depth study of Machine Learning (ML) in Digital Manufacturing, demonstrating\r\nhow data-driven insights and intelligent decision-making enhance process monitoring and quality\r\nassurance. It begins with exploring process quality and in-situ monitoring, highlighting how real-time data\r\nacquisition enables defect detection and precision optimization. Participants will examine sensorization\r\ntechnologies, including optical and acoustic sensors, and data acquisition methods that transform raw\r\nsensor outputs into actionable insights, ensuring accurate process control. The course covers data preprocessing and statistical techniques to manage vast manufacturing datasets, equipping learners to clean,\r\ntransform, and analyze data effectively. The curriculum then introduces ML fundamentals, including datadriven modelling and neural networks, focusing on perceptrons, backpropagation, and deep learning\r\nmethods such as Convolutional Neural Networks. Topics include automated defect detection and intelligent\r\ndecision-making, demonstrating how ML improves production flow and product consistency. Later, the\r\ncourse integrates intelligent systems, the Industrial Internet of Things (IIoT), and ML-driven process\r\nmonitoring, illustrating how sensor networks and data analytics create self-adaptive manufacturing\r\necosystems. Real-world use cases from Additive Manufacturing (AM), 3D printing, laser-assisted processes,\r\nand surface engineering provide practical context. An interactive discussion concludes the course, exploring\r\nAI-driven manufacturing trends, sustainable automation, and the evolution of intelligent digital\r\nmanufacturing.","valueSv":"This course provides an in-depth study of Machine Learning (ML) in Digital Manufacturing, demonstrating\r\nhow data-driven insights and intelligent decision-making enhance process monitoring and quality\r\nassurance. It begins with exploring process quality and in-situ monitoring, highlighting how real-time data\r\nacquisition enables defect detection and precision optimization. Participants will examine sensorization\r\ntechnologies, including optical and acoustic sensors, and data acquisition methods that transform raw\r\nsensor outputs into actionable insights, ensuring accurate process control. The course covers data preprocessing and statistical techniques to manage vast manufacturing datasets, equipping learners to clean,\r\ntransform, and analyze data effectively. The curriculum then introduces ML fundamentals, including datadriven modelling and neural networks, focusing on perceptrons, backpropagation, and deep learning\r\nmethods such as Convolutional Neural Networks. Topics include automated defect detection and intelligent\r\ndecision-making, demonstrating how ML improves production flow and product consistency. Later, the\r\ncourse integrates intelligent systems, the Industrial Internet of Things (IIoT), and ML-driven process\r\nmonitoring, illustrating how sensor networks and data analytics create self-adaptive manufacturing\r\necosystems. Real-world use cases from Additive Manufacturing (AM), 3D printing, laser-assisted processes,\r\nand surface engineering provide practical context. An interactive discussion concludes the course, exploring\r\nAI-driven manufacturing trends, sustainable automation, and the evolution of intelligent digital\r\nmanufacturing."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Online quiz, Project or seminar. There will be no written final exam.","valueEn":"Online quiz, Project or seminar. There will be no written final exam.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"In-person (recorded lecturers), Quiz and Project work/Seminar","valueEn":"In-person (recorded lecturers), Quiz and Project work/Seminar","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Lecture materials provided by teacher, scientific publications and online videos.","valueEn":"Lecture materials provided by teacher, scientific publications and online videos.","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"BSc degree in engineering","valueEn":"BSc degree in engineering","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"30% for quiz 1, 30% for quiz 2 and 40% for Project work/Seminar","valueEn":"30% for quiz 1, 30% for quiz 2 and 40% for Project work/Seminar","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Konetekniikka","valueEn":"Mechanical Engineering","valueSv":"Mechanical Engineering"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Vigneashwara Solai Raja Pandiyan","valueEn":"Vigneashwara Solai Raja Pandiyan","valueSv":"Vigneashwara Solai Raja Pandiyan"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}