Blar i NTNU Open på forfatter "Martinsen, Kristian"
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NSGA III for CNC End Milling Process Optimization
Ghosh, Tamal; Martinsen, Kristian (Peer reviewed; Journal article, 2020)Computer Numerical Controlled (CNC) end milling processes require very complex and expensive experimentations or simulations to measure the overall performance due to the involvement of many process parameters. Such problems ... -
Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework
Baturynska, Ivanna; Semeniuta, Oleksandr; Martinsen, Kristian (Journal article; Peer reviewed, 2018)In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. ... -
Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms
Baturynska, Ivanna; Martinsen, Kristian (Peer reviewed; Journal article, 2020)Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations ... -
Prediction of Width and Thickness of Injection Molded Parts Using Machine Learning Methods
Ogorodnyk, Olga; Lyngstad, Ole Vidar; Larsen, Mats; Martinsen, Kristian (Chapter, 2021)Injection molding is one of the major processes applied for production of thermoplastic products. Thermoplastic materials are used in manufacturing of dozens of products seen in everyday life, such as: car bumpers, children ... -
Proposed framework for flexible de- and remanufacturing systems using cyber-physical systems, additive manufacturing, and digital twins
Assuad, Carla Susana Aqudelo; Leirmo, Torbjørn Langedahl; Martinsen, Kristian (Peer reviewed; Journal article, 2022)Under the circular economy paradigm, de- and remanufacturing systems are more relevant than ever. However, such systems present specific challenges related to the system structure, automation, and recovery of complex ... -
Social aspects of process monitoring in manufacturing systems
Martinsen, Kristian; Holtskog, Halvor; Larsson, Catrine Eleonor (Journal article; Peer reviewed, 2012)Many systems for process monitoring using in-process sensor measuring rely on human interpretation and reaction in order to control the process and achieve the desired effect from the monitoring. This paper reports from ... -
A Surrogate-Assisted Optimization Approach for Multi-Response End Milling of Aluminium Alloy AA3105
Ghosh, Tamal; Yi, Wang; Martinsen, Kristian; Wang, Kesheng (Peer reviewed; Journal article, 2020)Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are ... -
System dynamics modelling and learning factories for manufacturing systems education
Assuad, Carla Susana Aqudelo; Tvenge, Nina; Martinsen, Kristian (Peer reviewed; Journal article, 2020)Manufacturing systems are complex socio-technical systems with non-linearities, accumulation, flows and delays that challenge decision-making processes. System Dynamics (SD) is a valuable approach to analyse and understand ... -
Tolerancing from STL data: A Legacy Challenge
Leirmo, Torbjørn Langedahl; Semeniuta, Oleksandr; Martinsen, Kristian (Journal article; Peer reviewed, 2020)Part representation in additive manufacturing (AM) is dominated by the stereolithography (STL) file format as a universal mode for communicating and transferring part geometry from one system to another. However, when the ... -
Towards a general application programming interface (API) for injection molding machines
Ogorodnyk, Olga; Larsen, Mats; Lyngstad, Ole Vidar; Martinsen, Kristian (Peer reviewed; Journal article, 2020)Injection molding is a complicated process, and the final part quality depends on many machine and process parameters settings. To increase controllability of the injection molding process, acquisition of the process data ... -
Towards increased intelligence and automatic improvement in industrial vision systems
Semeniuta, Oleksandr; Dransfeld, Sebastian; Martinsen, Kristian; Falkman, Petter (Journal article; Peer reviewed, 2018)Robots and in-process inspection systems equipped with machine vision solutions are used for increased flexibility and quality in automated manufacturing. Although vision systems have found wide industrial use, there are ... -
Towards Intelligent Process Control for Thermoplastics Injection Molding: Machine Learning for prediction of quality of injection molded parts
Ogorodnyk, Olga (Doctoral theses at NTNU;2021:71, Doctoral thesis, 2021)Injection molding is a process that is widely used for production of plastic parts of different shapes, sizes and applications. Some of its advantages are high production rates and efficiency. However, to be able to produce ... -
User Friendly Framework for Measuring Product and Process Novelty in the Early Stages of Product Development
Ringen, Geir; Holtskog, Halvor; Martinsen, Kristian (Journal article; Peer reviewed, 2012)This paper examines the innovation process in four Norwegian companies, all supplying the automotive industry with light weight structural components. Twelve representative product projects are categorized, together with ... -
Workarounds in application and use of manufacturing software as enablers to organizational change
Larsson, Catrine Eleonor; Andersen, Bjørn Sørskot; Martinsen, Kristian (Journal article; Peer reviewed, 2021)In this paper we look at four Norwegian manufacturers’ Information System and stages of Information Technology adaptation, explaining how practice of technology and actions/workarounds to IT systems, that enables, hinders, ... -
Workarounds in the Manufacturing Industry’s Information Systems: A sociomaterial perspective on technology-based organisational change
Larsson, Catrine Eleonor (Doctoral theses at NTNU;2017:119, Doctoral thesis, 2017)