Sökning: "Karl Brian Nielsen"

Hittade 3 avhandlingar innehållade orden Karl Brian Nielsen.

  1. 1. Failure Prediction of Complex Load Cases in Sheet Metal Forming : Emphasis on Non-Linear Strain Paths, Stretch-Bending and Edge Effects

    Författare :Alexander Barlo; Tobias Larsson; Mats Sigvant; Md. Shafiqul Islam; Johan Pilthammar; Karl Brian Nielsen; Blekinge Tekniska Högskola; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Sheet Metal Forming; Failure Prediction; Non-Linear Strain Paths; Stretch-Bending; Edge Effects; Maskinteknik; Mechanical Engineering;

    Sammanfattning : With the increased focus on reducing carbon emissions in today’s society, several industries have to overcome new challenges, where especially the automotive industry is under a lot of scrutiny to deliver improved and more environmentally friendly products. To meet the demands from customers and optimize vehicles aerodynamically, new cars often contain complex body geometries, together with advanced materials that are introduced to reduce the total vehicle weight. LÄS MER

  3. 2. Optimization of sheet metal forming processes

    Författare :Tomas Jansson; Karl Brian Nielsen; Linköpings universitet; []
    Nyckelord :TECHNOLOGY; TEKNIKVETENSKAP;

    Sammanfattning : The potential of using simulation and optimization techniques in the design of sheet metal forming processes has been investigated. Optimization has been used in a variety of sheet metal forming applications. LÄS MER

  4. 3. Algorithms and Tools for Learning-based Testing of Reactive Systems

    Författare :Muddassar Sindhu; Karl Meinke; Brian Nielsen; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; specification-based testing; learning-based testing; reactive systems; LBTest; case studies;

    Sammanfattning : In this thesis we investigate the feasibility of learning-based testing (LBT) as a viable testing methodology for reactive systems. In LBT, a large number of test cases are automatically generated from black-box requirements for the system under test (SUT) by combining an incremental learning algorithm with a model checking algorithm. LÄS MER