Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

Management of industrialization projects

Short time-to-market is a key success factor in the todays’ dynamic business environment and many companies are trying to improve their product development processes. A challenge is to develop products according to the time plan and at the same time keeping the cost low and the quality high. This study focuses on the project management within the product development process in an automotive industry. The background of this study started as a request from the research and development department at the automotive company, which led to the following questions; 1) what are the most crucial factors for project success? 2) How can these factors contribute to a more successful outcome? 3) How can project management decrease product development lead time by sharing knowledge? The research approach is a case study and the data collection consist of interviews and questioners at two companies connected to project management in product development projects. Spider charts are created from the collected data containing eleven dimensions to show similarities and differences between the project managers working within the research and development department as well as between the two companies. The main conclusions are that there is a need to allow a certain level of flexibility when managing projects, in order to more easily handle late changes. Being involved in a project from the concept phase could facilitate the product development activities later on, due to a deeper understanding regarding previous decisions. Further, knowledge sharing methods, such as databases, has to be designed to be suitable for a specific organization and user friendly which enables the users to more easily search for specific types of knowledge. Lastly, a low level on the detailed focus is shown to be another success factor, however, in some cases there is still a need of this detailed focus to solve specific problems but the details may never become a higher focus than the holistic view.