To reduce production time and decrease production cost, the increase of layer thickness is an adequate option in powder bed fusion. In order to determine the relationships between process parameters in laser powder bed fusion (PBF-LB/M) and final porosity in AlSi10Mg, samples were processed following a space-filling experimental design in the present study. A total of 144 samples were fabricated considering layer thicknesses of 30 mu m, 45 mu m, 60 mu m, and 90 mu m. Afterwards, porosity was assessed using image analysis and computed tomography. Different types of defects were found as expected, however, fully dense parts were realized in case of every considered layer thickness. Predictive models were developed using data-driven approaches, eventually enabling multi-variate analysis of the correlations and determination of appropriate processing conditions resulting in both low porosity of parts and high build rates.

Laser-based additive manufacturing enables the production of complex geometries via layer-wise cladding. Laser metal deposition (LMD) uses a scanning laser source to fuse in situ deposited metal powder layer by layer. However, due to the excessive number of influential factors in the physical transformation of the metal powder and the highly dynamic temperature fields caused by the melt pool dynamics and phase transitions, the quality and repeatability of parts built by this process is still challenging. In order to analyze and/or predict the spatially varying and time dependent thermal behavior in LMD, extensive work has been done to develop predictive models usually by using finite element method (FEM). From a control-oriented perspective, simulations based on these models are computationally too expensive and are thus not suitable for real-time control applications. In this contribution, a spatio-temporal input–output model based on the heat equation is proposed. In contrast to other works, the parameters of the model are directly estimated from measurements of the LMD process acquired with an infrared (IR) camera during processing specimens using AISI 316 L stainless steel. In order to deal with noisy data, system identification techniques are used taking different disturbing noise into account. By doing so, spatio-temporal models are developed, enabling the prediction of the thermal behavior by means of the radiance measured by the IR camera in the range of the considered processing parameters. Furthermore, in the considered modeling framework, the computational effort for thermal prediction is reduced compared to FEM, thus enabling the use in real-time control applications.

In this paper, the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is adressed. It is solved by reformulating the TS model in its Linear Parameter Varying (LPV) form and applying an extension of a recently proposed Regularized Least Squares Support Vector Machine (R-LSSVM) technique for LPV models. For that, a nonparametric formulation of the TS identification problem is proposed which uses data-dependent basis functions. By doing so, the partition of unity of the TS model is preserved and the scheduling dependencies of the model are obtained in a nonparametric manner. For the local order selection, a regularization approach is used which forces the coefficient functions of insignificant values of the lagged input and output towards zero.

With the development of advanced cutting tool materials, hard turning has become a beneficial alternative to grinding in finishing of highly stressed mechanical components. However, a poor selection of cutting parameters may cause deterioration of the component's surface integrity and thus its fatigue life. Up to now, the control of surface integrity parameters is not possible as most of these quantities can not be measured during operation. Hence, suitable models are necessary to predict, e.g., the surface hardness and residual stresses induced by hard turning in-process. In order to achieve this, multiple regression and Takagi-Sugeno modeling are applied and compared in this contribution regarding their capability to predict axial and residual stress depth profiles depending on the cutting parameters and the initial hardness of the workpieces. Data from specimen machined on an industrial lathe and measured by X-ray diffraction are used for the case study.

Modeling a technical system requires a lot of effort. Especially, when used for simulation, there are high demands on the models regarding approximation accuracy and computational complexity. As building a model based on physical laws is highly time-consuming, system identification is used increasingly. A key problem in system identification is to select an appropriate model, especially when no a priori information of the considered process is available. This paper treats the application of model selection approaches known from linear regression analysis to the identification of a dynamic boost pressure model for the use in a simulation.