This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

No abstract

No abstract

In this work, the federated learning methodology is applied to predict defects in sheet metal forming processes exposed to sources of scatter in the material properties and process parameters. Numerical simulations of the U-channel forming process were performed to analyze springback for three types of sheet steel materials. The datasets of different clients are used to train a single machine learning model. With this approach, multiple parties would simultaneously train a machine learning model on their combined data by training the models locally on the client nodes and progressively improving the learning model through interaction with the central server. This way the industrial peers have no access to the others local data in a centralized server. The predictive performance achieved is similar to a standard centralized learning method, offering competitive results of collaborative machine learning in industrial environment.

Machine learning models are built to predict the strain values for which edge cracking occurs in hole expansion tests. The samples from this test play the role of sheet metal components to be manufactured, in which edge cracking often occurs associated with a uniaxial tension stress state at the critical edges of components. For the construction of the models, a dataset was obtained experimentally for rolled ferritic carbon steel sheets of different qualities and thicknesses. Two types of tests were performed: tensile and hole expansion tests. In the tensile test, the yield stress, the tensile strength, the strain at maximum load and the elongation after fracture were determined in the rolling and transverse directions. In the hole expansion test, the strain for which edge cracking occurs, was determined. It is intended that the models can predict the strain at fracture in this test, based on the knowledge of the tensile test data. The machine learning algorithms used were Multilayer Perceptron, Gaussian Processes, Support Vector Regression and Random Forest. The traditional polynomial regression that fits a 2nd order polynomial function was also used for comparison. It is shown that machine learning-based predictive models outperform the traditional polynomial regression method; in particular, Gaussian Processes and Support Vector Regression were found to be the best machine learning algorithms that enable the most robust predictive models.

Melhorias de Processo de Software (MPS) precisam englobar toda a organização para garantir que as evoluções dos processos sejam adotadas. Contudo, é necessário analisar como os conhecimentos sobre essas evoluções são compartilhados, a fim de aplicar esforços adequados ao contexto da organização. Este artigo descreve a utilização da técnica de análise das redes sociais visando estudar como o conhecimento sobre o processo e sua melhoria é disseminado em organizações. Os resultados obtidos permitiram identificar colaboradores chave e obstáculos que influenciam a transmissão do conhecimento. Este tipo de análise pode auxiliar na melhoria da disseminação de conhecimentos relacionados à MPS, facilitando assim a aprendizagem.