Additive manufacturing is becoming one of the most utilized tools in an increasing number of fields from Industry 4.0 concepts, engineering, and manufacturing to aerospace and medical applications. One important issue with additive-manufactured components is their orthotropic behaviour where mechanical properties are concerned. This behaviour is due to the layer-by-layer manufacturing process and is particularly hard to predict since it depends on a number of factors, including the manufacturing parameters used during the manufacturing process (speed, temperature, etc.). This study aimed to create and train an artificial neural network-based predictive model using empirical tensile strength data obtained from additive manufactured test parts using the FDM method and PLA material. The predictive model was designed to predict mechanical characteristics for different orientation axis, which were used to set the material properties for finite element analysis. Results indicate a strong correlation between predicted finite element analysis behaviour and real-world tests on additive-manufactured components. The neural network model was trained to an accuracy of ~93% for predicting the mechanical characteristics of 3D-printed PLA material. Using the predicted mechanical characteristics for defining a custom orthotropic material profile in finite element analysis, the simulated failure mode and the behaviour of a complex geometry component agreed with the real-world test.

Additive manufacturing technologies present a series of advantages such as high flexibility, direct CAD to final product fabrication, and compact production techniques which make them an attractive option for fields ranging from medicine and aeronautics to rapid prototyping and Industry 4.0 concepts. However, additive manufacturing also presents a series of disadvantages, the most notable being low dimensional accuracy, low surface quality, and orthotropic mechanical behaviour. These characteristics are influenced by material properties and the process parameters used during manufacturing. Therefore, a predictive model for the characteristics of additive manufactured components is conceivable. This paper proposes a study on the feasibility of implementing Deep Neural Networks for predicting the dimensional accuracy and the mechanical characteristics of components obtained through the Fused Deposition Modelling method using empirical data acquired by high precision metrology. The study is performed on parts manufactured using PETG and PLA materials with known process parameters. Different Deep Neural Network architectures are trained using datasets acquired by high precision metrology, and their performance is tested by comparing the mean absolute error of predictions on training and validation data. Results show good model generalisation and convergence at high accuracy, indicating that a predictive model is feasible.

The aim of this paper is to develop an Artificial Neural Network (ANN) model for springback prediction in the free cylindrical bending of metallic sheets. The proposed ANN model was developed and tested using the Matlab software. The input parameters of the proposed ANN model were the sheet thickness, punch radius, and coefficient of friction. The resulting data is represented by the springback coefficient. Preparation, assessing and confirmation of the model were achieved using 126 data series obtained by Finite element analysis (FEA). ANN was trained by Levenberg - Marquardt back - propagation algorithm. The performance of the ANN model was evaluated using statistic measurements. The predictions of the ANN model, regarding FEA, had quite low root mean squared error (RMSE) values and the model performed well with the coefficient of determination values. This shows that the developed ANN model leads to the idea of being used as an instrument for springback prediction.

In this paper the authors shows hydroforming of tubular parts. Also it presents the technology of the hydroforming of the tubular parts. It is presented some experimental research compared with the prediction of the numerical simulation of this process. There are presented also the mechanical parameters of the material which are used in the field of the deforming process.

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology.

In this paper the authors present the hydroforming of the tubular parts. It also presents the technology of the tubular parts' hydroforming. In this paper we determine the forces necessary for the hydroforming process. They are also presented the mechanical parameters of the material that are used in the deformation process. Also, the variation in wall thickness of the aluminium specimen is determined before the deformation process