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.

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Abstract: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.

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This paper presents the basic principle for achieving a custom implant from biocompatible materials with the human body using Additive Manufacturing technologies. Due the fact that is a new product which will be introduced on the market, a marketing study was needed. This study presents also the mathematical, analytical and graphical modeling of the psychological price, for a custom implant type cranioplasty / hip prosthesis / spinal implant. The assessment of psychological price for a custom cranioplasty implant, that is not the object through curative Romanian health programs, bring relevant information on the knowledge of maximum and minimum limits that the purchasers, potential consumers, are willing to accept, so the price at which the proportion of consumers potential is the greatest.

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 I want to presents the process for manufacturing one complex parts made by aluminum alloy. For manufacturing this complex part I used CAD/CAM software, CNC milling machine and same special tools. Starting from the 3D model made in SolidWorks was manufactured this complex part, using new strategies for CNC milling. To be made this chain of pieces it is necessary to use smart software for this process.

The drilling process in real production places ever-increasing demands on the length and accuracy of the holes made. The drilling of holes beyond a length-to-diameter ratio of 5–10 is called deep drilling. The aim of the research was to determine in detail the deep-drilling process input conditions, their impact on the stability of the cutting process and the degree to which the output requirements were achieved. The focus of the analysis was on how the monitored technological and physical impacts translate into achieving the required gun-drill life and the quality and dimensional accuracy of deep holes, as well as their overall impact on tool life. Based on the analysis, tests were conducted to verify the impact of individual parameters on tool life. The obtained results were then statistically evaluated and optimized. Drawing on the evaluated experimental results, solutions and procedures were proposed and implemented in the environment of a real operation. This research obtained the optimal values of the frequency of rotation and displacement to ensure maximum tool life while maintaining the efficiency of the production of drilled parts. At the same time, based on the research, a methodology and recommendations for deep-drilling technology were developed.