application of numerical simulation for the estimation of die life after repeated hot forging work cycles

Abstract Die life estimation in hot forging processes is a compelling challenge, due to the number of factors, mainly wear and plastic deformation induced by thermal effects (tempering). The extent of the heating-cooling cycle and the steady state die temperature are known only after hundredth of work cycles. In the paper a realistic work sequence of repeated forging is simulated by the Finite Elements Method on a symmetrical workpiece geometry, for ease of calculation. Tool wear and tempering-induced deformation are estimated along the complete die life cycle, with the help of Neural Network Regression

neural network multiobjective optimization of hot forging

Abstract Hot Forging optimization depends on several factors, known with uncertainty: die pre-heating, geometry, tempering, workpiece temperature and shape, lubricant. There are also several objectives: quality, energy consumption and tool life. Global optimization methods require a numerous process evaluations to reach the optimum. While tests can be simulated by Finite Element Method (FEM), most of them were substituted by a Neural Network model. Neural Network training is less sensitive to problem dimension than standard Design of Experiments. The approach is assessed against the traditional Finite Element Optimization by exploiting a case study of a steel disc

Prediction of Poly-methyl-methacrylate Laser Milling Process Characteristics Based on Neural Networks and Fuzzy Data☆

Abstract Laser milling is a recent technology adopted in rapid prototyping to produce tool, mould and polymer-based microfluidic devices. In this process, a laser beam is used to machine a solid bulk, filling the area to be machined with a number of closely spaced parallel lines. Compared to traditional machining, this method has some advantages, such as: greater flexibility of use, no mechanical contact with the surface, a reduction in industrial effluents, a fine accuracy of machining, even with complex forms, and the possibility to work different kind of materials. While it is relatively easy to predict the depth of the area worked, the surface roughness is more difficult to predict due to the materials behaviors at microscopic level. This is truer when polymer processing is considered due to the local thermal effects. The paper addresses the application of an artificial neural network computing technique to predict the depth and the surface roughness in laser milling tests of poly-methyl-methacrylate. The tests were carried out adopting a CO 2 laser working in continuous and pulsed wave mode. The obtained results showed a good agreement between the model and the experimental data. As a matter of fact, despite the thermal degradation that occurs on the PMMA surface, neural network processing offers an effective method for the prevision of roughness parameters as a function of the adopted process parameters

Cognitive Decision-making Systems for Scraps Control in Aerospace Turbine Blade Casting☆

Abstract The competitiveness of a casting system in modern lost wax production of superalloy turbine blades strongly depends on the reduction of scraps, which commonly affect superalloy cast parts. In order to achieve a focused goal of competitiveness, some key and vital parameters (Key Process Variables) have to be continuously taken under control to make very accurate predictions of Target Variables, which represent, as mapped KPVs domain, the ultimate performance of the entire production link. Such an approach is based on the development of robust control monitoring of the ceramic shell manufacture, which is specifically conceived to foster a possible reduction of scraps in the production if superalloy components. The concerned control will take into consideration data coming from both sensors and measured values in laboratory. The sensor data, which is originated from both new adopted inline and offline equipments at Europea Microfusioni Aerospaziali S.p.A. (EMA) and data measured in the EMA laboratories, will be merged into a sensor pattern vector which represents the basis to develop the EMA demonstrator within the Intelligent Fault Correction and self Optimizing manufacturing systems EU project funded in FP7. The sensor pattern vector will be used to feed an automatic system for the prediction of the process vital parameters. An automated system, based on artificial intelligence paradigms, in particular neural networks, will be fed with the data coming from the sensor pattern vector in order to produce an optimal multi-object output

On the Porous Structuring using Unit Cells

Abstract This study presents the characteristics of the eleven commonly used porous structures. The structures are designed using ten different unit cells. Some of the unit cells consist of free-form surfaces (e.g., triply periodic minimal surface). Some of them are straightforward in design (e.g., honeycomb structure). Some of them have a hybrid structure. The 3D CAD models of the structures are created using commercially available CAD software. The finite element analysis is conducted for each structure to know how it behaves under a static load. The structures are also manufactured using a 3D printer to confirm the manufacturability of them. It is found that some of the structures are easy to manufacture, and some are not. Particularly, metal-alloy-printed structures need a minimal thickness. However, the structures' printed or virtual models are evaluated by determining their respective mass, production cost, production time, Mises stress, and surface area. Using the values of mass, production time and cost, Mises stress, and surface area, the optimal structure is identified. Thus, the outcomes of this study can help identify the optimal porous structure for a given purpose

tiphys an open networked platform for higher education on industry 4 0

Abstract Objective of Tiphys project is building an Open Networked Platform for the learning of Industry 4.0 themes. The project will create a Virtual Reality (VR) platform, where users will be able to design and create a VR based environment for training and simulating industrial processes but they will be able to study and select among a set of models in order to standardize the learning and physical processes as a virtual representation of the real industrial world and the required interactions so that to acquire learning and training capabilities. The models will be structured in a modular approach to promote the integration in the existing mechanisms as well as for future necessary adaptations. The students will be able to co-create their learning track and the learning contents by collaborative working in a dynamic environment. The paper presents the development and validation of the learning model, built on CONALI learning ontology. The concepts of the ontology will be detailed and the platform functions will be demonstrated on selected use cases

Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Abstract Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis

Prediction of Dressing in Grinding Operation via Neural Networks

Abstract In order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented

Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Abstract This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared