Abstract

Wire electrical discharge machining (EDM) possesses many advantages over the conventional manufacturing process. Hence, this process was used for machining of all conductive materials, especially, nowadays this is the most common process for machining of aerospace aluminum alloys. This process produces complex shapes in aluminum alloys with extremely tight tolerances in a single setup. But, for good surface integrity and longer service life, the residual stresses generated on the components should be as low as possible and it depends on the setting of process parameters and the material to be machined. In wire EDM, much of the work was concentrated on Titanium alloys, Inconel alloys and various types of steels and partly on aluminum alloys. The present investigation was a parametric analysis of wire EDM parameters on residual stresses in the machining of aluminum alloy using Taguchi method. The results obtained had shown a wide range of residual stresses from 8.2 to 405.6 MPa. It also influenced the formation of various intermetallics such as AlCu and AlCu3. Microscopic examination revealed absence of surface cracks on aluminum surface at all the machining conditions. Here, an attempt was made to compare the results of aluminum alloy with the available machined data for other metals.

Abstract

An interview with Karen Spence, assistant director of Information Systems, Office of Scientific and Technical Information (OSTI), United States Department of Energy, describes the major OSTI databases, repositories and scientific and technical information co-operation projects (especially on energy and environment).

Abstract

This article reviews known approaches to determining the uncertainty of predictive models: probabilistic analytical, probabilistic simulation and fuzzy. The main elements determining the specificity of a given approach are shown. The advantages and disadvantages are compared. Finally, the application guidelines are listed.

Abstract

In recent times, polymer matrix composite material technologies have undergone a true revolution driven by the boom in demand, especially in the transportation and energy sectors. Composites are increasingly being incorporated due to the significant weight reduction they offer in structural components, which leads to lower energy consumption in transportation systems. Ambitious EU sustainability policies (such as the EU Green Deal and the New Industrial Strategy) further accelerate this shift towards efficient and sustainable solutions, with a major impact on the transportation sector—particularly in the automotive industry, both for conventional and emerging propulsion systems. The use of lightweight materials is expected to increase from the current 30% to 70% by 2030, with composites representing approximately 20%.

However, the automotive sector is one of the most demanding in terms of processing times. Therefore, optimizing and monitoring process parameters through embedded sensors in molds is seen as a valuable tool to adjust processing times and enable continuous quality control of components.

This paper presents the results obtained within the framework of the COMPCERTO project, which aims to develop solutions that support the optimization of manufacturing structural components through thermforming processes using Sheet Molding Compound (SMC) materials. The project involves the implementation of sensors to monitor curing, pressure, and temperature within the molds, enabling the acquisition of representative real-time data at critical points during the thermoforming process in the press.

In order to determine the most suitable manufacturing parameters, preliminary tests are conducted to optimize the curing cycle. Based on the results, a Design of Experiments (DoE) is proposed. Finally, mechanical, thermal, and microstructural characterizations are performed on the samples defined in the DoE. This will generate a database that helps correlate real-time process data with potential defects in SMC parts.