In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks’ inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2^k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models’ predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.

The robust design of turning parameters is dealing with the optimization of surface roughness and cutting force in turning of reinforced polyetheretherketone (PEEK) with 30% of carbon fibers (PEEK CF30) using TiN-coated cutting tools. The selected turning parameters include the cutting speed, feed rate and depth of cut. Grey–Taguchi method is combining orthogonal array design of experiments with relational analysis, which enables the determination of the optimal combination of turning parameters with the multiple criteria. The basic aim of grey relational analysis is to find the grey relational grade, which can be used for the optimization conversion from a multi-criteria problem to a single objective problem. This study not only proposes a novel optimization technique, but also contributes the satisfactory solution for multiple CNC turning objectives with profound insight.

An important range of existing engineered industrial parts consists of plastic materials that are reinforced with carbon fibers. Due to their excellent mechanical and thermal properties, machined mechanical parts made from reinforced polyetheretherketone (PEEK) composite materials have become standard in many high-technology engineering fields such as aerospace, automotive, and electronics. There is however a crucial need to predict the machining criteria for reinforced PEEK composite materials in order to optimize their fabrication process. In this article, the process parameters including cutting speed, feed rate, and depth of cut are investigated. A fuzzy rule-based model was derived to predict the surface roughness parameters R_a and R_t, in dry turning of reinforced PEEK with 30% of carbon fibers using TiN-coated cutting tools. The model was identified using results of experiments carried out according to Taguchi method. Predictions of the fuzzy-based model were found to fit, very well, experimental data with a correlation coefficient as high as 99%.

While the construction industry consumes more raw materials than any other industrial sector, agriculture generates a large amount of waste that is not managed properly. The olive industry produces more than 7.5 million tons of waste that could be recovered. This paper presents a new method to valorize the leaves of olive tree pruning waste consisting of the manufacture of ecologic boards without adhesives by hot pressing. In order to analyze their influence, three manufacturing variables were varied to obtain the boards: leaf type (shredded and whole leaves), temperature (130, 140 and 150 °C) and time (4, and 12 min). Twenty-four boards were made and were then tested for their mechanical, physical and thermal properties according to the EN standards. The boards showed good results of thickness swelling (TS), water absorption (WA) and of thermal conductivity and can be used as an alternative for manufacturing thermal insulation boards. With a smaller particle size of shredded leaves, longer pressing times and higher pressing temperatures, the mechanical behavior of the boards could improve. The olive leaves are a low-cost renewable resource, and manufacturing products with a long, useful life can be beneficial to the environment.

Mechanics & Industry, An International Journal on Mechanical Sciences and Engineering Applications

Mechanical properties of ductile materials are very sensitive to environmental conditions in particular temperature. The paper shows the results obtained in the simultaneous testing of 5182 H111 Aluminium alloy, S275JR Carbon Steel and F114 Carbon Steel specimens using conventional methods and thermography. The infrared thermography was used to quantify deformation localization zones, also to relate the temperature changes of the specimens, continuously recorded by thermography, with the load extension diagram. It enables to predict reaching the critical stresses which cause the appearance of fractures and to define the criteria for determining the maximum sample temperature alteration in the field of elastic and elastic-plastic strains. Infrared thermography was found to be an efficient tool for investigating the deformation and damage scenario under tensile loading.