Analysis of Energy and Material Consumption for the Manufacturing of an Aeronautical Tooling: An Experimental Comparison between Pure Machining and Big Area Additive Manufacturing.

Additive manufacturing (AM) has been fully incorporated into both the academic and the industrial world. This technology has been shown to lower costs and environmental impacts. Moreover, AM-based technologies, such as wire arc additive manufacturing (WAAM), have been proven suitable for the manufacturing of large products with significant mechanical requirements. This study examines the manufacture of two aeronautical toolings: first, using conventional techniques, and second, using a big area additive manufacturing (BAAM) process, specifically WAAM technology, followed by second-stage hybrid machining. Both toolings can be considered interchangeable in terms of design and performance. Energy and material consumption were analysed and compared throughout both tooling procedures. The results show the important optimisation of both procedures in manufacturing WAAM tooling, encompassing the additive process and second-stage hybrid machining. Nevertheless, the time required for WAAM tooling manufacturing increased significantly compared to conventional manufacturing tooling. Moreover, based on metrology data from the AM process, a theoretical study was conducted to assess different design optimisations for WAAM tooling manufacturing and determine their influence on material and energy consumption. These theoretical results improve those already obtained regarding energy and raw material savings.

Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks.

The use of recycled polypropylene in industry to reduce environmental impact is increasing. Design for manufacturing and process simulation is a key stage in the development of plastic parts. Traditionally, a trial-and-error methodology is followed to eliminate uncertainties regarding geometry and process. A new proposal is presented, combining simulation with the design of experiments and creating prediction models for seven different process and part quality output features. These models are used to optimize the design without developing additional time-consuming simulations. The study aims to compare the precision and correlation of these models. The methods used are linear regression and artificial neural network (ANN) fitting. A wide range of eight injection parameters and geometry variations are used as inputs. The predictability of nonlinear behavior and compensatory effects due to the complex relationships between this wide set of parameter combinations is analyzed further in the state of the art. Results show that only Back Propagation Neural Networks (BPNN) are suitable for correlating all quality features in a single formula. The use of prediction models accelerates the optimization of part design, applying multiple criteria to support decision-making. The methodology is applied to the design of a plastic support for induction hobs. Furthermore, this methodology has demonstrated that a weight reduction of 27% is feasible. However, it is necessary to combine process parameters that differ from the standard ones with a non-uniform thickness distribution so that the remaining injection parameters, material properties, and dimensions fall within tolerances.

Tribological Performance of Nylon Composites with Nanoadditives for Self-Lubrication Purposes.

A systematic study comparing the wear behaviour of composites with nylon matrix (PA66, PA46, PA12) and different nanoadditives and reinforcing additives (graphite, graphene, MoS2 and ZrO2) has been carried out in order to achieve a proper self-lubricant material for bearing cages. The wear characterisation was done using pin-on-disc tests, SEM and EDX analysis. The results show that better outcomes are obtained for composites based on PA12. The addition of ZrO2 offers negative values of wear due to the metallic particle transference from the counterface to the polymeric pin.

Correction: Elduque, A., et al. Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.

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A New Multiparameter Model for Multiaxial Fatigue Life Prediction of Rubber Materials.

Most of the mechanical components manufactured in rubber materials experience fluctuating loads, which cause material fatigue, significantly reducing their life. Different models have been used to approach this problem. However, most of them just provide life prediction only valid for each of the specific studied material and type of specimen used for the experimental testing. This work focuses on the development of a new generalized model of multiaxial fatigue for rubber materials, introducing a multiparameter variable to improve fatigue life prediction by considering simultaneously relevant information concerning stresses, strains, and strain energies. The model is verified through its correlation with several published fatigue tests for different rubber materials. The proposed model has been compared with more than 20 different parameters used in the specialized literature, calculating the value of the R2 coefficient by comparing the predicted values of every model, with the experimental ones. The obtained results show a significant improvement in the fatigue life prediction. The proposed model does not aim to be a universal and definitive approach for elastomer fatigue, but it provides a reliable general tool that can be used for processing data obtained from experimental tests carried out under different conditions.

Dimensional Stability and Process Capability of an Industrial Component Injected with Recycled Polypropylene.

The usage of recycled polymers for industrial purposes arises as one of the most promising methods of reducing environmental impact and costs associated with scrapping parts. This paper presents a systematic study of the dimensional stability of a raw and 100% recycled polypropylene subjected to realistic environmental conditions occurring along its working life. The component studied is an internal part of an induction hob assembly. Industrial samples manufactured with both materials, in the same mold, and in the same injection machine, are subjected to ejection conditions, storage conditions (50 °C), and extreme performance conditions (80 °C). Induced dimensional changes are registered and analyzed using a coordinate measuring machine, and a tactile sensing probe. To verify the process capability of the samples manufacturing, Cp and Cpk values are calculated to evaluate the suitability of the recycled material as an alternative. Results conclude that, although the use of recycled material implies slight differences in terms of dimensional stability due to the changes induced in the polymer structure, these differences are not significant enough to affect the injection process capability. Therefore, recycling arises as one effective method to reduce both overruns associated with the consumption of raw polypropylene material and its environmental impact.

Influence of the Composition on the Environmental Impact of Soft Ferrites.

The aim of this paper is to analyze the influence of the composition on the environmental impact of the two main types of soft ferrites, allowing scientists and engineers to compare them based not only on cost and properties, but also on an environmental point of view. Iron oxides are the basis of soft ferrites, but these ferrites have a wide range of compositions, using materials such as manganese or nickel, which affect their magnetic properties, but also modify the environmental impact. A Life Cycle Assessment has been carried out for manganese‒zinc (MnZn) and nickel‒zinc (NiZn) soft ferrites, with a Monte Carlo approach to assess multiple compositions. The LCA model was developed with SimaPro 8.4, using the EcoInvent v3.4 life cycle inventory database. Environmental impact values were calculated under the ReCiPe and Carbon Footprint methodologies, obtaining a broad variety of results depending on the composition. The results were also significantly different from the standard EcoInvent ferrite. For the analyzed soft ferrites, the presence of manganese or nickel is a key factor from an environmental perspective, as these materials involve high environmental impacts, and their supply risk has increased during recent years, making them a concern for European manufacturers.

Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.

Polymer injection-molding is one of the most used manufacturing processes for the production of plastic products. Its electricity consumption highly influences its cost as well as its environmental impact. Reducing these factors is one of the challenges that material science and production engineering face today. However, there is currently a lack of data regarding electricity consumption values for injection-molding, which leads to significant errors due to the inherent high variability of injection-molding and its configurations. In this paper, an empirical model is proposed to better estimate the electricity consumption and the environmental impact of the injection-molding process. This empirical model was created after measuring the electricity consumption of a wide range of parts. It provides a method to estimate both electricity consumption and environmental impact, taking into account characteristics of both the molded parts and the molding machine. A case study of an induction cooktop housing is presented, showing adequate accuracy of the empirical model and the importance of proper machine selection to reduce cost, electricity consumption, and environmental impact.

Visco-Hyperelastic Model with Damage for Simulating Cyclic Thermoplastic Elastomers Behavior Applied to an Industrial Component.

In this work a nonlinear phenomenological visco-hyperelastic model including damage consideration is developed to simulate the behavior of Santoprene 101-73 material. This type of elastomeric material is widely used in the automotive and aeronautic sectors, as it has multiple advantages. However, there are still challenges in properly analyzing the mechanical phenomena that these materials exhibit. To simulate this kind of material a lot of theories have been exposed, but none of them have been endorsed unanimously. In this paper, a new model is presented based on the literature, and on experimental data. The test samples were extracted from an air intake duct component of an automotive engine. Inelastic phenomena such as hyperelasticity, viscoelasticity and damage are considered singularly in this model, thus modifying and improving some relevant models found in the literature. Optimization algorithms were used to find out the model parameter values that lead to the best fit of the experimental curves from the tests. An adequate fitting was obtained for the experimental results of a cyclic uniaxial loading of Santoprene 101-73.

Influence of Composition on the Environmental Impact of a Cast Aluminum Alloy.

The influence of alloy composition on the environmental impact of the production of six aluminum casting alloys (Al Si12Cu1(Fe), Al Si5Mg, Al Si9Cu3Zn3Fe, Al Si10Mg(Fe), Al Si9Cu3(Fe)(Zn) and Al Si9) has been analyzed. In order to perform a more precise environmental impact calculation, Life Cycle Assessment (LCA) with ReCiPe Endpoint methodology has been used, with the EcoInvent v3 AlMg3 aluminum alloy dataset as a reference. This dataset has been updated with the material composition ranges of the mentioned alloys. The balanced, maximum and minimum environmental impact values have been obtained. In general, the overall impact of the studied aluminum alloys varies from 5.98 × 10-1 pts to 1.09 pts per kg, depending on the alloy composition. In the analysis of maximum and minimum environmental impact, the alloy that has the highest uncertainty is AlSi9Cu3(Fe)(Zn), with a range of ±9%. The elements that contribute the most to increase its impact are Copper and Tin. The environmental impact of a specific case, an LED luminaire housing made out of an Al Si12Cu1(Fe) cast alloy, has been studied, showing the importance of considering the composition. Significant differences with the standard datasets that are currently available in EcoInvent v3 have been found.

The Influence of Different Recycling Scenarios on the Mechanical Design of an LED Weatherproof Light Fitting.

This paper analyzes the high relevance of material selection for the sustainable development of an LED weatherproof light fitting. The research reveals how this choice modifies current and future end of life scenarios and can reduce the overall environmental impact. This life cycle assessment has been carried out with Ecotool, a software program especially developed for designers to assess the environmental performance of their designs at the same time that they are working on them. Results show that special attention can be put on the recycling and reusing of the product from the initial stages of development.