Ingenius

Tensile/Compressive Response of 316L Stainless Steel Fabricated by Additive Manufacturing

2023-11-27T15:11:52+00:00

Additive manufacturing has evolved from a rapid prototyping technology to a technology with the ability to produce highly complex parts with superior mechanical properties than those obtained conventionally. The processing of metallic powders by means of a laser makes it possible to process any type of alloy and even metal matrix composites. The present work analyzes the tensile and compressive response of 316L stainless steel processed by laser-based powder bed fusion. The resulting microstructure was evaluated by optical microscopy. Regarding the mechanical properties, the yield strength, ultimate tensile strength, percentage of elongation before breakage, compressive strength and microhardness were determined. The results show that the microstructure is constituted by stacked micro molten pools, within which cellular sub-grains are formed due to the high thermal gradient and solidification rate. The compressive strength (1511.88 ± 9.22 MPa) is higher than the tensile strength (634.80 ± 11.62 MPa). This difference is mainly associated with strain hardening and the presence of residual stresses. The initial microhardness was 206.24 ± 11.96 HV; after the compression test, the hardness increased by 23%.

Prediction of abrasive wear and surface hardness of printed parts by SLA technology

2024-03-26T17:06:04+00:00

In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

Optimization algorithms for adaptative route sequencing on real-world last-mile deliveries

2024-03-26T17:05:02+00:00

This article explores the design and application of machine learning techniques to enhance traditional approaches for solving NP-hard optimization problems. Specifically, it focuses on the Last-Mile Routing Research Challenge (LMRRC), supported by Amazon and MIT, which sought innovative solutions for cargo routing optimization. While the challenge provided travel times and zone identifiers, the dependency on these factors raises concerns about the algorithms’ generalizability to different contexts and regions with standard delivery services registries. To address these concerns, this study proposes personalized cost matrices that incorporate both distance and time models, along with the relationships between delivery stops. Additionally, it presents an improved approach to sequencing stops by combining exact and approximate algorithms, utilizing a customized regression technique alongside fine-tuned metaheuristics and heuristics refinements. The resulting methodology achieves competitive scores on the LMRRC validation dataset, which comprises routes from the USA. By carefully delineating route characteristics, the study enables the selection of specific technique combinations for each route, considering its geometrical and geographical attributes. Furthermore, the proposed methodologies are successfully applied to real-case scenarios of last-mile deliveries in Montevideo (Uruguay), demonstrating similar average scores and accuracy on new testing routes. This research contributes to the advancement of last-mile delivery optimization by leveraging personalized cost matrices and algorithmic refinements. The findings highlight the potential for improving existing approaches and their adaptability to diverse geographic contexts, paving the way for more efficient and effective delivery services in the future.

Editorial

2023-12-26T15:48:26+00:00

A non-linear optimization model assessment for the economic dispatch of isolated microgrids

2024-03-26T16:59:22+00:00

The present research work shows the optimal energy management of an isolated microgrid based on non-conventional renewable energy sources. For which an economic dispatch problem is proposed that seeks to supply the electrical demand at the lowest possible operating cost, based on a mixed integer nonlinear optimization problem. The nonlinearity of the algorithm is presented by including the characteristic equation of the real operation of the generating set in the optimization model. The input data to the economic office such as solar radiation and wind speed were obtained from the NASA platform located on Santa Cruz Island, Galapagos province, Ecuador. In addition, the electricity demand data was obtained from real measurements of the sector. The economic dispatch problem has been determined for 12, 24 and 168 hours respectively, obtaining a proportional energy distribution for each case of 50.40% supplied by the photovoltaic generator, 23.92% by the diesel generator, 17.14% by the battery bank and 5.53% by the wind generator, so the demand was supplied in its entirety, meeting the objective that the generating set does not present intermittencies and obtaining the lowest operating cost of the system.

Electric substation inspection: YOLOv5 in hotspot detection through thermal imaging

2024-03-26T17:01:43+00:00

Substations are key facilities within an electrical system, untimely failures tend to cause low quality and negative effects on the electrical supply. An early indicator of potential electrical equipment failure is the appearance of hot spots; therefore, its detection and subsequent programmed correction avoids incurring in major failures and unnecessary operation stops. In this research, 64 experiments of the YOLOv5 algorithm were carried out, with the purpose of proposing an automated computer vision mechanism for the detection of hot spots in thermal images of electrical substations. The best results show a mAP value of 81.99%, which were obtained with the YOLOv5m algorithm and the transfer learning application. These results leave a basis to deepen and improve the performance of the algorithm by varying other hyperparameters to those considered in this study.

Network centrality measures for classifying important components in electrical power systems based on linegraph transformation

2024-03-26T17:04:02+00:00

Network theory techniques have recently contributed to the analysis of electrical power systems, enabling faster computational solutions. Taking advantage of the topological information of a network, it becomes possible to characterize its elements both locally (individual network components) and globally (interactions and behavior of the components). Identifying the crucial elements within an electrical system involves classifying each component based on its interaction with the entire network, considering, possibly, various operating conditions. Current network centrality measures predominantly focus on nodes, which represent connection buses in the system, to quantify the significance of individual elements. In this study, we employ the linegraph technique to transform links into nodes. Subsequently, we calculate and categorize the links (representing lines and transformers) of different electrical networks found in the literature using three centrality measures. Moreover, our methodology allows for the aggregation or combination of the indices from each measure, leading to a unified classification based on the importance of links in the considered electrical power systems. Analyzing diverse networks reveals a consistent empirical distribution of centrality indices, resulting in similar classifications of significant elements regardless of network size.

Liquefied Petroleum Gas Systems: A Review On Desing And Sizing Guidelines

2024-03-26T17:06:49+00:00

Liquefied Petroleum Gas (LPG) is a fossil fuel widely used in residential, commercial, and industrial applications. LPG systems must be designed and sized under minimum safety standards, which are established in national and international regulations. An LPG system is composed of fuel storage containers, pipelines, valves, meters, consumption equipment, and protection and safety elements. These must be sized and selected to withstand the action of the fuel gas and the working conditions to which they will be subjected. This document presents a review of the most important points to consider in the design and sizing of an LPG system based on the most representative international regulations.

Reuse of Electrical Vehicle Batteries for Second Life Applications in Power Systems with a High Penetration of Renewable Energy: A Systematic Literature Review

2024-03-26T17:07:07+00:00

This article presents a systematic literature review on the reuse of electric vehicle batteries (EVB) for second-life applications in power systems. The end-oflife of these batteries represents a major environmental problem due to their composition and materials. The study aims to analyze the reuse of EVBs as a sustainable alternative for the environment. Additionally, it seeks to provide complementary services to facilitate the incorporation of intermittent unconventional renewable generation into the electrical grid. Through an exhaustive search of scientific publications indexed in prestigious digital catalogs and their subsequent systematic treatment, a selected group of 49 scientific articles published between 2018 and 2023 have been found in which the different opportunities, benefits and limitations of second-life energy storage systems oriented to boost a circular economy have been identified. The study concludes that, although the reuse of batteries has not yet been fully addressed or implemented due to existing challenges in terms of technology, costs, and regulations, it is of utmost importance to delve deeper into its analysis to improve efficiency and reduce the environmental impacts associated with the manufacturing, use, and disposal of such batteries.

Improvement proposal in the structural system of a15” R29 rigid mountain bike frame, with fea and geometric optimization

2023-03-17T15:10:09+00:00

Currently, the practice of cycling has had a considerable increase, as well as the use of mountain bikes (MTB) with rigid frames, used as a means of transportation and for competition, due to their affordable cost. This type of bicycles, when used for various purposes, present varied stresses in its frame, which leads to exceed the design requirements, presenting failures in the upper chainstays. This type of failure will be analized in this study, which is why the information regarding the frame material, acting loads and 3D modeling is collected. Subsequently, a homologation analysis of the failure is generated and an improvement proposal is determined by applying geometric optimization, where a thickness of 3,50 mm is determined in the upper sheaths, guaranteeing the resistance of the bicycle frame under the study conditions; that is, a drop of 60 cm and a load of 74 kg, this guarantees that the stress in the upper chainstays does not exceed the ultimate stress of the material of 890,94 MPa.

Incidence of automotive air conditioning on the index of fuel consumption in spark ignition vehicle on a route in the ecuadorian amazon

2023-11-13T15:09:12+00:00

In recent years the environment has been affected by pollution produced by vehicles. The objective of this research project was to determine the incidence of air conditioning (A/C) in the vehicular fuel consumption index in the Shushufindi canton, through real traffic tests, Efficient driving mode and the use of Extra and Super gasoline, for the selection of the best alternative. The study was carried out on a route with a greater flow of vehicles, especially during normal (9:00 am) and beak (5:00 pm) hours, which comprises 16.17 km; for which used gasoline Extra (85 octane) and Super (92 octane). Data collection was carried out using an OBD2 ELM 327 system. The results obtained in the characterization of the representative mixed cycle at 9:00 am, a maximum speed of 81 km/h and an average speed of 39 km/h were obtained in a time of 1446 s route; while the mixed cycle at 5:00 pm the maximum speed is 70 km/h and an average speed of 37 km/h with a travel time of 1632 s. The lowest fuel consumption index was evidenced in normal hours, without A/C and Extra fuel (T3) with values between 0.0584 - 0.060 (L/km), and in normal hours, without A/C and super fuel (T7) that are between 0.0561-0.0585 (L/km).