Use of additive manufacturing on models for sand casting process

Casting is a forming process based on material pouring in liquid state. Heat is applied to melt the material from its solid state and pour it in a mold previously constructed to obtain the desired shape after letting it cool. Molds can be permanent or expendable. In sand casting processes, the mold is manufactured from a model that usually is extracted before pouring the melted material (Figure 1). To obtain the part, the sand model needs to be destroyed. Notwithstanding, the sand can be reused several times for new molds. Several elements are needed to obtain a part by a sand casting process: permanent patterns, flasks (cope and drag), gating system (pouring cup, sprue, risers and feeding channels) and cores (only if it is needed). The aim of this work is the manufacturing of some of these elements by additive manufacturing process. The equipment will be used for the practical activities (Figure 1) that currently take place on the subjects where this topic is taught, in the different Degrees in Industrial Engineering at the University of Malaga.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Fatigue test bench manufacturing by reusing a parallel lathe

Fatigue life of machined parts strongly depends on their surface condition. The rotating bar bending fatigue testing method is widely used to obtain the fatigue behavior of metallic materials due to its simplicity. In this work, the methodology for the design, manufacturing and setup of a fatigue test bench is exposed. The main novelty lies on the reuse of several elements from an old parallel lathe, currently out of order, and their use to manufacture some parts for the test bench. In this way, a double objective is achieved: high quality elements are recycled and the machine manufacturing cost is reduced.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Indirect Adhesion Wear Parametric Analysis in the Dry Turning of UNS A97075 Alloys

In this work a study of the influence of cutting parameters (cutting speed, feed and depth of cut) on indirect adhesion tool wear in the dry machining of UNS A97075 Al-Zn alloys has been made. In addition, an experimental methodology for a first approach to the measurement of Built-Up-Layer on the tool has been developed. Finally, different parametric models have been obtained, which allow predicting the BUL evolution as a function of cutting parameters applied.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Influence of printing parameters and short carbon fibre reinforcement on fatigue behaviour, dimensional accuracy and macrogeometrical deviations of polylactic acid in material extrusion

This paper evaluates the potential of short carbon fibres as a reinforcement material in order to improve the fatigue resistance of PLA. The fatigue behaviour has been analysed through rotational bending fatigue tests. The influence of printing parameters, such as layer thickness, printing temperature and printing speed, on the mechanical behaviour, dimensional accuracy and macrogeometrical deviations of printed parts have also been analysed as they can too interfere with the mechanical behaviour of the parts. The results show that there is no improvement on the mechanical behaviour of the printed parts with the incorporation of short carbon fibres. On the contrary, the fatigue behaviour worsens due to the poor adhesion between the short carbon fibres and the PLA matrix. Fatigue life is reduced by 6% compared to PLA. Focusing only on the printing parameters, it is shown that at the highest temperature allowed, the fatigue behaviour improves a 12%. The Printing speed is the least influential variable, with the layer thickness having the greatest influence, increasing fatigue life by 15% comparing 0.1 mm and 0.3 mm. Therefore, the best combination would be to print with the highest temperature and the highest layer thickness, for this case study. Finally, a parametric relationship is presented in order to relate the layer thickness with the fatigue behaviour.FundiFunding for open access charge: Universidad de Málaga / CBU

Digitalización de la fábrica de azúcar San Fernando (Sevilla) mediante el uso de vehículos aéreos no tripulados

La historia de la producción de azúcar en España se remonta a casi mil años, con la introducción del cultivo de la caña de azúcar en Al-Andalus. La industria del azúcar tuvo un gran interés en Andalucía durante los siglos XIX y XX, siendo la caña de azúcar la materia prima en los ingenios ubicados en su costa oriental y la remolacha en industrias implantadas principalmente en la zona occidental. En la actualidad, esta actividad fabril está prácticamente desaparecida, quedando únicamente restos de las fábricas que se encuentran en mal estado de conservación. Un importante ejemplo de esta actividad fabril fue la fábrica de azúcar San Fernando, ubicada en Los Rosales (Tocina, Sevilla), cuya producción a partir de remolacha comenzó en 1941. Además de esta actividad, contigua a la azucarera, se desarrolló La Destilería Los Rosales, aprovechando el propio proceso de producción de azúcar. Su estado actual es ruinoso, sin que se aprecie interés público o privado de retomar la actividad productiva a la que se dedicó, por lo que se considera necesario dejar registros gráficos para el futuro. En el presente trabajo se pretende establecer un proceso de digitalización de este bien industrial a partir del uso de vehículos aéreos no tripulados, aplicado al caso particular de la fábrica de azúcar San Fernando. Los vehículos aéreos no tripulados (drones) puede considerarse un recurso de gran utilidad para la generación de registros digitales de este tipo de bienes, generando imágenes aéreas las cuales permiten observar desde otra perspectiva el estado actual de un bien, frente a la fotografía convencional que solo puede obtenerse desde la superficie del terreno. Como complemento, se van a emplear planos originales delineados por el personal de la empresa, que permitirán identificar los elementos constructivos en su distribución original, así como referencias a las consultas con dicho personal, a fin de analizar su historia.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Guide for the implementation of operational control procedures in underwater cutting and welding activities.

Professional diving is an occupation that includes a wide spectrum of different activities, but with the same common denominator: its performance underwater. It is carried out in both fresh and salt waters and encompasses such disparate tasks that professional diving is currently present in numerous productive sectors: hydrocarbon extraction, offshore platforms, port maintenance, public works, civil engineering, infrastructure hydraulic, power plants (hydroelectric, thermal and nuclear), shipbuilding, underwater construction, aquaculture, ship and boat salvage, NDT filming and reporting, teaching and training, and scientific research (geological, biological, archaeological, etc.). It is also characterized by being one of the most dangerous professions and presenting very specific risks, derived from the workplace in a hyperbaric environment, combined with to other hazards of the work activity similar to those of the ground workers. The objective of this study is to analyze procedural models based on the UNE_EN_ISO 45001:2018 Standard, applied to the execution of thermal cutting and underwater welding works. In order to carry out this work safely and in the most effective way, it must be systematically analyzed, generating procedures and work instructions based on the tasks considered critical, and essential knowledge for all personnel who perform them. Such procedures and instructions should be based on a systematic analysis of the steps taken to perform each task, including the risks involved and the safety measures to be taken to control, reduce or, where appropriate, eliminate them.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Dimensional Analysis in Additive Manufacturing Processes with PLA+Carbon Fiber

The rise of Additive Manufacturing (AM) processes is due to the need to reduce material and energy costs. It has the capacity to achieve specialized tools fast while offering the possibility of customize products for the end user. Unlike the majority of the traditional manufacturing processes, AM generally generates the part by depositing material in layers that fuse and create a specific geometry, like Fuse Deposition Modeling (FDM). This comparatively recent technology is still being developed and adapted to the industry and materials requirements. Thus, im-provements are needed in areas like dimensional accuracy, geometric repeatability and material defects, among others, to be able to compare and have the same relia-bility that the products obtained by conventional manufacturing processes. Present work aims to carry out a dimensional control of workpieces designed to fit (axis-hole), so that the influence of certain printing parameters and the final dimensional precision of the specimens can be stablished. The printing parameter relation is studied for a composite material with a PLA matrix, reinforced with Carbon Fibers (PLA+CF), studying the influence of the printing temperature, the layer thickness and the printing direction. This material has been selected because PLA, together with ABS, is one of the most applied materials in AM. Notwithstanding, PLA is easy to process but lacks on resistance, which can be improved introducing carbon fiber as reinforcement. The best result is obtained decreasing the thickness. Also, In comparison with the vertical printing direction,Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Fatigue behaviour analysis of AISI 316-L parts obtained by machining process and additive manufacturing

Due to the great technological growth, 3D printing is becoming of great relevance within the automotive, aerospace and even medicine sectors. With this manufacturing method, parts with a complex geometry can be manufacture with considerable time and material savings compared to traditional processes such as machining. However, additive manufacturing processes still have a series of unresolved problems. Present work makes a comparison between AISI 316-L samples obtained by Selective Laser Melting technique and Dry Machining. The comparison is focus in properties mainly relevant in the industrial sectors highlighted. Macro and microgeometrical deviations, such as roughness, roundness and straightness are obtained in each case study and compared. Results show that, although for the printed samples the material deposition direction plays a fundamental role, being the horizontal samples the ones with better results due to the direction of the layers, the machining process is the one with significant better results compared to the 3D printing process. After the macro and microgeometrical deviations measurements, all samples were subjected to a rotational bending fatigue test for a mechanical behaviour study. As expected, the mechanized specimens have a better fatigue behaviour due to the better surface finish, among other aspects. Between the additive manufactured specimens, the vertical is the one that presents a better behaviour due to the transverse orientation of the deposited layersUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Use of artificial neural networks in the evaluation of geometrical deviations in the dry machining of the UNS A97075 (AL-ZN) alloy..

In this work, an analysis of the cutting parameters influence on macro and micro geometric deviations of dry machined UNS A97075 (Al-Zn) alloy has been carried out. Specifically, the cutting speed and feed rate influence on the arithmetic mean roughness, the straightness and the circular runout of cylindrical specimens have been studied. A shallow artificial neuronal network has been used to obtain a regression model that is able to predict the value of the output variables as a function of the cutting parameters, under the cutting conditions applied. The main novelty of this study lies in obtaining a regression model of the experimental results that considers several geometric variables simultaneously, on a micro and macro scale. For this purpose, the optimal number of neurons in the hidden layer, that gives rise to a minimum error, was analysed. After the network training, most of the results (around 80%) showed a prediction error lower than 10%. These results were compared with other regression models (potential and exponential) previously developed in similar research. In all cases, the use of artificial neuronal network gave rise to the best fit, for every output variable studied. Thus, the use of artificial neuronal networks has been shown as an effective tool in obtaining regression models that combine variables of different nature simultaneously, marking a starting point for future analyses related to the influence of cutting parameters on surface integrity variables on the sustainable machining of this alloy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Predictive models based on RSM and ANN for roughness and wettability achieved by laser texturing of S275 carbon steel alloy

Laser texturing is increasingly gaining attention in the field of metal alloys due to its ability to improve surface properties, particularly in steel alloys. However, the input parameters of the technology must be carefully controlled to achieve the desired surface roughness. Roughness is critical to the activation of the surface before further bonding operations, and it is often assessed using several parameters such as Ra, Rt, Rz, and Rv. This surface activation affects the properties of the metal alloy in terms of wettability, which has been evaluated by the deposition of ethylene glycol droplets through a contact angle. This allowed a direct relationship to be established between the final roughness, the wettability of the surface and the texturing parameters of the alloy. This raises the interest of being able to predict the behaviour in terms of roughness and wettability for future applications in improving the behaviour of metallic alloys. In this research, a comparative analysis between Response Surface Models (RSM) and predictive models based on Artificial Neural Networks (ANN) has been conducted. The model based on neural networks was able to predict all the output variables with a fit greater than 90%., improving that obtained by RSM. The model obtained by ANN allows a greater adaptability to the variation of results obtained, reaching deviations close to 0.2 μm. The influence of input parameters, in particular power and scanning speed, on the achieved roughness and surface wettability has been figured out by contact angle measurements. This increases its surface activation in terms of wettability. Superhydrophilic surfaces were achieved by setting the power to 20 W and scanning speed to ten mm/s. In contrast, a power of 5 W and a scanning speed of 100 mm/s reduced the roughness values.Funding for open access charge: Universidad de Málaga / CBU