INVESTIGATION OF SINGLE-PASS/DOUBLE-PASS TECHNIQUES ON FRICTION STIR WELDING OF ALUMINIUM

The aim of this research is to study the effects of single-pass/ double-pass techniques on friction stir welding of aluminium. Two pieces of AA1100 with a thickness of 6.0 mm were friction stir welded using a CNC milling machine at rotational speeds of 1400 rpm, 1600 rpm and 1800 rpm respectively for single-pass and double-pass. Microstructure observations of the welded area were studied using an optical microscope. The specimens were tested by using a tensile test and Vickers hardness test to evaluate their mechanical properties. The results indicated that, at low rotational speed, defects such as ‘surface lack of fill’ and tunnels in the welded area contributed to a decrease in mechanical properties. Welded specimens using double-pass techniques show increasing values of tensile strength and hardness. From this investigation it is found that the best parameters of FSW welded aluminium AA1100 plate were those using double-pass techniques that produce mechanically sound joints with a hardness of 56.38 HV and 108 MPa strength at 1800 rpm compared to the single-pass technique. Friction stir welding, single-pass/ double-pass techniques, AA1100, microstructure, mechanical properties

Friction Stir Welding Manufacturing Advancement by On-Line High Temperature Phased Array Ultrasonic Testing and Correlation of Process Parameters to Joint Quality

Welding, a manufacturing process for joining, is widely employed in aerospace, aeronautical, maritime, nuclear, and automotive industries. Optimizing these techniques are paramount to continue the development of technologically advanced structures and vehicles. In this work, the manufacturing technique of friction stir welding (FSW) with aluminum alloy (AA) 2219-T87 is investigated to improve understanding of the process and advance manufacturing efficiency. AAs are widely employed in aerospace applications due to their notable strength and ductility. The extension of good strength and ductility to cryogenic temperatures make AAs suitable for rocket oxidizer and fuel tankage. AA-2219, a descendent of the original duralumin used to make Zeppelin frames, is currently in wide use in the aerospace industry. FSW, a solid-state process, joins the surfaces of a seam by stirring the surfaces together with a pin while the metal is held in place by a shoulder. The strength and ductility of friction stir (FS) welds depends upon the weld parameters, chiefly spindle rotational speed, feedrate, and plunge force (pinch force for self-reacting welds). Between conditions that produce defects, it appears in this study as well as those studies of which we are aware that FS welds show little variation in strength; however, outside this process parameter “window” the weld strength drops markedly. Manufacturers operate within this process parameter window, and the parameter establishment phase of welding operations constitutes the establishment of this process parameter window. The work herein aims to improve the manufacturing process of FSW by creating a new process parameter window selection methodology, creation of a weld quality prediction model, developing an analytical defect suppression model, and constructing a high temperature on-line phased array ultrasonic testing system for quality inspection

Ultrasonic Spot Welding of Dissimilar Metal Sheets: An Experimental, Numerical and Metallurgical Investigation

Ultrasonic metal welding (USMW) is a new and emerging concept used in the industries over the past twenty years and serving to the manufacturing sectors like aviation, medical, microelectronics, automotive and much more due to various hurdles faced by conventional fusion welding process. USMW is a clean and reliable technique in which the welding takes place with a high energy, no flux or filler metal needed, longer tool life and it takes very short time (less than one second) to weld materials in a perfect controllable environment with greater efficiency.To acquire high vibration amplitude in USMW, there is a necessity to design a welding system that consists of components like a booster and horn. The principal purpose of these parts is to amplify the input amplitude of vibration so that the energy transferred to the welding spot should be sufficient for creating a joint. In the present study, new type of booster and horn are proposed and modelled with adequate precision not only to produce high-quality welds but also to solve a lot of issues faced while designing these types of ultrasonic tools. The modal analysis module of finite element method (FEM) is used to analyze the effects of different step lengths and fillet radius on its natural frequency of 20 kHz, ensuring that these components will be in a resonating condition with other parts of the system. It is found that there were 1.11 % and 2.52 % errors in the length calculation of both parts. Similarly, 0.61 % error is obtained for both while calculating the magnification ratio. However, such low levels of errors may be considered to be insignificant. The dynamic analysis has also been performed to find out the stress distribution in both parts under cyclic loading conditions. Due to these cyclic loading conditions, the nodal regions (hot areas) are under highly stressed, and the relevant temperature field is consequently determined. The results obtained from the simulation, and experimental results were found to be close to each other and an error of 2% was noticed. Other welding components are also fabricated such as anvil, specimen-holder and backing plate for producing a satisfactory weld. Meanwhile, the complex mechanism behind the USMW has been addressed and modelled analytically. This model can predict the forces as well as temperatures those occur during the welding process and also explains the effects of various material properties and surface conditions on the weld behaviour. The experiments have been performed on the aluminium, copper, brass and stainless steel metal sheets with a number of different configurations, anvil designs, and surface conditions. The fundamental aspect of this study is to control the process parameters like vibration amplitude, weld pressure and weld time so that, an appreciable weld strength can be obtained. Thus, tensile shear and T-peel failure load studies suggest that increase in vibration amplitude means the increase of scrubbing action between the faying surfaces, resulting a better bonding strength. Similarly, increase in weld pressure also increases these weld failure loads and reach a peak value at a particular pressure. But, subsequently, these failure loads decrease due to suppression of relative motion between sheets and initiation of cracks. Excessive weld time also causes cracks around the weld spot. Likewise, if the thickness of the sheets increased, weld strengths are also increased due to absorption of more amount of ultrasonic energy. Moreover, the highest weld interface temperatures and weld areas are observed at the end of weld time because of the larger plastic deformation at the mating surfaces. For all the experiments, first anvil design shows maximum failure loads due to its non-cutting width and angle of knurls. Likewise, on the increase of surface roughness, the tensile shear, and T-peel failure loads decrease. It is found that, in lubricating condition, the highest failure loads are obtained. Furthermore, the polynomial regression, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods are developed and compared for each performance measure so that the whole welding process can be accurately described by a best predictive model. A welding mechanics based numerical model has been developed which can predict the temperatures during USMW process for various surface conditions. For all the experimental investigations, the predictive results show good agreement with the experimental values. In addition to it, acoustic softening during ultrasonic welding is found to very significant for the reduction in yield strength of the weld material up to 95 %. It is seen that the quality of welding depends on the material properties, process parameters, and thickness of the workpiece. The present investigation also explains in details the effect of process parameters on the responses through metallurgical analysis. A quality lobe of welding like “under weld”, “good weld” and “over weld” is proposed after observing the fractured samples in optical microscopy and scanning electron microscopy (SEM). Meantime, energy dispersive spectroscopy (EDS) and X- ray diffraction (XRD) analyses are also used to reveal the thickness of interatomic diffusion and IMCs along the weld interface

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

Integrated approach to Wire Arc Additive Manufacturing (WAAM) optimization: Harnessing the synergy of process parameters and deposition strategies

The flexibility of Additive Manufacturing (AM) technologies in the metal 3D printing process has gained significant attention in research and industry, which allows for fabricating complicated and intricate Near-Net-Shape (NNS) geometry designs. The achievement of desired characteristics in Wire-Arc Additive Manufactured (WAAM) components is primarily contingent upon the careful selection and precise control of significant processing variables, including bead deposition strategy, wire materials, type of heat source, wire feed speed, and the application of shielding gas. As a result, optimizing these most significant process parameters has improved, producing higher-quality WAAM-manufactured components. Consequently, this has contributed to the overall rise in the method's popularity and many applications. This article aims to provide an overview of the wire deposition strategy and the optimization of process parameters in WAAM. The optimization of numerous wire deposition techniques and process parameters in the WAAM method, which is required to manufacture high-quality additively manufactured metal parts, is summarised. The WAAM optimization algorithm, in addition to anticipate technological developments, has been proposed. Subsequently, a discussion ensues regarding the potential for WAAM optimization within the swiftly growing domain of WAAM. In the end, conclusions have been derived from the reviewed research work

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university