Additive Manufacturing of Directionally Heat Conductive Objects

Purpose: Directionally Heat Conductive (DHC) objects are also known as thermal cables. DHC objects used in cooling solid state devices as large heat sinks. This paper presents Additive manufacturing (AM) as a new method for realization of DHC objects. AM can produce DHC objects accurately, rapidly and economically. Design/Methodology/Approach: It is very hard to realize DHC objects through conventional manufacturing. The authors describe the realization of metallic DHC objects using their AM process called Hybrid Layered Manufacturing (HLM). HLM, which uses GMAW deposition, can, in principle, produce DHC out of any metal for which welding wire is available. . Differential conductivity in HLM is achieved by appropriately distributing the air gaps among the beads. The air gap distribution depends on the layer thickness and step over increment. Findings: The authors have demonstrated the ability of HLM to produce DHC objects for Al alloy 4043. The thermal conductivity achieved on two orthogonal directions was 100.496 and 129.740 W m-1 K-1 as against 163 W m-1 K-1 for solid metal. These are significant variations. Research Limitations/Implications: HLM realize near net shape of DHC objects, further post processing namely machining is required. Originality Value: AM Specifically HLM for realization of DHC objectsMechanical Engineerin

Varying Feedback Strategy and Scheduling in Simulator Training: Effects on Learner Perceptions, Initial Learning, and Transfer

This experimental study investigated the effects of visual feedback on initial learning, perceived self-efficacy, workload, near transfer, far transfer, and perceived realism during a simulator-based training task. Prior studies indicate that providing feedback is critical for schema development (Salmoni, Schmidt, & Walter 1984; Sterman, 1994). However, its influence has been shown to dissipate and is not directly proportionate to the frequency at which it is given (Wulf, Shea, & Matschiner, 1998). A total of 54 participants completed the study forming six treatment groups. The independent treatment, visual feedback, was manipulated as scheduling (absolute—every practice trial or relative—every third trial) and strategies (gradual decrease of visual cues within the interface, gradual increase of visual cues within the interface, or a single consistent cue for each trial). Participants completed twelve practice trials of welding under one of six feedback manipulations; then, participants completed twelve practice trials of welding without it. Lastly, participants performed the weld task on actual equipment in a shop area. No treatment showed significant difference among groups with regard to initial learning, retention, near transfer, and far transfer measures. However, a statistical significance was found during initial learning and retention within each treatment group. Findings support empirical evidence that a variability of practice paradigm promotes learning (Lee & Carnahan, 1990; Shea & Morgan, 1979). Learner perceptions of realism suggest that novice learners perceive simulator fidelity as high, however, these perceptions may dissipate as the learner practices. Those groups that involved the greatest number of cues at the onset of practice or having cues available at every other trial reported the greatest amount of workload. All groups reported increases in perceptions of self-efficacy during practice on the simulator, but those perceptions decreased when participants performed the weld task on actual equipment. Findings suggest that contextual-interference of increasing, decreasing, or changing feedback counteracts the guidance effect of feedback as found in previous studies

Quality Assessment of Laser Welding Dual Phase Steels

Since non-conforming parts create waste for industry, generating undesirable costs, it is necessary to set up quality plans that not only guarantee product conformity but also cut the root causes of welding defects by developing the concept of quality at origin. Due to their increasing use in automotive industry, dual phase (DP) steels have been the chosen material for this study. A quality plan for welding DP steel components by laser was developed. This plan is divided into three parts: pre-welding, during and post-welding. A quality assessment regarding mechanical properties, such as hardness, microstructure and tensile strength, was also performed. It was revealed that DP steel does not present considerable weldability problems, except for the usual softening of the heat affected zone (HAZ) and the growth of martensite in the fusion zone (FZ), and the best analysis techniques to avoid failures in these steels are finite element method (FEM), visual techniques during welding procedure and digital image correlation (DIC) for post-weld analysis.The present work was done and funded under the scope of projects UIDB/00481/2020 and UIDP/00481/2020—FCT—Fundação para a Ciencia e a Tecnologia; and CENTRO-01-0145-FEDER- 022083—Centro Portugal Regional Operational Programme (Centro2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund. LAETA/INEGI/CETRIB is acknowledge due to the support provided in all research activities.info:eu-repo/semantics/publishedVersio

TOWARD INTELLIGENT WELDING BY BUILDING ITS DIGITAL TWIN

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by building its digital twin and contributes to digital twin in the following two aspects (1) increasing the information analysis and reasoning ability by integrating deep learning; (2) enhancing the human user operative ability to physical welding manufacturing via digital twins by integrating human-robot interaction (HRI). Firstly, a digital twin of pulsed gas tungsten arc welding (GTAW-P) is developed by integrating deep learning to offer the strong feature extraction and analysis ability. In such a system, the direct information including weld pool images, arc images, welding current and arc voltage is collected by cameras and arc sensors. The undirect information determining the welding quality, i.e., weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed by a traditional image processing method and a deep convolutional neural network (CNN) respectively. Based on that, the weld joint geometrical size is controlled to meet the quality requirement in various welding conditions. In the meantime, this developed digital twin is visualized to offer a graphical user interface (GUI) to human users for their effective and intuitive perception to physical welding processes. Secondly, in order to enhance the human operative ability to the physical welding processes via digital twins, HRI is integrated taking virtual reality (VR) as the interface which could transmit the information bidirectionally i.e., transmitting the human commends to welding robots and visualizing the digital twin to human users. Six welders, skilled and unskilled, tested this system by completing the same welding job but demonstrate different patterns and resulted welding qualities. To differentiate their skill levels (skilled or unskilled) from their demonstrated operations, a data-driven approach, FFT-PCA-SVM as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed and demonstrates the 94.44% classification accuracy. The robots can also work as an assistant to help the human welders to complete the welding tasks by recognizing and executing the intended welding operations. This is done by a developed human intention recognition algorithm based on hidden Markov model (HMM) and the welding experiments show that developed robot-assisted welding can help to improve welding quality. To further take the advantages of the robots i.e., movement accuracy and stability, the role of the robot upgrades to be a collaborator from an assistant to complete a subtask independently i.e., torch weaving and automatic seam tracking in weaving GTAW. The other subtask i.e., welding torch moving along the weld seam is completed by the human users who can adjust the travel speed to control the heat input and ensure the good welding quality. By doing that, the advantages of humans (intelligence) and robots (accuracy and stability) are combined together under this human-robot collaboration framework. The developed digital twin for welding manufacturing helps to promote the next-generation intelligent welding and can be applied in other similar manufacturing processes easily after small modifications including painting, spraying and additive manufacturing

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0:40 - 1:56% for the BGP model and 4:49 - 7:52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications

Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product

With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process. However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result

Development of Bead Modelling for Distortion Analysis Induced by Wire Arc Additive Manufacturing using FEM and Experiment

In this research, Wire Arc Additive Manufacturing is modelled and simulated to determine the most suitable bead modelling strategy. This analysis is aimed to predict distortion by means of thermomechanical Finite Element Method (FEM). The product model with wire as feedstock on plate as substrate and process simulation are designed in form of multi-layered beads and single string using MSC Marc/Mentat. This research begins with finding suitable WAAM parameters which takes into account the bead quality. This is done by using robotic welding system with 01.2mm filler wire (AWS A5.28 : ER80SNi1), shielding gas (80% Ar/ 20% CO2) and 6mm-thick low carbon steel as base plate. Further, modelling as well as simulation are to be conducted with regards to bead spreading of each layers. Two different geometrical modelling regarding the weld bead are modelled which are arc and rectangular shape. Equivalent material properties from database and previous researches are implemented into simulation to ensure a realistic resemblance. It is shown that bead modelling with rectangular shape exhibits faster computational time with less error percentage on distortion result compared to arc shape. Moreover, by using the rectangular shape, the element and meshing are much easier to be designed rather than arc shape bead

On Sensor-Controlled Robotized One-off Manufacturing

A semi-automatic task oriented system structure has been developed and tested on an arc welding application. In normal industrial robot programming, the path is created and the process is based upon the decided path. Here a process-oriented method is proposed instead. It is natural to focus on the process, since the path is in reality a result of process needs. Another benefit of choosing process focus, is that it automatically leads us into task oriented thoughts, which in turn can be split in sub-tasks, one for each part of the process with similar process-characteristics. By carefully choosing and encapsulating the information needed to execute a sub-task, this component can be re-used whenever the actual subtask occurs. By using virtual sensors and generic interfaces to robots and sensors, applications built upon the system design do not change between simulation and actual shop floor runs. The system allows a mix of real- and simulated components during simulation and run-time

Kaynak dikiş formunun yapay sinir ağı ve vokselleme yöntemleriyle modellenmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu çalışmada, kaynakçı adaylarının eğitimi amacıyla geliştirilen düşük maliyetli sanal kaynak simülatörü için gerçek zamanlı ve üç boyutlu bir kaynak dikiş formu modellenmiştir. Adaylar bu simülatör vasıtasıyla kaynak tekniklerini herhangi bir iş kazasına neden olmadan güvenli bir ortamda öğrenebilir ve kısa sürede normalden daha fazla uygulama yaparak becerilerini geliştirebilirler. Geliştirilen simülatörde, Flock of Birds konum ve oryantasyon sensörü ile başa takılan ekran gibi özel sanal gerçeklik aygıtları kullanılmıştır. Simülasyon, torcun konumunu izleyen Flock of Birds sensör cihazından gelen verilere dayanarak, kaynak dolgu şeklini ve nufuziyet miktarını belirler. Kaynak dolgu şekli oluşturulurken, kaynak dikiş kesitinin parabol ile benzerliği nedeniyle bu şekil temel dolgu birimi olarak kullanılmıştır. Kaynak dikişimizi oluşturacak temel dolgu şeklinin yükseklik, genişlik ve nufuziyet parametrelerine ait değerler literatürdeki kaynak dikişi deneylerinden elde edilmiştir. Sanal kaynak işlemi esnasında, kaynak dolgu şekli parametre değerleri belirli zaman aralıklarında, ileri beslemeli geri yayılımlı yapay sinir ağı kullanılarak hesaplanır. Ağ kurgusu yapılırken eğitim fonksiyonu olarak TrainLM (Levenberg-Marquardt) referans alınmıştır. En uygun transfer fonksiyonu belirlenirken de en iyi sonucu LogSig() fonksiyonunun verdiği saptanmıştır. Ara katman sayısı ve her ara katmandaki proses elemanı (nöron) sayısının kaç olacağına deneme/yanılma yöntemiyle karar verilmiştir. Aynı zaman aralığında voksel haritası ve buna karşılık gelen hash tabanlı sekizli ağaç veri yapısı gerçek zamanlı olarak oluşturulur. Voksellenen veriler kullanılarak, kaynak dolgusunun üçgenlerden oluşan eş yüzeyleri, yürüyen küpler algoritması ile yeniden oluşturulur. Bu sayede daha gerçekçi bir kaynak dikiş görüntüsü elde edilir. Bu görüntü ve sanal sahne devamlı olarak başa takılan ekrana yollanarak sanal ortam içindeki gerçeklik hissi devam ettirilir. Vokselleme ve eş yüzey oluşturma işlemleri için yüksek çözünürlüklü sanal sahnelerde işlem süresini kısaltmak için de çok iş parçacıklı programlama tekniği kullanılmıştır. Farklı iş parçacığı sayıları için eş yüzey oluşturma süreleri de gösterilmiştir.In this study, a real time and three dimensional weld seam form was modeled for a low cost virtual welding simulator developed for training welder candidates. Through this simulator, candidates can learn welding techniques in a safe environment without causing any work accidents and improve their skills by performing more applications than usual in a short time. In the developed simulator, special virtual reality devices such as Flock of Birds position and orientation sensor and head mounted display are used. The simulation determines the weld bead shape and amount of penetration based on data from the Flock of Birds sensor device monitoring the position of the torch. When forming the weld bead shape, parabola was used as the basic bead shape unit due to the similarity of the weld bead slice with the parabola. The values of the height, width and penetration parameters of the basic weld bead shape that will form our weld seam were obtained from the weld seam experiments in the literature. During the virtual welding process, the weld bead shape parameter values are calculated at specified time intervals using the feed-forward back-propagation artificial neural network. TrainLM (Levenberg-Marquardt) was used as the training function for network design. While determining the most appropriate transfer function, it was found that LogSig () function gave the best result. The number of hidden layers and the number of process elements (neurons) in each hidden layer were determined by trial and error method. In the same time interval, the voxel map and the corresponding hash-based octree data structure are generated in real time. By using voxelized data, the triangular isosurfaces of the weld bead are reconstructed using the marching cubes algorithm. This results a more realistic weld seam appearance. This image and virtual scene are continuously sent to the head mounted display to maintain the sense of reality in the virtual environment. Multi-threaded programming technique is also used to shorten the processing time in high resolution virtual scenes for voxelization and isosurface extraction processes. The isosurface extraction times for different number of threads are also shown

Influence of parameters on mechanical and micro-structural properties of tungsten inert gas (TIG) welded joint of 1 mm thin Inconel 625 plates

Inconel 625 (IN625), since its invention, has been a material of choice for industries where components in service conditions are exposed to extremely high temperatures, corrosion, and oxidation environments. As per the American welding society for butt-joining such alloys in sheets, non-traditional techniques are the best means for producing permanent joints. These techniques require high-cost setup and high maintenance costs which are only economical for high-volume assembly line fabrication in mass-production facilities. Therefore, Tungsten Inert Gas (TIG) welding is highly suitable for producing defect-free autogenous weldments in high-strength alloy sheets with a thickness below 3mm. The current work focuses on the experimental investigation of the TIG welding of Inconel 1mm sheets. In this work, mechanical properties and microstructure are studied for controlled heat input of TIG welding. Current is varied from 40A to 65A and voltage from 10V to 12V. Only three combinations of parameters show sound weld visually and are analyzed by performing various testing. The main objective of the study is to find the feasible process parameters for the micro joining of IN625 sheets. From this discussion, it appears that arc energy, welding current, and voltage significantly affect the quality of the weld