scholarly journals Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

2021 ◽  
Vol 11 (24) ◽  
pp. 11949
Author(s):  
Natago Guilé Mbodj ◽  
Mohammad Abuabiah ◽  
Peter Plapper ◽  
Maxime El Kandaoui ◽  
Slah Yaacoubi
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Prediction Model ◽  
Travel Speed ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Evolutionary Trend ◽  
Deposition Parameters ◽  
Layer Deposition ◽  
Height Prediction

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

scholarly journals Additive Manufacturing of H11 with Wire-Based Laser Metal Deposition

2017 ◽  
Vol 22 (4) ◽  
pp. 466-479 ◽  
Author(s):  
Stella Holzbach Oliari ◽  
Ana Sofia Clímaco Monteiro D’Oliveira ◽  
Martin Schulz
Keyword(s):  
Additive Manufacturing ◽  
Travel Speed ◽  
Metal Deposition ◽  
Parameters Optimization ◽  
Layer By Layer ◽  
Laser Metal Deposition ◽  
3D Cad ◽  
Deposition Parameters ◽  
Commercial Applications ◽  
Functional Components

Abstract Laser additive manufacturing (LAM) is a near-net-shape production technique by which a part can be built up from 3D CAD model data, without material removal. Recently, these production processes gained attention due to the spreading of polymer-based processes in private and commercial applications. However, due to the insufficient development of metal producing processes regarding design, process information and qualification, resistance on producing functional components with this technology is still present. To overcome this restriction further studies have to be undertaken. The present research proposes a parametric study of additive manufacturing of hot work tool steel, H11. The selected LAM process is wire-based laser metal deposition (LMD-W). The study consists of parameters optimization for single beads (laser power, travel speed and wire feed rate) as well as lateral and vertical overlap for layer-by-layer technique involved in LMD process. Results show that selection of an ideal set of parameters affects substantially the surface quality, bead uniformity and bond between substrate and clad. Discussion includes the role of overlapping on the soundness of parts based on the height homogeneity of each layer, porosity and the presence of gaps. For the conditions tested it was shown that once the deposition parameters are selected, lateral and vertical overlapping determines the integrity and quality of parts processed by LAM.

Control of humping phenomenon and analyzing mechanical properties of Al–Si wire-arc additive manufacturing fabricated samples using cold metal transfer process

2021 ◽  
pp. 095440622199840
Author(s):  
Yashwant Koli ◽  
N Yuvaraj ◽  
Aravindan Sivanandam ◽  
Vipin
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Heat Input ◽  
Large Scale ◽  
High Deposition Rate ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Cold Metal Transfer ◽  
Geometrical Shapes ◽  
Wire Arc Additive Manufacturing

Nowadays, rapid prototyping is an emerging trend that is followed by industries and auto sector on a large scale which produces intricate geometrical shapes for industrial applications. The wire arc additive manufacturing (WAAM) technique produces large scale industrial products which having intricate geometrical shapes, which is fabricated by layer by layer metal deposition. In this paper, the CMT technique is used to fabricate single-walled WAAM samples. CMT has a high deposition rate, lower thermal heat input and high cladding efficiency characteristics. Humping is a common defect encountered in the WAAM method which not only deteriorates the bead geometry/weld aesthetics but also limits the positional capability in the process. Humping defect also plays a vital role in the reduction of hardness and tensile strength of the fabricated WAAM sample. The humping defect can be controlled by using low heat input parameters which ultimately improves the mechanical properties of WAAM samples. Two types of path planning directions namely uni-directional and bi-directional are adopted in this paper. Results show that the optimum WAAM sample can be achieved by adopting a bi-directional strategy and operating with lower heat input process parameters. This avoids both material wastage and humping defect of the fabricated samples.

Using Unsupervised Learning for Regulating Deposition Speed During Robotic Wire Arc Additive Manufacturing

2021 ◽  
Author(s):  
Ashish Kulkarni ◽  
Prahar M. Bhatt ◽  
Alec Kanyuck ◽  
Satyandra K. Gupta
Keyword(s):  
Additive Manufacturing ◽  
Unsupervised Learning ◽  
Molten Metal ◽  
Length Variation ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Arc Length ◽  
Regulation Scheme ◽  
Speed Regulation ◽  
Wire Arc Additive Manufacturing

Abstract Robotic Wire Arc Additive Manufacturing (WAAM) is the layer-by-layer deposition of molten metal to build a three-dimensional part. In this process, the fed metal wire is melted using an electric arc as a heat source. The process is sensitive to the arc conditions, such as arc length. While building WAAM parts, the metal beads overlap at corners causing material accumulation. Material accumulation is undesirable as it leads to uneven build height and process failures caused by arc length variation. This paper introduces a deposition speed regulation scheme to avoid the corner accumulation problem and build parts with uniform build height. The regulated speed has a complex relationship with the corner angle, bead geometry, and molten metal dynamics. So we need to train a model that can predict suitable speed regulations for corner angles encountered while building the part. We develop an unsupervised learning technique to characterize the uniformity of the bead profile of a WAAM built layer and check for anomalous bead profiles. We train a model using these results that can predict suitable speed regulation parameters for different corner angles. We test this model by building a WAAM part using our speed regulation scheme and validate if the built part has uniform build height and reduced corner defects.

The Earlier the Better? Investigating the Importance of Timing on Effectiveness of Design for Additive Manufacturing Education

2018 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Educational Interventions ◽  
Layer By Layer ◽  
Design For Additive Manufacturing ◽  
Layer Deposition ◽  
Subtractive Manufacturing ◽  
The Impact ◽  
Design Ideas ◽  
Am Processes

Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.

Additive Manufacturing of Kevlar Reinforced Epoxy Composites

2019 ◽  
Author(s):  
Nashat Nawafleh ◽  
Jordan Chabot ◽  
Mutabe Aljaghtham ◽  
Cagri Oztan ◽  
Edward Dauer ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Mechanical Performance ◽  
Failure Strain ◽  
Flexural Modulus ◽  
Layer By Layer ◽  
Structural Components ◽  
Thermoset Composites ◽  
3D Objects ◽  
Layer Deposition ◽  
Subtractive Manufacturing

Abstract Additive manufacturing is defined as layer-by-layer deposition of materials on a surface to fabricate 3D objects with reduction in waste, unlike subtractive manufacturing processes. Short, flexible Kevlar fibers have been used in numerous studies to alter mechanical performance of structural components but never investigated within printed thermoset composites. This study investigates the effects of adding short Kevlar fibers on mechanical performance of epoxy thermoset composites and demonstrates that the addition of Kevlar by 5% in weight significantly improves flexure strength, flexural modulus, and failure strain by approximately 49%, 19%, and 38%, respectively. Hierarchical microstructures were imaged using scanning electron microscopy to observe the artefacts such as porosity, infill and material interdiffusion, which are inherent drawbacks of the 3D printing process.

scholarly journals Design and Additive Manufacturing of Porous Sound Absorbers—A Machine-Learning Approach

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1747
Author(s):  
Sebastian Kuschmitz ◽  
Tobias P. Ring ◽  
Hagen Watschke ◽  
Sabine C. Langer ◽  
Thomas Vietor
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Lattice Structure ◽  
Material Behavior ◽  
Machine Learning Techniques ◽  
Layer By Layer ◽  
Learning Models ◽  
Micro Scale ◽  
Special Cases ◽  
Machine Learning Models

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

scholarly journals Deep forest model for diagnosing COVID-19 from routine blood tests

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maryam AlJame ◽  
Ayyub Imtiaz ◽  
Imtiaz Ahmad ◽  
Ameer Mohammed
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Performance Metrics ◽  
Laboratory Data ◽  
Machine Learning Techniques ◽  
Layer By Layer ◽  
Infected People ◽  
Global Pandemic ◽  
Learning Techniques ◽  
Deep Forest

AbstractThe Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

scholarly journals Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling

Metals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 683 ◽  
Author(s):  
Sudeepta Mondal ◽  
Daniel Gwynn ◽  
Asok Ray ◽  
Amrita Basak
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
A Priori Estimates ◽  
Process Parameter ◽  
Bayesian Optimization ◽  
Layer By Layer ◽  
Complex Objects ◽  
Melt Pool ◽  
Gp Model

Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

Additive Manufacturing Feature Taxonomy and Placement of Parts in AM Enclosure

2022 ◽  
pp. 138-176
Author(s):  
Prafull Agarwal ◽  
Rishi Kurian ◽  
Ravi Kumar Gupta
Keyword(s):  
Additive Manufacturing ◽  
Knowledge Base ◽  
Manufacturing Process ◽  
Critical Role ◽  
Optimal Placement ◽  
Layer By Layer ◽  
Design Flexibility ◽  
Layer Deposition ◽  
Key Factor ◽  
Manufacturing Feature

Additive Manufacturing (AM) is a layer-by-layer deposition of material for the production of the desired product. The design flexibility associated with AM is much more when compared to the conventional manufacturing process. To manufacture a part with AM, two things play a critical role: the designing of the part and the other is the placement of the part in the build volume. As already mentioned, design flexibility associated with AM is much more when compared to the conventional manufacturing process. However, to correctly implement the design flexibility, we need a knowledge base at our disposal so that appropriate features can be used for the part production. The AM feature taxonomy forms the backbone of the knowledge base. The taxonomy comprises AM features classified based on different categories, which helps us understand every feature's importance. Talking about the part placement, we know that optimal placement is the key factor that makes the AM process economically feasible.

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