Search results

Modeling and control of bead geometry in wire and arc additive manufacturing is significant as it affects the whole manufacturing process. The purpose of this paper is to establish an efficient model to control the bead geometry with fewer experiments in wire and arc additive manufacturing (WAAM).

A multi-sensor system is established to monitor the process parameters and measure the bead geometry information. A dynamic parameters experimental method is proposed for rapid modeling without dozens of experiments. A deep learning method is used for bead modeling and control. To adaptively control the bead geometry in real-time, a closed-loop control system was developed based on the bead model and in situ monitoring.

A series of experiments were conducted to train, test and verify the feasibility of the method and system, and the results showed that the proposed method can build the bead model rapidly with high precision, and the closed-loop system can control the forming geometry adaptively.

The proposed modeling method is novel as the experiment number is reduced. The dynamic parameters experimental method is effective with high precision. The closed-loop control system can control the bead geometry in real-time. The forming accuracy is elevated.

This paper aims to develop experimentally validated numerical models to accurately characterize the cross-sectional geometry of the deposited beads in a fused filament fabrication (FFF) process under various process conditions.

The presented numerical model is investigated under various fidelity with varying computational complexity. To this end, comparisons between the Newtonian, non-newtonian, isothermal and non-isothermal computational models are presented for the extrusion of polylactic acid material in an FFF process. The computational model is validated through an experimental study on an off-the-shelf FFF printer. Microscope images of experimentally printed FFF bead cross-sections corresponding to various printing conditions are digitally processed for the validation. In the experimental study, common practical printing conditions for an FFF process are tested, and the results are compared to the numerical model.

Microscope image analyses of the cross-sectional geometries of deposited beads show that the numerical model provides a precise characterization of the cross-sectional geometry under varying process parameters in terms of the cross-section outline, bead height and width. The results show that the nozzle-to-table distance has a great effect on the bead shape when compared to the extrusion rate at a given nozzle-to-table distance. Comparison of the various computational models show that the non-Newtonian isothermal model provides the best tradeoff between computational complexity and model accuracy.

The authors provide detailed computational models, including the extruder nozzle geometry for cases ranging from Newtonian isothermal models to non-Newtonian non-isothermal models with experimental validation. The validation study is conducted for practical process parameters that are commonly used in FFF in practice and show that the computational models provide an accurate depiction of the true process outputs. As the developed models can accurately predict process outputs, they can be used in further applications for process planning and parameter tuning.

This paper aims to develop a numerical model of bead spreading architecture of a viscous polymer in fused filament fabrication (FFF) process with different nozzle geometry. This paper also focuses on the manufacturing feasibility of the nozzles and 3D printing of the molten beads using the developed nozzles.

The flow of a highly viscous polymer from a nozzle, the melt expansion in free space and the deposition of the melt on a moving platform are captured using the FLUENT volume of fluid (VOF) method based computational fluid dynamics code. The free surface motion of the material is captured in VOF, which is governed by the hydrodynamics of the two-phase flow. The phases involved in the numerical model are liquid polymer and air. A laminar, non-Newtonian and non-isothermal flow is assumed. Under such assumptions, the spreading characteristic of the polymer is simulated with different nozzle-exit geometries. The governing equations are solved on a regular stationary grid following a transient algorithm, where the boundary between the polymer and the air is tracked by piecewise linear interface construction (PLIC) to reconstruct the free surface. The prototype nozzles were also manufactured, and the deposition of the molten beads on a flatbed was performed using a commercial 3D printer. The deposited bead cross-sections were examined through optical microscopic examination, and the cross-sectional profiles were compared with those obtained in the numerical simulations.

The numerical model successfully predicted the spreading characteristics and the cross-sectional shape of the extruded bead. The cross-sectional shape of the bead varied from elliptical (with circular nozzle) to trapezoidal (with square and star nozzles) where the top and bottom surfaces are significantly flattened (which is desirable to reduce the void spaces in the cross-section). The numerical model yielded a good approximation of the bead cross-section, capturing most of the geometric features of the bead with a reasonable qualitative agreement compared to the experiment. The quantitative comparison of the cross-sectional profiles against experimental observation also indicated a favorable agreement. The significant improvement observed in the bead cross-section with the square and star nozzles is the flattening of the surfaces.

The developed numerical algorithm attempts to address the fundamental challenge of voids and bonding in the FFF process. It presents a new approach to increase the inter-bead bonding and reduce the inter-bead voids in 3D printing of polymers by modifying the bead cross-sectional shape through the modification of nozzle exit-geometry. The change in bead cross-sectional shape from elliptical (circular) to trapezoidal (square and star) cross-section is supposed to increase the contact surface area and inter-bead bonding while in contact with adjacent beads.

The modeling and optimization of a weld bead in the middle of the weld are often simple, as the forming process is dynamically balanced. However, the arc striking (AS) and arc extinguishing (AE) areas of weld beads are generally abnormal because the dynamic processes at these areas are unstable. The purpose of this paper is to investigate the abnormal areas of the weld bead with optimization modeling methods in wire and arc additive manufacturing (WAAM).

A burning-back method was proposed to fill the slanted plane in the AE area. To optimize the welding parameters and obtain the optimal design, a response surface methodology was proposed to build the relationships between the input parameters and response variables.

The proposed burning-back method could fill the slanted plane in the AE area. Second-order models of abnormal areas were developed and the optimization effects were analyzed. The experimental results indicated that the relationship models at both ends were applicable and preferable for the optimization of weld beads.

In this paper, a burning-back method was proposed to optimize the slanted plane in the AE area. Second-order models of abnormal areas were established. The methods and models were preferable in the optimization of the abnormal areas in WAAM.

The purpose of this study is to optimize the process parameters (wire feed rate (F), voltage (V), welding speed (S) and torch angle (A)) in order to obtain the optimum bead geometry (bead width (W), reinforcement (R) and depth of penetration (P)), considering the ranges of the process parameters using evolutionary algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm.

The modeling of welding parameters in flux cored arc welding process using a set of experimental data and regression analysis, and optimization using GA and SA algorithm.

The adequate mathematical model was developed. The multiple objectives were optimized satisfactorily by the GA and SA algorithms. The feasible solution results are very closer to the optimized results and the percentage error was found to be negligibly small.

The optimal welding parameters were identified in order to increase the productivity. The welding input parameters effect was found.

Material extrusion additive manufacturing processes inevitably produce bead-shaped surface patterns on the walls of parts, which create stress concentrations under load. This study aims to investigate the influence of such stress concentrations on the strength along the build direction (“Z-strength”).

This work consists of two main parts – an experimental demonstration to show the significance of stress concentrations on the Z-strength, followed by numerical modeling to evaluate the theoretical stress concentration factors (k_t) for such shapes. Meso-scale finite element analysis (FEA) was performed to evaluate k_t at the roots of the intersecting bead shapes. The critical bead shape parameters influencing k_t were identified, and parametric FEA studies were performed on different bead shapes by varying the normalized parameters.

The experimental results showed that up to a 40% reduction in the effective Z-strength could be attributed only to the presence of surface bead shapes. Bead overhang and root radius were identified as critical shape parameters influencing k_t. The results of the parametric FEA studies were used to generate a single empirical equation to determine k_t for any bead shape.

Predictive models for Z-strength often focus on crystallization kinetics and polymer chain interdiffusion to predict interlayer adhesion strength. The authors propose that the results of such studies must be combined with surface bead-shape induced stress concentration factors to obtain the combined, “effective” Z-strength.

The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics.

By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell.

Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown.

The approach considers only stringer beads. Weave bead and bead penetration are not considered.

With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process.

The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.

The purpose of this paper is to optimize the gas metal arc welding (GMAW) process input parameters simultaneously considering the multiple output variables (bead width (BW), bead height (BH) and depth of penetration (DP)).

Grey‐based Taguchi approach was used for designing the experiment, L27 orthogonal array was used which composed of three levels and 27 rows, which means that 27 experiments were carried out. Design of experiments was selected based on a four welding parameters with three levels each. The selected welding parameters for this paper are gas flow rate, voltage, travel speed and wire feed rate. The bead‐on‐plate welding trials are carried out on AISI 904L super austenitic stainless steel (SASS) sheets and evaluate the shape of the fusion zone depends upon a number of input parameters.

Bead‐on‐plate welding of 904L SASS sheet is successfully performed (without any cracks and discontinuity) by GMAW process and the bead profiles are measured. The predicted bead profiles have the better DP and lower BH and BW. It is found that the optimized setting values are improving the response values by 10 per cent.

The optimal welding conditions are identified in order to increase the productivity and minimize the total operating cost. The process input parameters effect is determined under the optimal welding combinations.

The continual growth of additive manufacturing has increased tremendously because of its versatility, flexibility and high customization of geometric structures. However, design hurdles are presented in understanding the relationship between the fabrication process and materials microstructure as it relates to the mechanical performance. The purpose of this paper is to investigate the role of build architecture and microstructure and the effects of load direction on the static response and mechanical properties of acrylonitrile butadiene styrene (ABS) specimens obtained via the fused deposition modeling (FDM) processing technique.

Among additive manufacturing processes, FDM is a prolific technology for manufacturing ABS. The blend of ABS combines strength, rigidity and toughness, all of which are desirable for the production of structural materials in rapid manufacturing applications. However, reported literature has varied widely on the mechanical performance due to the proprietary nature of the ABS material ratio, ultimately creating a design hurdle. While prior experimental studies have studied the mechanical response via uniaxial tension testing, this study has aimed to understand the mechanical response of ABS from the materials’ microstructural point of view. First, ABS specimen was fabricated via FDM using a defined build architecture. Next, the specimens were mechanically tested until failure. Then finally, the failure structures were microstructurally investigated. In this paper, the effects of microstructural evolution on the static mechanical response of various build architecture of ABS aimed at FDM manufacturing technique was analyzed.

The results show that the rastering orientation of 0/90 exhibited the highest tensile strength followed by fracture at its maximum load. However, the “45” bead direction of the ABS fibers displayed a cold-drawing behavior before rupture. The morphology analyses before and after tensile failure were characterized by a scanning electron microscopy (SEM) which highlighted the effects of bead geometry (layers) and areas of stress concentration such as interstitial voids in the material during build, ultimately compromising the structural integrity of the specimens.

The ability to control the constituents and microstructure of a material during fabrication is significant to improving and predicting the mechanical performance of structural additive manufacturing components. In this report, the effects of microstructure on the mechanical performance of FDM-fabricated ABS materials was discussed. Further investigations are planned in understanding the effects of ambient environmental conditions (such as moisture) on the ABS material pre- and post-fabrication.

The study provides valuable experimental data for the purpose of understanding the inter-dependency between build parameters and microstructure as it relates to the specimens exemplified strength. The results highlighted in this study are fundamental to the development of optimal design of strength and complex ultra-lightweight structure efficiency.

The present research work aims to study the effect of average beam power (laser process parameters) on the overlapping factor, depth of penetration (DOP), weld bead width, fusion zone and heat affected zone (HAZ) in laser welding of 304L and st37 steel. Back side and top surface morphology of the welded joints have also been studied for varying average beam power.

Laser welding of austenitic stainless steel (304L) and carbon steel (st37) was carried out using Nd:YAG laser integrated with ABB IRB 1410 robot in pulse mode. The selection of laser process parameters was based on the specification of available laser welding machine. Dissimilar laser welding of 304L and st37 carbon steel for full depth of penetration have been performed, with varying average beam power (225-510W) and constant welding speed (5mm/s) and pulse width (5ms).

Recrystallized coarse grains were observed adjacent to the fusion zone and nucleated grains were seen away from the fusion zone towards carbon steel. Overlapping factor and HAZ width st37 side increases with increase in average beam power whereas top weld bead width increases first, attains maximum value and then subsequently decreases. Bottom weld bead width increases with increase in average beam power. The mechanical properties namely microhardness and tensile strength of the welded joints have been investigated with varying average beam power.

In the recent development of the automobile, power generation and petrochemical industries the application of dissimilar laser welding of austenitic stainless steel (304L) and carbon steel (st37) are gaining importance. Very limited work have been reported in pulsed Nd:YAG dissimilar laser welding of austenitic stainless steel (304L) and carbon steel (st37) for investigating the effect of laser process parameters on weld bead geometry, microstructural characterization and mechanical properties of the welded joint.