Selection of Process Parameters for Near-Net Shape Deposition in Wire Arc Additive Manufacturing by Genetic Algorithm

Kumar, Aashish; Maji, Kuntal

doi:10.1007/s11665-020-04847-1

Cited by 57 publications

(18 citation statements)

References 21 publications

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“…Two experienced users were utilized to program the robotic path using both OP and OLP robotic approaches, where the workpiece was kept in the same fixed position at all times relative to the robotic manipulator. The comparison metrics that were used were the path planning time, the number of teaching points required, the need to teach a base for motion planning, and the ability to adapt to the material overbuild of the WAAM manufacturing sample [ 38 ]. Table 3 demonstrates these results, where the proposed kinesthetic guidance approach proved robust and, overall, superior across all the comparison metrics.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries

Loukas

Vasilev

Zimmerman

et al. 2023

Sensors

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The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force–torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near-net-shaped Wire–Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production.

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Section: Quantitative Comparisonmentioning

confidence: 99%

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries

Loukas

Vasilev

Zimmerman

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly, Imani et al (2018) demonstrated the ability of an ANN to predict laser powder bed fusion process parameter regimes using layer wise optical images. Kumar and Maji (2020) in their recent work employed a Genetic Algorithm-based (GA) framework for extracting the optimal process parameters of wire-arc additive manufacturing process. Similarly, Vaissier et al (2019) demonstrated the efficacy of using GA for optimizing support structure generation in additively manufactured parts.…”

Section: Prior Work Challenges and Noveltymentioning

confidence: 99%

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

et al. 2022

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In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

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“…In recent years, WAAM has garnered considerable research attention, yet it has seen minimal industrial applications. Numerous issues with geometric accuracy, poor surface consistency, layer unevenness, poor repeatability, and the need for post-processing to complete a part [7,8]. Over the last decade, extensive research has been conducted on process details such as forming precision, surface quality, microstructure, mechanical properties, and residual stress to improve quality and meet industrial needs [9].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, optimizing the process parameters is crucial to achieving superior component quality and mechanical properties. Kumar and Maji [8] discussed optimizing the selection of parameters for near net shape deposition to minimize void and excess material in WAAM by the Genetic Algorithm. Bead geometry parameters, width, height, and cross-section were expressed in process parameters like voltage, wire feed rate, torch speed, and gas flow rate.…”

Section: Introductionmentioning

confidence: 99%

Parametric Optimisation of Micro Plasma Welding for Wire Arc Additive Manufacturing by Response Surface Methodology

Rosli¹,

Alkahari²,

Ramli³

et al. 2022

Manufacturing Technology

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High deposition rate with minimal heat input is one of the primary emphasis in wire arc additive manufacturing. This study aims to determine the optimal input parameters of micro plasma welding for single-layer deposition. The stability of a single layer is crucial as it serves as the foundation to the multi-layers deposition in producing 3D additively manufactured structure. The study focuses on wire feeding speed, welding speed, and pulse and their interaction between the input and response variables. Based on the study, the regression equation between the three key parameters and the response using the Box-Behnken Design response surface methodology was proposed. The outcome demonstrates that the optimized sample deposition produces a smooth surface appearance with no apparent defects. additive manufacturing 3D printing wire arc additive manufacturing micro plasma arc welding response surface methodology

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Selection of Process Parameters for Near-Net Shape Deposition in Wire Arc Additive Manufacturing by Genetic Algorithm

Cited by 57 publications

References 21 publications

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries

Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Parametric Optimisation of Micro Plasma Welding for Wire Arc Additive Manufacturing by Response Surface Methodology

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