Height control of laser metal-wire deposition based on iterative learning control and 3D scanning

Heralic, Almir; Christiansson, Anna-Karin; Lennartson, Bengt

doi:10.1016/j.optlaseng.2012.03.016

Cited by 206 publications

(62 citation statements)

References 35 publications

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“…Although wire-based DLD machines [17,18,91,92] can be beneficial for obtaining increased process efficiency and surface quality, their prominence-in-the-field is significantly less than that of powder-fed DLD. This can be attributed to the fact that blown powder dynamics are easier to control in real-time (less of a response lag) and can be 'tuned' for more precise fabrication of complex geometries.…”

Section: Direct Laser Depositionmentioning

confidence: 99%

See 1 more Smart Citation

An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

Thompson

Bian

Shamsaei

et al. 2015

Additive Manufacturing

678

379

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Section: Direct Laser Depositionmentioning

confidence: 99%

“…Furthermore, non-used powders can be recycled for later M a n u s c r i p t use. Wire-fed DLD is susceptible to vibration and disturbances and require a moderate-to-high degree of control [92] for material deposition which is in contrast to that of powder-fed DLD.…”

Section: Direct Laser Depositionmentioning

confidence: 99%

An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

Thompson

Bian

Shamsaei

et al. 2015

Additive Manufacturing

678

379

View full text Add to dashboard Cite

“…(6), statistical predictive model (13), optimal compensation model (16) are generally robust and predictive. • The training data for establishing polygon model (13) and opti mal compensation model (16) only include square and pentagon shapes. The validation experiment conducted for dodecagon is therefore not within the experimental range of polygon sides.…”

Section: Optimal Compensation and Experimental Validationmentioning

confidence: 99%

“…In the second category, Heralic et al [13], to obtain a flat depo sition surface in a laser metal-wire deposition process, controlled the offset of the robot in the vertical direction based on the 3D scanned data. The deviations in the layer height were compen sated by controlling the wire feed rate on next deposition layer through iterative learning control.…”

Section: Introductionmentioning

confidence: 99%

Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products

Huang

Nouri

et al. 2014

Journal of Manufacturing Science and Engineering

109

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Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Ax, (2) control process variables x based on the observed product deviation Ay, and (3) control input product geometry y based on the observed product deviation Ay. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class o f product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Ex perimental investigation and validation of polyhedrons a indicates the promise of predict ing and compensating a wide class of products built through 3D printing technology.

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“…Applications of ILC include several manufacturing processes [9] and robotic applications [10,11]. Yet, only recently has there been some effort to use ILC to control additive manufacturing processes [12]. Therefore, this paper aims to use the model developed in [8], which incorporates the dependency of the melt pool morphology on part height, to design an iterative process controller, by using existing optimal control methods, to track an iteration varying reference.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control of bead morphology in Laser Metal Deposition processes

Sammons

Bristow

Landers

2013

2013 American Control Conference

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Laser Metal Deposition (LMD) is a layer-based manufacturing process in which a laser and powdered metal are used to create a molten bead that is then traced along a path to create functional parts. The properties of the structure, including shape and material microstructure, are the result of complex interactions between the laser, the powder, the part substrate and other factors. Thus, a control algorithm is needed to accurately produce the designed part. However, feedback control of the process can create phase lag in the resulting control structure, which in turn can create dimensional instability. Additionally, the LMD process has been shown to change with part height or layer number. Taking these issues into account, a feed-forward, adaptive-type controller that changes with each fabricated layer, should be used. This paper first presents a dynamic model for the LMD process that incorporates the dependency of the process on part height. Then, an optimal Iterative Learning Process Control algorithm is presented to regulate the melt pool morphology of a deposited part using layer number as the iteration axis. A simulation study on the LMD process using the designed process controller shows that it is able to achieve good tracking performance.

show abstract

Height control of laser metal-wire deposition based on iterative learning control and 3D scanning

Cited by 206 publications

References 35 publications

An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics

Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products

Iterative learning control of bead morphology in Laser Metal Deposition processes

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