Development of an adaptive, self-learning control concept for an additive manufacturing process

Renken, Volker; Albinger, Stephan; Goch, G.; Neef, Arne; Emmelmann, Claus

doi:10.1016/j.cirpj.2017.05.002

Cited by 40 publications

(11 citation statements)

References 5 publications

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“…Local defects may occur during layer by layer construction of an AM part. The root causes could be traced to improper process parameters, insufficient supports, a non-homogeneous powder deposition, improper heat exchanges and/or material contaminations [4][13] [14]. The effects of process parameters, namely; laser power, scanning speed, hatch spacing, layer thickness and powder temperature, on the tensile strength of AM products are reported in [15] [16].…”

Section: Key Process Parametersmentioning

confidence: 99%

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Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Okaro¹,

Jayasinghe²,

Sutcliffe³

et al. 2018

Preprint

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Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

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Section: Key Process Parametersmentioning

confidence: 99%

“…Understanding these data is a challenging area. However, advances in machine learning have made it possible to create and apply intelligent algorithms to large datasets for decision making [4]. Such algorithms can identify patterns in large data, after being trained.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Okaro¹,

Jayasinghe²,

Sutcliffe³

et al. 2018

Preprint

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“…Ranken et al [25], used sensor-based adaptive self-learning control for melt pool modulation for the SLM process. The same authors used model-based feedforward control using a finite element heat simulation in combination with feedback by the thermal sensor for temperature control of SLM [26].…”

Section: Introductionmentioning

confidence: 99%

Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

et al. 2021

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Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.

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“…An effective way of controlling heat accumulation is by adjusting laser power to influence the process temperature [6]. Data-driven concepts using multiple in-situ sensors for closed-loop control are presented in [7], but require large training data sets. Accurate analytical and computational…”

Section: Introductionmentioning

confidence: 99%

In-layer Thermal Control of a Multi-layer Selective Laser Melting Process

Liao-McPherson¹,

Balta²,

Wüest³

et al. 2021

Preprint

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Selective Laser Melting (SLM) is an additive manufacturing technology that builds three dimensional parts by melting layers of metal powder together with a laser that traces out a desired geometry. SLM is popular in industry, however the inherent melting and re-solidification of the metal during the process can, if left uncontrolled, cause excessive residual stress, porosity, and other defects in the final printed parts. This paper presents a control-oriented thermal model of a multilayer SLM process and proposes a structured model reduction methodology with an associated reduced order model based in-layer controller to track temperature references. Simulation studies demonstrate that the controller is able to prevent layerto-layer heat buildup and that good closed-loop performance is possible using relatively low-order models.

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Development of an adaptive, self-learning control concept for an additive manufacturing process

Cited by 40 publications

References 5 publications

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

In-layer Thermal Control of a Multi-layer Selective Laser Melting Process

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