Identification and predictive control of laser beam welding using neural networks

Bollig, Alexander; Abel, Dirk; Kratzsch, Ch.; Kaierle, Stefan

doi:10.23919/ecc.2003.7085334

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…Any variation in process parameters often necessitates a recalibration, which might even require the assembly line to be halted. Research has offered promising approaches for controlling laser welding processes [138] and for teaching machines how to handle them [139]- [141]. In a Cognitive Factory, many different sensors are combined and real-time machine-learning techniques to improve process quality and control are developed.…”

Section: ) Motivationmentioning

confidence: 99%

Artificial Cognition in Production Systems

Bannat¹,

Bautze²,

Beetz³

et al. 2011

IEEE Trans. Automat. Sci. Eng.

103

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Abstract-Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally-and situationally dependent triggered paths on state-based graphs are described in this paper.Note to Practitioners-Today's manufacturing enterprises have to face a number of challenges in a turbulent environment that originates amongst other from saturated markets, unprecedented and abrupt changes in market demands, an ever increasing number of product variants and smaller lot sizes. A recent research trend in Germany is the so called Cognitive Factory, where artificial cognitive capabilities are introduced to the control of production systems. The applications range from production planning and control, human-robot-cooperation, automatic robot programming to intuitive worker guidance systems.The concept of fully automated production systems is no longer a viable vision, as it has been shown, that the conventional automa- tion is not able to deal with the ever-rising complexity of modern production systems. Especially, a high reactivity, agility and adaptivity that is required by modern production systems, can only be reached by human operators with their immense cognitive capabilities, which enable them to react to unpredictable situations, to plan their further actions, to learn and to gain experience and to communicate with others. Thus, new concepts are required, that apply these cognitive principles to the planning processes and control systems of pr...

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Section: ) Motivationmentioning

confidence: 99%

Artificial Cognition in Production Systems

Bannat¹,

Bautze²,

Beetz³

et al. 2011

IEEE Trans. Automat. Sci. Eng.

103

View full text Add to dashboard Cite

show abstract

“…Considering the complexity of the welding process, this finding is not surprising. Nonlinear dependencies were also found for the relation between penetration depth and melt pool intensity [18] and between penetration depth and melt pool width [19]. …”

Section: (T) ≡ H (P R (T)) and P F (T) ≡ H (P F (T)) To Be Constant mentioning

confidence: 91%

“…3, a feedback strategy is required which is robust against the transient response of the process. In contrast to publications [18,19] which apply a time independent nonlinear model to images acquired at rather low frame rates, we exploit the high frame rate and short latency achieved by the CNN camera. Of course, the probability p FPH (t) has to be estimated within a single sample function which means that the relative frequency must be measured at different times.…”

Section: Closed-loop Controlmentioning

confidence: 99%

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Blug

Abt²,

Nicolosi³

et al. 2012

Appl. Phys. B

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Although laser-welding processes are frequently used in industrial production the quality control of these processes is not satisfactory yet. Until recently, the "full penetration hole" was presumed as an image feature which appears when the keyhole opens at the bottom of the work piece. Therefore it was used as an indicator for full penetration only. We used a novel camera based on "cellular neural networks" which enables measurements at frame rates up to 14 kHz. The results show that the occurrence of the full penetration hole can be described as a stochastic process. The probability to observe it increases near the full penetration state. In overlap joints, a very similar image feature appears when the penetration depth reaches the gap between the sheets. This stochastic process is exploited by a closedloop system which controls penetration depth near the bottom of the work piece ("full penetration") or near the gap in overlap joints ("partial penetration"). It guides the welding process at the minimum laser power necessary for the required penetration depth. As a result, defects like spatters are reduced considerably and the penetration depth becomes independent of process drifts such as feeding rate or pollution on protection glasses.

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“…The main advantages of laser welding are narrow seam with high weld depth and minimal residual mechanical stress of thermal origin in the material [10]. Due to these advantages, laser welding in the industry is used very widely and only one key question, weldability of materials, has to be satisfied.…”

Section: Laser Weld Parametersmentioning

confidence: 99%

Estimation of laser weld parameters using surrogate modelling technique

Pavlíček

Kotlan

Doležel

2018

Journal of Electrical Engineering

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A surrogate technique based on Gaussian Process (GP) is used for predicting quality of laser in case of laser welding process that may be supported by induction preheating. FEM-based solution of the problem is computationally expensive because it combines computation of 3D coupled nonlinear electromagnetic and temperature fields. The quality of laser welds is quantified with weld depth, which depends on a number of input parameters. The paper deals with two of them —thickness of the welded steel sheet and power of the laser beam. First, selected FEM simulations allow finding data describing the dependency between the two input parameters and weld depth. These data allow creating a surrogate model that is able to predict weld depth at any point close to the points where the results are known. The principal goal is to essentially save the computational time. The surrogate model also allows estimating prediction plausibility and running the full FEM calculation in cases where the prediction is not sufficiently accurate. The methodology is illustrated with a typical example whose results are discussed.

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Identification and predictive control of laser beam welding using neural networks

Cited by 13 publications

References 14 publications

Artificial Cognition in Production Systems

Artificial Cognition in Production Systems

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Estimation of laser weld parameters using surrogate modelling technique

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