Towards a universal laser machine for batch-size 1 production
Numerous manufacturing paradigms have evolved in recent years, from mass personalisation1 to Industry 4.02, or more recently software-defined manufacturing (SDM)3.
A trend can be observed in almost all of these paradigms that leads towards increasing product variety and correspondingly smaller batch sizes, while simultaneously aiming for lower prices and shorter time-to-market.
This trend, often referred to as ‘batch-size 1’, is still ongoing and poses a challenge for manufacturers as it leads to constantly changing requirements for both the products and their production, which manufacturers have to cope with.
This requires manufacturing environments that are highly versatile, fully and easily adaptable, and efficient at the same time.
Most of the existing paradigms focus on the manufacturing system as a whole to address this challenge, which is of course an important aspect. However, the machine tool itself must also be versatile and adaptable. Such a ‘universal manufacturing node’4 is considered as one of the key enablers for SDM.
The laser is a very promising tool for such a universal manufacturing node, since it is already a versatile tool. In principle, it is possible to cover processes from all six main manufacturing groups of the German standard DIN 8580 using the laser as a tool and only changing a few processing parameters5. This opens up the opportunity to develop a universal manufacturing node that utilises the combination of laser-based processes on one machine for highly versatile and adaptable manufacturing. However there is still considerable need for research in the field of corresponding system technology.
Key technologies for a universal laser machine
The junior research group ‘Advanced Manufacturing’ at the Institut für Strahlwerkzeuge (IFSW) at the University of Stuttgart is working on the research and development of the key technologies for such a universal laser machine. This interdisciplinary group has been established within the Innovation Campus Future Mobility (ICM), and the work is jointly conducted between the IFSW, the Institute for Machine Tools (IfW), and the Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), also at the University of Stuttgart, as well as the Institute of Production Science (wbk) of the Karlsruhe Institute of Technology (KIT).
Figure 1: The conceptualised Universal Laser Machine, capable of highly versatile and adaptable manufacturing(Credit: IFSW)
We mainly consider remote (or remote-capable) laser processes, since they are best suited to exploit the versatility of the laser, as their use minimises the need for additional hardware, e.g. auxiliary equipment for the supply of process gases or filler material. Moreover, we focus on a machine for the processing of metals as a first step towards a fully universal laser machine.
We have identified five key technologies required in the development towards a universal manufacturing node:
Laser-based process chains: knowledge about the technological interactions in laser-based process chains is required to derive optimised production sequences.
Control architecture: an ‘on-the-fly’ reconfiguration of real-time applications and real-time networks is required to ensure a seamless switching between the different laser-based manufacturing processes.
Kinematics: highly dynamic and precise kinematics are required to realise the different laser-based manufacturing processes on one machine and to utilise their full potential.
Parameter prediction models: models for the prediction of process parameters for the different laser-based manufacturing processes are required, minimising time, material consumption, and experimental effort.
Adaptable process diagnostics: reliable online quality monitoring for different laser-based manufacturing processes and changing boundary conditions requires adaptable process diagnostics.
Current challenges and research needs
There are currently still some challenges to overcome in the development of these key technologies for a universal laser machine. The following is a brief overview on the challenges and corresponding research needs that we will address within our research group.
A universal laser manufacturing node enables the realisation of a large variety of versatile and adaptable process chains since a large variety of different laser processes is accessible on one machine. The technological interactions between the involved processes must be taken into account in order to derive an optimised process chain. This is already known from process chains using mainly conventional manufacturing processes like casting, milling, or turning6. For laser-based process chains, however, there is little known about the technological interactions between the processes so far, and the existing knowledge is mainly limited to the combination of two laser processes.
Therefore, the technological interactions between laser processes have to be investigated to expand this knowledge, especially for process chains comprising more than two laser processes. Furthermore, this knowledge must be made accessible in a digital form to support the optimisation of laser-based process chains.
Such versatile and adaptable process chains also require a control architecture that supports easy and seamless switching between the different laser processes on-the-fly. This includes exchanging the real-time control applications and reconfiguring the corresponding real-time network, since every process needs a different set of control applications to run. Preferably, a distributed control system patterned after a cyber-physical production system (CPPS)7 architecture should be used for this. For uninterrupted switching between the laser-based processes, a reconfiguration of the real-time network without interrupting the communications is crucial in order to not affect any other currently running or future process on the machine. This requires a specialised concept and operations for the rescheduling of the real-time network8 that will be further developed and implemented.
Figure 2: Key technologies required for a universal laser manufacturing node (Credit: IFSW)
The large variety of different laser processes and laser-based process chains also leads to a wide range of processing parameters that have to be covered by the machine. Required processing speeds can range from a few m/min up to an order of magnitude of 1,000m/s (especially for processing with ultrashort-pulsed lasers9), while required laser spot diameters can range from some tens of micrometres up to a few centimetres. Furthermore, the machine should allow for the processing of 3D-parts of different shapes and sizes within a processing volume of up to some cubic-metres. The currently used kinematic systems cannot meet all of these requirements simultaneously, but are limited in at least one of the aspects, e.g. in their dynamics and/or the processing volume. Therefore, a novel design of the kinematics is required that is specifically tailored for laser-based manufacturing and considers the special properties of light as a manufacturing tool, instead of using kinematics systems that were designed for mechanical machining processes.
With the trend towards batch-size 1, every part to be manufactured might be different, which means that the optimum process parameters for a new part might also be different every time, and have to be determined anew each time. A model-based procedure is assumed to be suited best for this parameter estimation task in order to minimise the associated experimental effort, time, and costs. The approach is to use so-called ‘hybrid models’, which are a combination of physics-based models (e.g. analytical or simplified numerical models) and data-based models (machine learning). This approach can help to make the models more reliable and robust, to compensate for physical effects that are too complex or too computationally expensive to be modelled explicitly, while simultaneously reducing the amount of required training data for the machine learning models.
Quality monitoring is another important aspect of manufacturing and should ideally be carried out directly online. For a universal manufacturing node, this requires a monitoring system that can easily be adapted to the various processes. Ideally, a lean setup should be used for this, which contains only a small number of different sensors. It turns out that a large part of the considered quality features can be roughly separated into ‘surface defects’ and ‘inner defects’. A promising sensor combination for this is the combination of a scanning optical coherence tomography (OCT) system and a scanning pyrometer. Another important aspect to ensure reliable online quality monitoring is suitable data processing methods, which will also be part of our group’s research.
The previously described research topics will be the main focus of the Advanced Manufacturing junior research group’s work over the coming years. The future progress and results in these research topics will represent an initial step towards a universal laser manufacturing node and contribute towards its development.
Dr Michael Jarwitz is head of the Advanced Manufacturing junior research group at the University of Stuttgart’s IFSW
The presented work was funded by the Ministry of Science, Research and the Arts of the Federal State of Baden-Wuerttemberg within the ‘InnovationCampus Future Mobility’, which is gratefully acknowledged.
 Hu SJ. ‘Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization’. Procedia CIRP 2013;7:3-8.
 Cohen Y, Faccio M, Pilati F, Yao X. ‘Design and management of digital manufacturing and assembly systems in the Industry 4.0 era’. International Journal of Advanced Manufacturing Technolgy 2019;105(9):3565-3577.
 Lechler A, Verl A. ‘Software Defined Manufacturing extends cloud-based control’. In: Proceedings of the ASME 2017 12th MSEC2017.
 Xu L, Chen L, Gao Z, Moya H, Shi W. ‘Reshaping the Landscape of the Future: Software-Defined Manufacturing’. Computer 2021;54(7):27-36.
 Graf T, Hoßfeld M, Onuseit V. ‘A Universal Machine: Enabling Digital Manufacturing with Laser Technology’. In: Heieck F, Ackermann C, editors. Advances in Automotive Production Technology - Theory and Application. ARENA2036. Berlin, Heidelberg: Springer Vieweg; 2021. p. 386-393.
 Denkena B, Henjes J, Henning H. ‘Simulation-based dimensioning of manufacturing process chains’. Journal of Manufacturing Science and Technology CIRP 2011;4:9-14. doi: 10.1016/j.cirpj.2011.06.015
 Cruz Salazar LA, Ryashentseva D, Lüder A, Vogel-Heuser B. Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns. International Journal of Advanced Manufacturing Technolgy 2019;105(9):4005–4034. DOI: 10.1007/s00170-019-03800-4.
 von Arnim C, Lechler A, Riedel O. ‘Operations for non-disruptive modification of real-time network schedules’. In: 2021 22nd IEEE International Conference on Industrial Technology (ICIT).
 Weber R, Graf T, Freitag C, Feuer A, Kononenko T, Konov VI. ‘Processing constraints resulting from heat accumulation during pulsed and repetitive laser materials processing’. Optics express 2017;25(4):3966–3979. doi: 10.1364/OE.25.003966.