Robotic Process Automation (RPA) Technologies in Additive Manufacturing

Robotic process automation (RPA) constitutes a set of software technologies that facilitates the building and deployment of software robots (bots) that mimic human actions when interacting with graphical user interfaces and other digital systems. Actions, such as opening a file, moving the mouse, filling in form fields, etc. may thus be automated and be carried out in a much faster fashion after training and programming specific RPA platforms.

RPA frameworks are finding application in various manufacturing and service domains with the primary goal being to automate workflow processes and data handling. Standard RPA frameworks may offer the capability to develop new applications by using visual programming blocks, thus enabling designers /engineers to deploy an automated process flow with minimal programming knowledge. The basic idea behind RPA technologies in the manufacturing sector is to allow trivial parts of a process or data exchange workflow to be automated with a high degree of precision.

RPA technologies for Additive Manufacturing (AM)

An RPA-based workflow could in principle lead to time savings by up to 50%, depending on the type of workflow when compared to a traditional workflow that does not implement an automation framework. Manufacturing services are striving to become more autonomous and efficient, introducing more automation and robots in specific parts of production and assembly systems. The introduction of smart automation systems is becoming a necessity for companies so that they may react quickly to changing market trends while staying competitive. The use of such systems also comes with a downside, which is the generation of huge volumes of product–process lifecycle data. To manage such volumes of data, it becomes necessary to deploy a framework that is capable of profiling, detailing, documenting, and delivering data in a highly structured manner for the efficient management of the workflow. This will ultimately lead to delivering lean and agile manufacturing services, leading in turn to reduced product–to–market times.

At the Laboratory for Advanced Manufacturing Simulation and Robotics in University College Dublin (UCD-LAMS), our team is currently engaged in a series of projects where smart digital technologies, such as RPA, are designed, deployed and tested in carefully selected realistic use cases. In particular, RPA technologies have been tested in a set of AM-based product design and development phases [1]. An RPA-based software framework was built for automating the process of selecting and simulating different Fused Deposition Modelling (FDM) process configurations. The framework would automatically select different layer thickness, infill density, support options and material, interact with a CAM slicing tool, analyse the simulation results in terms of time, cost and material usage and would present a consolidated view of these results to the user, thus allowing designers / engineers to take informed decisions in a very fast and accurate manner.

In the future, the use of advanced analytics and feedback control together with augmented reality-based visualisation could lead to further process workflow improvements and to far more detailed feedback to designers / engineers. Integrating an RPA framework with the typical CAD-CAM workflow could make designers / engineers more productive, by eliminating trivial tasks and by exploring a bigger part of the manufacturing process parameters space. Ultimately, a framework, such as RPA that allows the automation of tedious, repetitive, and monotonous tasks within a product lifecycle workflow, could help designers/engineers focus on more complex tasks.


In summary, the advantages of employing RPA technologies include

  • Once properly implemented, RPA frameworks will yield consistent results.
  • Reduction of the time spent by the designer / engineer on repetitive and monotonous tasks.
  • Allowing designers / engineers to better manage their time and engage in more complex tasks.
  • Cost and time savings while executing manufacturing workflows.
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