For anybody planning their automation strategy and interested in understanding and using robots, Irish Manufacturing Research (IMR), in conjunction with Engineers Ireland, is running a four-week CPD accredited course that will enable you to identify and evaluate deployment opportunities for robotics and automation in your organisation and identify where the greatest business impact can be achieved.
The course is intended for anybody planning their automation strategy and interested in understanding and using robots; it covers the core concepts and state-of-the-art approaches; planning and evaluating robotics; plus outlining the pitfalls involved. It is aimed at engineers, engineering managers, manufacturing process engineers, and project managers interested in automation.
Collaborative robots have the power to transform industrial operations by increasing flexibility and reducing capital investment. Collaborative robots are designed for safety by using compliant links/actuators, removing pinch points and enabling the low-cost integration with a suite of advanced sensors.
These attributes could allow the accelerated cell integration fixed infrastructure and thus enable small medium-sized enterprises (SMEs) to exploit automation in their factories.
However, in spite of the progress in hardware and programming interfaces, the lack of clear guidance regarding the implementation of safety requirements (ISO 15066) coupled with confusion over correct automation paradigms for different tasks is impeding the widespread use of human robot collaboration.
In this course, we will address the safety requirements, and discuss where and for what task collaborative robots should be used.
Machine vision, powered by deep learning and the increase of available computational power, have greatly advanced over the past few years. Machine vision enables a robot to perceive its environment and use this data to complete a task.
The increasing availability of 3D cameras has opened up a range of applications such as obstacle avoidance, online path planning and random bin picking.
By exploiting the robustness to uncertainty provided by machine vision, rigid production cells characterised by precisely machined jigs and fixtures can be transformed into more flexible systems.
However, often complex assembly tasks cannot be accomplished by vision sensors alone. Consider a delicate assembly operation comprising snap fits or bolting components together. Humans feel a misalignment and can adapt accordingly. A robot using vision to generate an input for a stiff position controller lacks this sense of touch.
If the system is programmed to follow a trajectory, a small misalignment will lead to an ever-increasing robot torque to compensate for the position error.
This, in turn, will result in damage to the component or tool. By equipping a robot with a force sensor such errors can be avoided and in more advanced controllers the signal can be used to change the robot’s behaviour.
On a wider scaler, autonomous mobile robots (AMRs) will be a key component of the interconnected factories foreseen in the industry 4.0 framework. Instead of fixed assembly line structures, a system consisting of a mobile manipulator and a set of modular production cells provides manufacturing flexibility and robustness to errors in individual manufacturing steps.
By logging and communicating the data associating with each production cell, a high lever logistics planner – known as a fleet manager – can exploit the current state of the cells to optimise the assembly flow. However, there are challenges to implementing AMRs notable in the configuration of the factory and integration with existing infrastructure.
Frequently, however, infrastructure at a cell level cannot be altered.
For instance, a robot may be positioned next to a machine or output dock. In this case the classical method of programming the robot – ie, using the teach pendant – will take an enormous amount of time, during which the production cell is not running.
While this may have been acceptable when each cell ran untouched for a number of years, the mass customisation trend means cells will have to be reconfigured frequently and cope with large product variations.
Collaborative robots, machine vision and force sensors can greatly increase a system’s flexibility, and this is magnified when used with offline virtual commissioning and path planning techniques.
These offline software suites use a digital twin of a production cell to plan robot trajectories and validate cell functionality. Complex trajectories can be generated using trajectory optimisation and sampling-based methods to allow robots to plan efficient paths in cluttered environments.
Many of these tools even allow the direct transfer of the resulting task to the system. Additionally, by using next generation exteroceptive sensing systems, the digital twins can be updated with real time information to ensure a plan's tasks are fully representative of the current state of the cell.
IMR has considered these critical factors and insights on behalf of industry in our role as Ireland's largest research technology organisation (RTO), building flexible and engaging learning experiences through the power of digital communications and resources such as e-Learning, video, job aids and micro learning, with the richness of human interaction.
Understand the benefits of 3D vision, adaptive assembly and what collaborative robots bring to your application. By conducting real-world tasks each week with expert help and guidance, you will identify and assess opportunities for automation in your workplace, learn how to select suitable technology options for automation, consider options for integrating robots into your workplace and learn how to build a business case for automation.
Digital online blended learning platform with live virtual classrooms, including:
Throughout this IMR course you will be supported by its expert instructors in putting together an innovation approach, specific to your business.
The course – which is over four weeks and is 37 Hours Engineers Ireland CPD approved – will cover several topics which are currently or will in the near future transform how robots are used in the factory.
To book or find out more information email: email@example.com