Flexible assembly: production in motion
Flexible assembly: production in motion
Mass production has historically not been the most flexible way of manufacturing. Production lines were set up to build a single product and switching to another product required a lot of effort. However, products are increasingly customised as customers increasingly expect personalisation. This is the case with consumers, who expect their products in multiple colours, sizes and finishes. But also in the corporate world, there is a high demand for specialised products, such as air compressors, looms and feed harvesters, products produced in small batches. This evolution makes flexible assembly increasingly important in industry and is therefore at the forefront of Flanders Make's agenda. Our new site in Kortrijk will focus even more on this trend.
To make specialised and personalised products at mass production prices, we need a high degree of flexibility but still at high productivity. This puts us at the fault line of flexible, human labour and fast, automated production lines. So to achieve our goal, we need to improve either human productivity or the flexibility of automated production lines. Research at Flanders Make naturally focuses on both avenues and on hybrid solutions where people work together with automated production lines.
Robot programming
Robots are the ideal technology for flexible automation because they are in essence very flexible in their movements and actions. However, programming them remains a significant part of the total investment. An expense that is also required again for every change in its tasks. Being able to programme robots faster is therefore an important factor for more flexible assembly, allowing the robot to quickly switch between different tasks and products.
At Flanders Make, we believe that skill-based programming & operation is the solution to reduce the time it takes to programme robots. Here, a task in the assembly process is broken down into small skills until we get to individual movements or actions of the device. Think of grasping and moving an object or performing a quality check on a camera. Then, for each product to be assembled, all that is needed is a new recipe indicating which actions to perform, in the right order. The basic skills are already established.
The only complexity that then remains is capturing all the skills of a robot. It is always possible to programme them in the traditional way, but we can also teach them by demonstration. Some skills can even be learned (partly) automatically, through a machine learning process.
Infraflex demonstrator
To demonstrate these concepts in an industrially relevant context, we built a robotised flexible assembly cell, which we named Infraflex. With this, we incorporate a whole range of concepts around flexible assembly in a very small area. For example, the central robotic arm can be quickly programmed with a recipe that combines a range of skills into a task. Around the robot arm there are also a series of add-ons that take over small tasks, prepare kitting or prepare new parts. The setup shows concrete situations where the cell assembles a compressor, for example, but temporarily pauses this task for a rush order, preparing new parts for a completely different assembly process. Afterwards, the cell can resume its original task.
Human flexibility
Yet robots cannot take over all tasks from humans equally easily, and flexible assembly is not a story of just automation. Here too, we look for the best of 2 worlds by combining the productivity of automation with the flexibility of humans. Thus, Infraflex also includes an add-on for manual assembly and the human skills in the recipe are also provided with digital work instructions projected on the add-on. This helps to dramatically shorten an operator's training. Cameras can even monitor task progress and feed it back to a central planning algorithm that takes this into account in the other tasks in the recipe.
By dividing a task into skills and looking at how best to perform each small subtask, we can facilitate hybrid assembly by humans and robots. Thus, on the one hand, tasks impossible for a robot can be taken up by humans. On the other hand, a scheduling algorithm can also monitor how long each takes on its subtask and then adjust who takes on the next subtask.
Flexible kitting
Making the most of this approach requires a structured working environment. Digital work instructions can indicate the location of a part, but then, of course, that part has to reach that location somehow. Parts must therefore be delivered in structured kits that make their locations predictable. An assembly may be faster if the right screw is always at hand, but if this means someone still has to look for it while assembling the kit beforehand, it is still not a time saver in the complete picture.
So to make assembly more flexible, there is definitely a need for flexible kitting as well. We therefore extend the skills-based approach to kitting to automatically assemble a new kit. Again, a simple recipe is enough to collect parts in the kit, and the robot automatically finds them together. The remaining challenge is then just to consistently keep sufficient stock of all parts.
Flexible grippers
When we talk about the automated assembly of kits, many naturally raise the legitimate concern around the picking up of these parts. After all, a kit can contain very different parts, from a heavy electric motor to the tiniest of screws. If we want to move these in an automated way, it has to be done with the right gripper and each part has to be gripped in a specific way. However, a specific gripper for each part is not possible, so there is a need for flexible grippers that can grip a whole range of parts.
First of all, we have to make an important choice between some types of grippers. For example, there are mechanical grippers with moving fingers that grab objects, as well as grippers that function with electromagnets or vacuum suction cups. Depending on the gripper type chosen, we then look at the optimal gripping points to grab each object. These can be sides of the object that grippers can clamp, but also surfaces that suction cups or electromagnets can clamp to. Finally, we create a final design for the gripper, further adapting the gripper to the objects if necessary. This can be done, for example, by adjusting the length of a gripper's fingers, but also by correctly aligning suction cups. Flexibility is also possible here by making grippers interchangeable or adaptable so that adjustments in production can also be smoothly accommodated here.
Larger assembly processes
Of course, everything we have discussed so far mainly applies to smaller products and components. Therefore, the next challenge for Flanders Make is to make the leap towards larger products, such as trucks or looms. When we work at that scale, we can no longer speak of supplying small parts to a robot arm, but rather, in many cases, move the robot arm to the product. That brings a lot of additional challenges, such as keeping a shop floor safe where employees also walk around or accurately determining where all the parts and machines are in a larger factory hall.
To explore this further, we are currently developing a new lab in our new building in Kortrijk, where we are taking the concepts of this blog post to a larger scale. Mobile robots, automated cranes and multiple assembly cells working together form the basis for this. But certainly also the software and data that will link it all together.