After watching all those sci-fi movies about robots taking over the world, you’d probably ask, can it happen?
Actually, there’s an army of them, hard at work right now, but not to enslave or exterminate humans — rather help us produce more. Where else can you find these gentle automatons but in the manufacturing sector?
Yes, collaborative robots, or cobots, are bringing new energy and productivity to our modern factories. Try watching them strutting their stuff, and you’d swear that the future has arrived a century early.
Picture this: 24/7 ‘lights out’ factories where robots are building, testing, and inspecting — themselves! They no longer just carry out repetitive mechanical functions but are smart enough to perform cognitive tasks and making independent decisions based on real-time data.
Assembly lines are patrolled by machines that can detect flaws with half the width of a human hair — and use the data to make sure that the cause is fixed.
We should be proud of such technological progress, of course. But as we continue to advance AI in manufacturing to Terminator-smart level, some loose ends need to be fixed on the ground.
Tightening the Screws
As ambitious as transforming the factory into an AI-run facility may be, several bolts and screws remain loose, so to speak.
Not surprisingly, the challenges lay on the human side of the equation.
Decision makers are either not ready or unable to comply with the exacting quality regulations and standards required to set up AI in factories. Too many elements need to be connected — i.e., sensors, communication devices, other high-tech tools — yet most plants are still in the demo stages.
The internal processes of most managers still operate in an old-fashioned way. Many of them are reluctant to adopt new technologies or cling to legacy systems.
Then there’s the critical issue of data. AI, especially machine learning algorithms, require massive amounts of data inputs. However, companies are unwilling to share their information, concerned that revealing them will blunt their competitive edge.
Finally, AI in manufacturing is experiencing a huge gap in skills. Not enough professionals are specializing in the field, and millions — not just thousands — of AI-related jobs go unfilled when they are needed most.
Rust could settle in before the technology really ramps up.
AI Applications in Manufacturing
Ready or not, manufacturing companies now have the option of adopting sophisticated AI applications. Here are some of them:
- Cobots work in close physical proximity to human workers under time constraints and can be quickly re-programmed during manufacturing changeovers.
- Force sensors installed in cobots’ joints prevent injury by automatically stopping them in case of contact with their human counterparts.
- Sensors and advanced analytics embedded in manufacturing equipment report machine conditions to enable the resolution of issues before they occur.
- Sensors simulate in a virtual environment a digital twin of remote equipment to analyze conditions and head off downtime.
- Materials, manufacturing methods, and cost parameters are inputted into design software for the best possible permutations of a solution.
- Cloud computing and machine learning can be leveraged to test what works and what doesn’t in the generative design process.
- Machine vision tools use a machine-learning algorithm to detect microscopic defects and immediately send an alert to address them.
- Machines equipped with powerful cameras not only ‘see’ problems but process the information and learn from them.
- AI algorithm alerts manufacturing teams to possible production faults that are likely to cause product quality issues.
- Quality 4.0 allows manufacturers to collect data about their products’ use and performance to make better design and engineering decisions.
AI is making a tremendous impact on the manufacturing sector. While it cannot be expected to solve all the problems, the industry is left with no other option if it is serious about achieving a high level of productivity that it aims for.
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