Practical AI in manufacturing

The role of AI & Machine Learning in your Factory of the Future

Artificial Intelligence (AI) is a crucial piece of the factory of the future. Connecting the manufacturing process through an end-to-end connected factory provides unlimited production data that humans simply cannot fully compute.

The biggest misconception of AI is that it is an abstract, futuristic concept. In reality, valuable practical Machine Learning and AI applications are ready to be put to use today. High-value, low risk applications can be applied to boost OEE, maximise production quality and empower workers.


Benefits of Applying AI & ML to Today’s Manufacturing

Using AI, massive amounts of data can intelligently be analysed to find new, unprecedented avenues to maximise output, boost quality, and work alongside human operators and technicians to elevate their roles.

Machine Learning, as an element of AI, provides automated insights that either take humans a long time to do – or that humans simply cannot spot. From shop-floor diagnostics to extremely accurate predictive production capabilities, automation which harnesses the power of Machine Learning will propel your Factory to the Future.

Integrating smart automation and Machine Learning throughout every step of the production process provides insights earlier that will lead to taking immediate actions to prevent downtime and losses.

Insights derived from AI through Machine Learning can be applied on each process step, boosting human-machine interaction. These insights solve both chronic issues, which are ongoing and having a lower level impact on performance, and sporadic problems, those momentary changes or faults that cause performance issues.

As manufacturers worldwide continue their shift towards the factory of the future, AI and Machine Learning technologies are becoming stronger, more intelligent, and more practical.

applied industrial ai in manufacturing

 

Machine Learning vs Artificial Intelligence

Artificial Intelligence and Machine Learning are related, but the terms shouldn’t be used interchangeably. In fact, Machine Learning is a subset of Artificial Intelligence. It’s the natural evolution of the use of AI to generate more intelligent, more connected production through the use of self-learning technology.

Machine Learning is tremendously practical in manufacturing to generate novel avenues for process optimisation.

ARTIFICIAL INTELLIGENCE

Umbrella term encompassing the use of computers to gain insights into complex systems using process generated data.

MACHINE LEARNING

Subset of applied artificial intelligence: the forefront of AI.

 

Expert systems where computers are instructed how to analyse data, what data to use, and what results to generate.
Computers use data to make up the rules and intelligently use automation to develop their own algorithms specific to the process or computational output.

 

The machine, or central processor, learns from data similarly to how the human brain learns new skills, habits, and knowledge: learning by experience and improving through iteration.

 

Using Machine Learning for Manufacturing

Today, one of the use cases for Machine Learning is to identify production issues faster. Initially, we may need humans to act on this data to bring performance back into line, but over time we want to leverage machine learning to deploy an automated process (i.e robots or drones) to make the required adaptation or fix.

By doing this over time, we iterate enough to improve performance and reliability with reduced labour, ultimately lowering the cost of transformation.

Defect Detection

Minimising defects is one of the pillars of boosting OEE . Quality teams and production leaders can use Machine Learning to assess output using historical production data as well as real-time output.

Machine Learning can recognise production defects like scratches, dents, low fill levels, and leaks.

Machine Learning can also help with the reduction of false positives as the machine can learn to spot small variances within tolerance to an exceptionally high degree of certainty.

More about moving Beyond OEE →

 

Preventative Maintenance

Technicians and engineers undoubtedly rely on operational data as well as their experience and tacit knowledge to design maintenance schedules.

Machine Learning uses historical and real-time data to establish optimal TPM procedures and preventive maintenance routines. Instead of relying on human intuition, use artificial intelligence to ensure preventative maintenance activities are optimised. Machines receive maintenance exactly when they need it, eliminating failure while minimising excess labour for technicians.

 

Digital Twins

Use real data to run extremely accurate simulations to model process changes, upgrades, or new equipment.

Machine Learning facilitates data computation across a factory’s processes to mimic the entire production line or sections throughout the process.

Instead of running real experiments, data derived from machine learning produces near-perfect simulations which can be optimised and adjusted before starting real-world trials.

 

Quality Assurance

Machine Learning over hours of production output can pick up and self-learn trends throughout the manufacturing process. Before a product shifts too far from accepted standards, automation can intervene and self-correct.

Achieving Quality 4.0 in your Factory of the Future relies on integrating AI and Machine Learning.

More about Quality 4.0 & Quality Intelligence →

 

Practical AI for Manufacturing Applications Solutions in Your Factory

The main goal of Machine Learning is to continue to drive down the cost of manufacturing through improving machine and line efficiency, optimising labour, and predicting outcomes more accurately.

LineView is currently working on Artificial Intelligence proof of concepts, such as these, to suggest potential actions, guided interventions and forecasts for better outcomes.

1. Task/SIC Review and Resource Allocation

Finding trusted partners who share your company’s vision of an end-to-end factory of the future manufacturing ecosystem is the most important step towards success.
Of course, working with external partners poses potential security risks. Finding and vetting valued partnerships is vital in setting up long term collaborations.


2. Accurate Production Planning

Given a sequence of production requirements like production order and volume, Machine Learning will tell you what will most likely be achieved and by when.
Using information like on-shift teams, time of day, daily product SKUs, and historical track records, machine learning algorithms will generate a more realistic and accurate production planning based on proven data.


3. Tackling Abnormal Machine Behaviour

Machine Learning can alert technicians and operators to abnormal behaviour and guide them exactly where to focus on that machine. The algorithm generates a confidence level of that process or output being abnormal and will take what happened around that time and record all of the production variables.
Machine Learning can give the individual the knowledge that the machine is behaving differently than normal as well as provide context as to why. Faster, more informed insights reduce the likely impact on long term line performance, nipping problems in the bud before they escalate.

Ready to collaborate on integrating AI and Machine Learning into your factory of the future? Let’s talk →


 

At Lineview Solutions we are passionate about making sure you get results.

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