Why Factories Using Artificial Intelligence Approaches Are The Future

  • Artificial Intelligence (AI) can be advantageous, especially where the data exhibits some form of non-linearity.
  • To reproduce some of the flexibility and power of the human brain, artificial neural networks (ANN) try and emulate the brain structure through computational models.
  • Different personality types should also be taken into consideration while designing appropriate tools and solutions.

A smart factory with interlinked smart systems is powerful – it responds to customer demands and changes within a short span of time, makes better predictions of future operations, allows for more informed decisions that improve efficiency, etc. However, smart factories are yet to function independently without human involvement. Embodying accurate human-like thinking into a mechanical system will create the most efficient and optimized factories and manufacturing units.

Fuzzy Logic and Neural Network in AI

When it comes to tasks requiring delicate human intuition based decisions, Artificial Intelligence (AI) can be advantageous, especially where the data exhibits some form of non-linearity. Fuzzy logic (FL) and neural network theory (NN) are complementary constituents of AI rather than competitive as it is evident and it is beneficial to employ FL and artificial NN in combination rather than exclusively. To reproduce some of the flexibility and power of the human brain, artificial neural networks (ANN) try and emulate the brain structure through computational models. Suppose, there are five machines running in parallel performing different tasks and one of them gives out an undesired output due to some unseen problem in the machine. Ideally, an intelligent system will retrieve cases from memory that are relevant to solving it and map the solution of that case to the target problem. It will not only map the solution to the problem but, will also adapt the solution as required to fit the new case and will apply the new solution in the real world and store this solution under a new case for future use. Now, in a situation like above, if the other four machines are dependent on the output of the first machine, it might also be possible that these machines might face similar problems which might compromise the safety of the unit. To prevent this, the first system will alert the other systems regarding the faulty situation and the other systems will be able to similarly map out solutions accordingly. This is a classic example of machines adapting and displaying flexibility to different situations independently using a combination of FL and ANN. However, using anecdotal evidence as the main operating principle with no statistically relevant data to support a generalization, is not enough to prove that the generalization is accurate. It is also essential to remember that human beings are not completely rational. Different people might react differently to the same situation and make decisions based on their own unique experiences. Different personality types should also be taken into consideration while designing appropriate tools and solutions.

FL and ANN are not widely used in the manufacturing industry however, once the methods gain traction,  and familiarity, the process of configuring such a system will become relatively easy. It is clear that this technological shift in manufacturing industries is here to stay with the promise to improve the quality, safety and precision.

MachinePulse is bringing the groundbreaking results of artificial intelligence approaches to factories through FactoryPulse. The Internet of Things solution has AI capabilities that improve the overall efficiency of projects leading to better performance and higher returns.

About MachinePulse
MachinePulse™ is an Internet of Things platform provider based in Mumbai. The big data platform, erixis™, has in-built machine learning and predictive analytics capabilities. MachinePulse is based in Mumbai, India with branch offices in Chennai and Bangalore. Contact us to learn more.

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