Smart manufacturing has garnered substantial attention since the emergence of Industry 4.0 in 2012. The automation of manufacturing technologies, intelligent deep and machine learning AI systems that are operating masses of advanced data analytics to improve the manufacturing process’s system performance and decision making.

As production lines become more adapted to building with machines, the complex body requires an adaptable mind to guide its hand to match the high velocity, large volume of various roles in manufacturing. Deep learning is being used globally, multiplying amongst hundreds of businesses, evolving how manufacturers big data is used to create profits, bring down labour spending, and boost production speed and efficiency.

It is already evident with behemoth companies such as Microsoft, Intel, NVIDIA, Siemens, Kuka, etc, who have made massive adaptions to using machine learning-equipped approaches to benefit their manufacturing process.

Deep Learning is being set with the task of runnign their giant factoriers, assembly lines and all. Deep Learning models are more flexible than previous counterparts; able to identify the very relationships between software and hardware that even human beings may have missed. 


The smart manufacturing industry is making significant developments and will continue to project improvements for the next five years. The global smart manufacturing industry already has a value peaking $200 billion and will continue to expand until it has nearly doubled in worth by 2020.

The wave of automated industry has been surfing towards us for over a hundred years, spanning back to the universal power of steam and the first mechanized productive machines. The second industry wave came in the form of mass production; the birth of the assembly line and the industrial revolution that changed the world. As humanity adjusted to mass production and an adapted workforce, the twentieth century saw a turn in era with the third industry rising, the advent of computers and the slow automation with robots and machines on assembly lines.

The fourth age has completely flipped the previous perspectives of mass production and a manufacturing workforce. Automation has created the smart factory, an entire production line controlled by a singular cyber-physical system that monitors the continuous processes of the factory in order to make quick and valuable decisions. You see, these computer systems are connected to the very robotics of the assembly line. It is a manufacturing singularity.

Smart manufacturing is causing a huge shift in the way we operate and manage manufacturing. By enabling edge computing to their network, manufacturers are enhancing the very connectivity of system to the very core mechanics of their factory. Efficiency is peaked on both ends by the singularity. With artificial intelligence, improvements are constantly being found to a producer’s settings as the system continues to make adjustments in making a perfect assembly line environment.


These are just old words with a new tune, of course. Automation and robotics have been in the manufacturing industry for years. Now, with the crowning of deep learning to a mainstream setting, manufacturers are streamlining the advanced field to a new level. The technology is being used to start a new step on the evolution of their industry.

Deep Learning has the potential to prevent an array of current factory issues, perhaps even the ability to detect an upcoming defect before the anomaly has even occured. By analysing its data, the AI may be able to automatically take corrective measures that prevent the defect from even occuring. Soon, Deep learning may become more self-reflective in terms of its workload, able to automatically figure out when it needs maintenance and repairs and accurately know how many products it will be able to create in a single day. These are very much in the realm of possibility,

With amazing connectivity, advanced sensors, swifter robotics, and a commanding analysis of data and analytics, machine learning is opening up some amazing possibilities; less equipment failures, less training, the possibility for product customisation, and a higher resource for mass produced goods. One day, every mass produced good may be able to be tailor-made to a customer from the very factory floor itself, with machines programmed with the specifics required.


The only hurdle is in the price of developments such as these. The costs to remodel an entire factory floor to fit these models, as well as the training for personnel to continue the operation of such machines, is still a substantial amount that smaller businesses are yet able to meet.

However, as advancements are made, and customisations become available, even modest manufacturers are finding system models that can match their budget and workload.

We are on the horizon of what Deep Learning is capable of, with its amazing properties and endless possibilities. It is up to us now to fix what may be issues with it further down the road. Datasets must be accumulated now by manufacturers and producers if they wish to give their systems a step up on processing the running of their factories. The future of how manufacturing is dealt is dependant on the current leaders of the industry.

Written by Harry Pages

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