Beyond Automation: Building the Intelligent Factory

Why the fate of factories and that of machine learning are intertwined.

Factories already have a lot in common with living beings. They consume raw materials, require energy, and have interlocking systems that all move in a complex choreographed dance toward a shared goal. Automation and computationally driven designs have given us factory equipment that can perform repetitive tasks with some variation based on operating conditions and control signals.

But today’s factories can’t learn from their own mistakes, innovate autonomously, or teach themselves how to optimize existing processes. That day is coming soon, on a wave of machine learning that will drive the intelligent factory of the near future.

Machine learning, combining distributed artificial intelligence (AI), advanced sensors, and precision robotics, is taking manufacturing into Industry 4.0. It will be the fourth major era for manufacturing, following steam power, assembly lines, and automation.

Crucial Technologies for the Intelligent Factory
A number of significant advances are coming together at the right time to make learning machines and intelligent factories a reality. Wireless networking meshes have reached a degree of speed and reliability such that hundreds or even thousands of devices in a single factory can quickly and safely exchange information with each other and with central data stores. Data mining and analysis have advanced to help both human and AI analysts find patterns hidden in the records, uncover buried inefficiencies, and drive errors out of the workflow. Cloud technologies can store untold amounts of data and perform constant analysis. And small ultra-low-power networked sensors are capable of accurate measurements well below 1mm and can distinguish between materials such as plastic, drywall, and fabric.

Meanwhile, the huge investment in self-driving automobiles benefits manufacturing with machine-vision breakthroughs, making computers better than ever at recognizing objects and correctly manipulating them. Computationally powerful but energy-efficient multicore processors are small and affordable, and can be programmed and repurposed by a wide range of coders worldwide. All of these elements are the building blocks of automated systems that will guide, control, and educate the next generation of manufacturing capital.

How Data Becomes Wisdom in the Intelligent Factory
For decades, data has been essential to safe and efficient operations in any factory. Human operators already collect and analyze raw facts and figures about inputs, outputs, waste, duty cycles and mechanical failures. Advances in AI and big data processing make it possible to create machines that cannot only generate more raw data, but can also process the data into meaningful information, understanding its content and applying that information as learned wisdom. These machines will come together in intelligent factories and learn how to avoid mistakes, correct imbalances, and improve processes.

Today’s “smart” machines are only as adaptable as their programming. Even a thorough coder cannot account for all of the contingencies and variations a typical factory environment can face. Wear and tear; variation in raw material quality; and environmental factors like temperature, dust and grime can cause yields to fall and components to fail, forcing costly slowdowns, repairs, and adjustments.

With access to massive cloud data storage and computation, as well as high-speed integrated processors, machines can start learning from conditions as they occur. Distributed intelligence networks can analyze every robot’s position and activity and every sensor’s report on temperature, proximity, orientation, chemical composition, distance, and more.

Figure 1:  A self-controlled machine acts based on wisdom distilled from lower levels, ultimately arising from massive amounts of data.

Instead of just collecting data for later analysis, intelligent factories will be able to apply AI to reach conclusions, make informed judgments, and take corrective action. Robots will compensate for drift as parts heat up or bearings wear down. Chemical control systems will optimize recipes as conditions change, analyzing slight variances in supply batches. Re-tuned and synchronized motors will work more efficiently on cooperative jobs.

The Impact of the Intelligent Factory
When they do come online, intelligent factories will become a new engine of growth and profitability as self-healing, self-improving centers of innovation. Manufacturing excellence in the Industry 4.0 world will belong to those who give their machines the data and resources they need to perceive and report on the work they are doing, with enough computational heft to translate that data into wisdom and act automatically.

Combining AI with advances in both machine vision and voice-activated agents will make robots not only more powerful and productive, but also safer and more reliable.

The intelligent factory doesn’t mean the end of human labor. In fact, industrial intelligence could enable people and large-scale robots to work together much more closely. Instead of being separated by safety barriers, intelligent factory robots will be able to automatically detect people nearby and adapt their own work to take greater precautions. As the safety barriers around robots shrink or come down entirely, further work on power conditioning and signal isolation will ensure that robots have steady and reliable power sources that pose little risk to people and other machinery in proximity.

We’ve yet to imagine the impact of intelligent factories. No one could have invented Kanban or computer numerical control (CNC) without first seeing an assembly line. In the same way, it’s safe to say that many of the processes uniquely well-suited to intelligent factories won’t be invented until the machines themselves start coming online. Human imagination, unbounded by the need to rigidly program robots for specific tasks and contingencies, will play a huge role in shaping the increasingly complex mechanical, chemical, and biological products made possible by intelligent factories.

Matthieu Chevrier is Systems Manager, PLC systems. Chevrier leads the system team based in Freising Germany, which supports a WW base of PLC (Programmable Logic Controllers) customers. He brings to his role his extensive experience in embedded system designs in both hardware (power management, mixed signal, and so on) and software (such as low-level drivers, RTOS, and compilers). He earned his master of science in electrical engineering (MSEE) from Supélec, an Ivy League university in France. Chevrier holds patents from IPO, EPO, and USPTO.


Tobias Puetz is a SystemsEengineer in the Texas Instruments Factory Automation and Control team, where he is focusing on Robotics and Programmable Logic Controllers (PLCs). Puetz brings to this role his expertise in various sensing technologies, power design, and wireless charging as well as software design. He earned his master’s degree in electrical engineering and information technology at the Karlsruhe Institute of Technology (KIT), Germany in 2014.


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