Sensing the bigger picture

We are in the midst of a fourth Industrial Revolution.

The first Industrial Revolution, which began in the late eighteenth century, mechanized production with steam power. The second, just a century later, introduced electricity and mass production. The third took place in the 1960s and 1970s and brought early automation and computing technology onto the factory floor.

Each of these events fundamentally changed the world and disrupted multiple industries and sectors. And each arguably pales in comparison to what’s currently taking place.

Digitalizing the factory floor

Unless you’ve been living under a rock, you’ve likely heard the term Industry 4.0 at least in passing. Industry 4.0 is a catchall term for the evolution of the manufacturing sector. A deceptively complex concept, Industry 4.0 encompasses multiple technologies, innovations, trends, and subsectors.

Source: Adobe

“Manufacturing 4.0” is one such trend, integrating modern technology to digitalize, optimize, and streamline the manufacturing process. In many ways, Manufacturing 4.0 is the most essential cornerstone of Industry 4.0—and the one that organizations frequently get wrong.

“Like other areas of the 4.0 era, Manufacturing 4.0 is all about using data and connectivity to make processes efficient and lean while enabling intelligent systems to make decisions,” explains Mario Sheppard, Senior Engineer, Automation, Robotics & Manufacturing Operations at Supernal. “[Although] artificial intelligence, machine learning, augmented reality, and big data tend to monopolize the spotlight, [several] vital—albeit under-recognized—technologies [are] helping to advance Manufacturing 4.0.”

These supporting technologies ultimately all serve the same purpose—awareness.

Keeping eyes on the ground

“Without sensors, manufacturing automation simply could not happen,” Sheppard continues. “Sensors are the digital eyes, ears, nose, and fingers of automation that remove guesswork or assumptions, which leads to much safer, consistent, efficient working conditions. From a data-collection standpoint, sensors are the gateway to the insights you seek in that they provide the raw data [that are] used to tell the story of what’s happening along the production line.”

These sensors may include the following:

• Positional sensors to align components of an industrial machine
• Presence detection sensors to identify personnel and obstructions in a machine’s operational area
• Size-detecting sensors for quality control and operational safety
• Contact sensors to identify whether a hatch or door is closed or to hard stop a piece of equipment to prevent damage
• Vibration sensors to help facilitate more efficient maintenance (for example, vibration from a servo motor may indicate deteriorating parts)
• Temperature and humidity sensors to maintain optimal operating conditions and detect possible hazards
• Level sensors to manage and monitor the volume of materials such as fuel
• Infrared sensors, which are frequently used in high-tech fields such as medical, military, and aerospace manufacturing
• Pressure sensors to monitor pipes and tubes for leaks or blockages

Most of the sensor technology listed above is hardly new. Some of these sensors, such as pressure gauges, have existed for decades. How exactly do these systems enable manufacturing automation? In two ways: through sensor fusion and through the use of programmable logic controllers (PLCs).

“Sensor fusion has also advanced the quality and type of data that can be collected and the certainty of insights derived from the data,” Sheppard explains. “You might use a laser sensor to detect height and a vision sensor to confirm. [This] enables you to combine data based on [each sensor’s] strengths.”

Sheppard continues: “When collecting information from machines, communication is not sensor to sensor. It’s from sensor to PLC along the manufacturing network…. PLCs are the brains of manufacturing. [They are] where logic and process information is stored and [also] where network communication begins.”

Advancing programmable innovation

In its simplest form, a PLC is a ruggedized, solid-state, industrial computer used to control manufacturing processes. Using input from one or more industrial sensors, it determines the order of operations for several complex manufacturing processes based on real-time, logic-based decision-making. Output destinations may include the following:

• Sirens
• Relays
• Indicator lights
• Cylinders
• Analog outputs
• Robots
• Complex machinery (such as robots)
• Other PLCs

Essentially, when a pressure sensor or proximity sensor detects that something may be amiss, the PLC will act on those data. Imagine, for instance, that someone inadvertently activates a trash compactor while a colleague is retrieving something inside. The associated PLC will see the input from the trash compactor’s proximity sensor and immediately deactivate the machine.

This use case represents only a fraction of what PLCs can achieve. With the right sensors and the proper code, they can automate even the most sophisticated manufacturing processes. PLCs can also significantly improve operational efficiency when implemented as part of a closed-loop digital twin (CLDT).

“The idea of CLDTs is to use a virtual model that ideally accounts for all systems and variables that affect production efficiency,” says Sheppard. “Here, the PLCs provide historical and real-time [input/output] data that, along with data from other systems, can be used to fine-tune machine settings, staffing, material storage, and other operational aspects. CLDTs can be implemented at any scale, ranging from a single piece of equipment to an entire [factory floor].”

Together, augmented sensors and PLCs offer powerful guidance for modern manufacturing processes. These systems, however, only represent part of the equation.

Simply collecting data and defining a few action triggers is not enough. Manufacturing 4.0 demands more—it demands autonomy.

Seeing, thinking and acting

In a way, vision systems represent the true intersection of Industry 3.0 and Industry 4.0. These advanced platforms ingest information from across the entire factory floor—from every sensor, camera, logic controller, and application. They can then leverage this information to perform several different tasks:

• Configuring the position and orientation of each part in a machine
• Sorting inventory
• Recognizing bar codes
• Identifying defects and abnormalities
• Ensuring compliance
• Enforcing standardized sizing on components
• Identifying parts and ensuring they’re properly constructed and oriented

“In manufacturing environments, vision systems can be trained to recognize objects that can be measured, counted, decoded, or positioned,” Sheppard explains. “As with other machine learning applications, training requires large datasets in which characteristics of shape, size, orientation, edges, patterns, colors, and the like are labeled. For example, [a] system might be trained to identify tubes of a specified length and circumference with [plain strip fins and] welds placed 0.14″ apart—the trained system would then store a reference image or a series of reference images.”

In practice, this is much simpler than it sounds. Vision systems used in this fashion basically employ a sophisticated form of pattern recognition; they’re trained to recognize a specific formation of pixels and then deliver a result of either pass or fail. While a vision system may improve at recognizing variations of the original pattern, it still cannot surpass its original purpose.

In fairness, it doesn’t really need to. As they exist now, vision systems have proved invaluable to Manufacturing 4.0. According to Sheppard, they’ve advanced the manufacturing sector in a multitude of ways:

• Improved product quality
• Reduced materials waste
• More efficient use of time
• Improved worker productivity
• Lower operational overhead
• Easier traceability and accountability
• Streamlined regulatory compliance

Conclusion

The manufacturing sector of today and the manufacturing sector as it existed ten years ago may as well be from completely different realities. With the birth and proliferation of Industry 4.0, manufacturing has evolved at a breakneck pace. Buzzworthy technologies such as artificial intelligence, cloud computing, and big data are undoubtedly at the heart of this revolution.

But a heart is useless if it lacks blood—in this case, the information generated by sensors, PLCs, and vision systems. As Sheppard notes, these technologies may be less glamorous than flirting with the singularity or searching a veritable ocean of data, but they are no less important:

• Sensors capture the data necessary to generate insights and guide decision-making.
• PLCs facilitate communication between systems and apply low-level automation.
• Vision systems are sophisticated tools that allow machines to see and identify objects, convey critical information, and autonomously react to data.

“Flashy technologies may steal the spotlight, but these less glamorized [systems] are vital to Manufacturing 4.0 and beyond,” Sheppard concludes. “Without them, there can be no fourth Industrial Revolution.”

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Nick Greene is an experienced writer who works closely with multiple brands to create technology content.  https://www.mouser.ca/