Empowering Digital Factories: The Critical Role of Electronic Components in Smart Manufacturing

The introduction of computers marked the beginning of digitizing manufacturing. Digitization has since evolved into highly interconnected, intelligent, automated and autonomous manufacturing systems, shaping today’s digital factories and smart manufacturing plants. Increased productivity, efficiency and sustainability are the primary goals of a digital factory. 

Many technologies, such as machine learning (ML), remote monitoring and intelligent analytics enable various applications in digital factories, including smart diagnostics, predictive maintenance, extended reality (AR/VR) and digital twins to achieve these goals.

Advanced electronic components are at the core of this digital revolution, providing processing power, data security and energy efficiency. In digital factories, among other components, processors, power management ICs (PMICs) and sensors play a crucial role in improving manufacturing productivity and achieving sustainability goals.

Transforming Digital Factories with Smart Sensors

Sensing is foundational to understanding the physical world in the digital domain. Sensors are like the eyes and ears of digital factories, impacting every stage of factory automation. 

Embedded sensors in connected industrial processes provide real-time data on ambient parameters like temperature, pressure, humidity and vibration. This data feeds into remote monitoring, predictive maintenance and digital twin applications to obtain real-time insights on equipment operational status and overall plant health.

Smart sensor technology advances sensing by embedding a microprocessor on the sensor. Sensor data processing can happen onboard these smart sensors before the data is communicated to external devices. Local data processing and analysis help offload processors and the cloud, improve privacy and system efficiency and reduce latency.  

Adding intelligence to sensors makes entirely new functionalities possible. For example, non-ML experts can use smart sensor platforms to build machine learning (ML) applications leveraging the native ML and AI building blocks. Sensor data of multiple parameters to be locally analyzed. This allows predicting actionable insights to improve asset efficiency and lifetime while reducing maintenance costs, unplanned downtime and energy consumption at a system level.

Modern micro-electromechanical (MEMS) sensors with ML capabilities help reduce the time-to-solution in digital factories. MEMS sensors can accurately sense and analyze multiple parameters for early failure detection and intelligent decisions, thus reducing the time to resolution while offering better power efficiency partitioning.

Processors Power Time-Sensitive Applications

Processors enable real-time data processing, complex analytics and factory automation in digital factories. Along with PMICs, processors render more efficient and sustainable manufacturing processes by efficiently regulating and distributing power, reducing carbon emissions and lowering electricity costs.

Processors used in smart manufacturing range from applications with low power and data rates to computationally intensive high data-rate processing.

Digital factories automate industrial command and control systems that require real-time device monitoring and generating time-sensitive control signals based on sensor data. Real-time response times in the order of micro or milliseconds are typical in factory automation applications. Embedded processing with integrated microcontrollers and microprocessors is critical in meeting these critical response requirements.

Embedded processors must be able to handle a range of processing power, depending on the complexity and demands of the industrial application. Some examples are executing real-time control algorithms, churning data analytics, executing ML/AI algorithms and processing network communication. In applications involving industrial motors or gas turbines, faster processors enable faster cycle times, translating to improved productivity and efficiency.

Designing Components for Smart Manufacturing

The specific needs of industrial applications drive component design. In automated production processes, the type and rate of sensing, power source requirements, desired analytics and insights, response times and other operational environment factors are important considerations while designing components for a specific solution. The desired degree of intelligence, network connectivity, power consumption, safety and security parameters are equally important.

Sensors, processors and microcontrollers are integrated into the edge network in connected factories, which exposes them to new attack vectors that can compromise sensitive data and intellectual property. Component security becomes a crucial design consideration. The embedded processors must incorporate robust security features like identity and authentication, data encryption, secure boot and firmware update capabilities. 

Power management is another fundamental consideration when designing sustainable factory automation systems. Power management solutions optimize energy consumption by monitoring energy sources in real time. Components that maximize energy conversion to reduce power leakage help to meet energy-efficiency requirements. 

Power management also helps ensure a clean, well-regulated power supply to the devices being powered across a wide range of temperature, humidity, shock/vibration and other environmental conditions to extend the equipment’s productive life. PMICs with integrated power monitoring features support other power management capabilities, such as dynamic power scaling, monitoring and control.

Conclusion

Digital factories are increasingly adopting edge computing, ML algorithms optimized for the edge (TinyML) and robust security to protect against cyber-attacks. By transitioning computational and AI capabilities closer to the data source, factories can minimize dependence on cloud-based processing to achieve lower latency and faster response times for critical operations. Edge processing requires less data to be transmitted and processed by cloud computing infrastructure, thus improving energy efficiency. 

TTI is committed to supplying reliable processors, sensors and PMICs to power this digital evolution towards improved automation, productivity and sustainability.

Sravani Bhattacharjee has worked as a tech leader at Cisco, Honeywell and other companies where she delivered many successful innovations to the market. As the principal of Irecamedia, she collaborates with Industrial IoT innovators to create compelling vision, strategy and content that drives awareness and business decisions.