Abstract

Manufacturing firms are constantly on the lookout for new ways to boost the operational efficiency of their factories.

Stringent regulations and safety measures enforced to curb the spread of COVID-19 have compelled manufacturers to reduce dependencies on human labor. Though industrial internet of things (IIoT) technologies have been instrumental in boosting manufacturing efficiency over the last few years, these still require some amount of human intervention. The technologies have enabled manufacturers to progressively automate more than 80% of factory operations. But critical and contextual decision-making requires human intelligence for preventive as well as corrective action. This restricts setups from becoming fully autonomous systems.

With connected infrastructure across assets, operations, and workforce, manufacturers can build a neural plant using the tenets of Neural ManufacturingTM. Such a plant is built using intelligent devices, artificial intelligence (AI), and machine learning (ML). This paper outlines how neural plants can be equipped with self-diagnostic and self-healing capabilities to redefine their operations not only to make them autonomous but also enable them to be resilient, adaptable, and sustainable.

Evolution of manufacturing plants

Over the years, manufacturing plants have evolved from using paper-based systems (with siloed functions) to completely digitalized and fully automated setups, as shown in Figure 1.

Developments in digital technologies, robotics, and automation have reduced human intervention in routine and repetitive tasks. The systems still need human involvement in converting data—accumulated from various connected assets, operations, and workforce—into information. These are largely reactive in nature, and corrective action depends on the ability and availability of the human workforce.

However, armed with real-time information, such as customer demand, production capacity, operational performance, and product quality, next-generation manufacturing plants can assist in operations and asset management with self-learning capabilities. These lay the foundation for neural plants, the future of manufacturing, in which factories are enabled with contextual intelligence and, eventually, autonomous operations.

Mimicking human intelligence in machines

Neural plants draw inspiration from the human nervous system in the way they respond to external stimuli.

By sensing, perceiving, and acting on signals, they self-heal or adapt to shocks. All these aspects of the neural plant are based on the tenets of Neural Manufacturing. In these futuristic plants, the physical and digital worlds and the workforce are interconnected through wearable and handheld devices. The interaction between these elements generates data, which is stored as knowledge. The historical learning is processed by edge computing devices to exhibit intelligence, which is relayed to the executional systems, such as robots, automated guided vehicles, and drones in the form of decisions for the desired actions. Neural plants are built on three pillars, as shown in the Figure 2:

1. Central command center: Equipped with AI and cloud computing capabilities, the central command center is the brain of the plant. It analyzes interactions among the various internal functions and external events to derive actionable insights based on historical learning.
2. Peripheral systems: These perform various functions including planning, internal logistics, production, quality control, and maintenance. Equipped with AI and edge computing capabilities, these systems analyze situations and make critical decisions for the functionality they represent.
3. Technological structure: This lays the foundation to achieve the desired neural capabilities. It is built on the 5G network architecture for seamless connectivity across the plant and its external ecosystems. AI and ML platforms enable continuous learning and autonomous decision making. Intelligent devices such as robots, cobots, and cyber-physical systems execute tasks sent by the AI and ML systems, and the blockchain platform ensures security and transparency in the chain of operations. A web of these technologies supported by smart sensors and cameras spread across the plant, helps in replicating a biological system to continuously perform the sense-perceive-act cycles.