Defining Key Concepts of Smart Manufacturing

Smart manufacturing encompasses multiple concepts, including:

• Industry 4.0: integrates digital technologies into manufacturing
• Industrial Internet of Things (IIoT): a collection of sensors, actuators, software and other devices used to improve manufacturing and industrial processes
• Digital twins: a collection of data that serves as the digital counterpart of a physical system for simulation, integration, testing, monitoring, maintenance and recycling
• Machine learning: algorithms and software that enable the prediction of process outcomes without explicitly being programmed
• Overall Equipment Effectiveness (OEE): a metric that identifies the percentage of productive manufacturing time.

The concepts are related as shown in Figure. 1. We see that the IIoT supports Industry 4.0 which, in turn, helps to enable smart manufacturing. Similarly, digital twins and machine learning support smart manufacturing. Finally, the capabilities offered by smart manufacturing serve to increase OEE in the modern factory.

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Smart manufacturing uses real-time data from Internet-connected sensors and machines located across the supply chain to monitor, automatically adapt and improve manufacturing processes in smart factories for increased productivity, decreased cost, increase flexibility, greater operational efficiency and reduced energy use. It also provides more flexible technical workforce training to improve product design, supply chain, maintenance, distribution and sales.

Data-dependent modeling and analytics, including AI and machine learning, are used to improve processes and may be performed in the cloud or at the edge. Common challenges for smart manufacturing implementation are cost, equipment/device compatibility, cybersecurity and lack of workforce training.

The National Institute of Standards and Technology (NIST) defines smart manufacturing systems as “fully-integrated, collaborative manufacturing systems that respond in real-time to meet changing demands and conditions in the factory, in the supply network, and in customer needs.”

Let’s define some terms that are used in smart manufacturing.

• Edge computing: distributed data storage that is physically near the location where it is needed
• Cloud computing: centralized data storage where information technology services and resources are uploaded to and retrieved from the Internet as opposed to a direct connection to a local server
• Information technology (IT): computer systems, software, programming languages and the processing, storage and distribution of data and information.

There are several key technologies within smart manufacturing, including:

• CNC machining: material removal by milling, turning, drilling and other operations from CAD digital part descriptions and tool paths from CAM software
• Automation/robotics: enable repeatable performance and data gathering for tasks that have been previously completed manually; release humans to perform thinking tasks
• AM/hybrid manufacturing: AM can supplement or replace traditional manufacturing; hybrid manufacturing combines metal AM with machining (and other processes) to reduce material waste and produce designs that may not be possible by AM or machining alone
• Digital twin: digital model of an intended or actual real-world physical product, system or process
• Design for manufacturing (DFM): DFM or design for manufacturing and assembly (DFMA) is a design methodology that enables and optimizes prefabrication through a set of design choices and principles; products and components are designed specifically to make manufacturing processes easier and more cost-effective
• Big data analysis: analysis of large data sets using cloud storage and processing; can assist with process improvement, logistics, risk assessment, cost structures, growth strategies, quality control, build-to-order and other sales patterns, as well as after-sales services
• AI/machine learning: using AI, intelligent machines are created that work and react like humans; machine learning is a subset of AI, allowing software to predict outcomes without explicitly being programmed
• Augmented reality/virtual reality (AR/VR): assists with training by enabling an employee to receive instructions from a remote expert who sees an activity through the employee’s eyes

Smart manufacturing is implemented in smart factories, where (ideally) activities are tracked in real time, machines talk to one another, machines are repaired before they malfunction, production lines can be rapidly altered and customized and energy consumption is optimized. However, there are implementation challenges for smart factories that must be addressed, such as:

• we must not only collect the data but also use the data to learn about processes and enable improved decision making
• we must install the appropriate sensors at preferred spatial locations on equipment, which requires domain expertise
• the cost of sensors, storage, computing, network and analysis may take time to provide a return on investment
• the data collection infrastructure must be compatible with existing and new equipment to provide machine-to-machine (M2M) communication
• cyber secure and reliable network connectivity is required.

Smart manufacturing and Industry 4.0 are sometimes used interchangeably. Industry 4.0 captures the rapid technological advancements of the 21^st century and enables smart manufacturing. Its key elements are:

• machines with sensors that upload continuous data streams to the cloud for analysis (that is IIoT)
• robotics and automation
• advanced human-machine interfaces
• cyber-physical systems
• AI, machine learning and data analytics.

Within the smart factory, Industry 4.0 is relevant to:

• design – innovative new products and technologies
• prototyping – AM, CNC machining and other processes
• production – automating production lines
• delivery and tracking – on-time delivery with shipment tracking
• assistance – customer support during distribution and after purchase.

Industry 5.0 is a newer concept that extends Industry 4.0 through a focus on collaboration between humans and machines. Its goal is to empower people to fully use their skills and make work safer, more efficient and more meaningful. It aims beyond efficiency and productivity to consider the role and the contribution of industry to society, while emphasizing worker well-being. It applies new technologies to provide prosperity beyond jobs and growth and respects the production limits of the planet.