Industry 4.0 and smart manufacturing technologies that drive today’s smart factories

Last updated on July 26th, 2023

The fourth industrial revolution, Industry 4.0, is changing everything as we know it, from how we learn, earn, and play to how we design, engineer, and manufacture the things we need. Today’s manufacturers are leveraging smart Industry 4.0 technologies, such as artificial intelligence (AI) and machine learning (ML), the Industrial Internet of Things (IIoT), cloud and edge computing, virtual reality (VR) and augmented reality (AR), human-machine collaborations, and more. These advanced technologies are helping manufacturers move faster, waste less, and create better products, all while gaining actionable insights to make continuous business improvements.

Noteable Industry 4.0 technologies

As the fourth industrial revolution progresses, smart manufacturing technologies are becoming more and more powerful and competent. These technologies are here to stay and are actively being leveraged by companies that wish to remain competitive in the global marketplace. Some of them include the following:

IIoT and smart sensors

The Industrial Internet of Things refers to machines and equipment that can be connected to the Internet, the cloud, and to each other. This connectivity facilitates data sharing. Smart sensors – which are an example of edge computing or basic computation at source – are used to collect and process data that can be analyzed for valuable insights about operations, productivity, equipment status, product quality, and even product safety. The sensors in connected vehicles, for example, can help identify potential part failures to drive better product quality, as well as improved safety on the road.

ML/AI and predictive analytics

The best ML/AI solutions compile mountains of data that manufacturers collect from sensors, equipment, business units, and third parties to ingest it, analyze it, and provide data-driven intelligence. This includes predictions that anticipate production issues, address problems before they occur, and improve manufacturing and product quality. In fact, ML/AI models can predict anomalies and discover correlations and patterns in the data in ways most humans simply couldn’t.

Digital twins

The data can also be used to create virtual models, AKA digital twins, that provide visual representations of processes and products in real time. Because they are based on advanced machine learning algorithms, digital twins are predictive and can detect anomalies and suggest fixes to avoid them in future.

Human-machine collaborations

A productive smart factory includes automation, as well as human-machine collaboration where people co-work with machines rather than using them to complete a task. Each brings their particular strengths to the collaboration. For example, machines can provide physical strength, such as a robotic exoskeleton that literally helps with heavy lifting. Machines can also handle the dangerous or repetitive parts of a task, while humans can provide dexterity and creative problem-solving skills.

Virtual reality and augmented reality

While AR adds elements to a live view, like in Pokémon Go, VR provides a completely immersive experience. PwC expects that by 2030 VR and AR used in product development will add $537 billion to the US GDP. The most common application of VR and AR in manufacturing is in product design and development (38%). This is followed by safety and skills training (28%); equipment maintenance, repair, and operations (19%); virtual assembly and process design improvements (17.3%); and others (respondents could choose multiple applications).

Challenges to smart manufacturing

Two major challenges that smart manufacturers face today are issues with machine interoperability and a lack of technical standards for sensor data. Smart manufacturing relies on machines actively sharing data and effectively communicating with each other. In the US, the National Institute of Standards and Technology (NIST) is looking to develop and promote standards for broad adoption, but the process is ongoing.

The upfront cost of equipping a smart factory can also be a barrier to implementation, but long-term cost savings, efficiency gains, and product improvements can offset the initial outlay and help manufacturers quickly recover their investment.

Smart manufacturing and your business: ready to get started?

The implementation of any new technology must always come back to what’s best for your business. When considering the adoption of Industry 4.0 technologies, manufacturers mustn’t lose sight of the end goal – creating better products. Many companies believe that optimizing production will equate directly to optimized parts, but that isn’t the case. Precision manufacturers need a system that is dedicated to improving part quality if they want to build better parts.

Our LinePulse solution helps you build better parts more efficiently by applying machine learning and artificial intelligence to your product and production data. LinePulse aggregates and analyzes all relevant data from all of your sources to proactively deliver intelligence that uncovers defects, provides root cause analysis, and improves first time yield. 

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