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What is Industry 4.0: Understanding the impact of AI in Manufacturing

In recent years, we’ve heard of major corporations leveraging AI to transform their operations. At that time, AI seemed like a distant dream, requiring vast resources in terms of people, materials, and money. However, with the recent advancements, it’s becoming evident that AI could soon be accessible to manufacturing companies of all sizes.

What possibilities does AI bring to the table when paired with Digital Transformation (DX) to revolutionize our work methods?

Personally, it seems that the term “Industry 4.0” has faded from everyday conversation. Yet, globally, even without explicitly using the term “DX,” there is a concerted push towards rapid digitalization to change how we work. So, how is AI poised to alter the landscape of development, design, and manufacturing?

We will explore the current state and future potential of AI within development, design, and manufacturing environments.

What is Industry 4.0

Industry 4.0 is a term coined by the German government in 2011, aiming to automate and digitalize the manufacturing sector through advanced technologies. Central to this concept is the digital twin technology, which connects physical objects with their virtual counterparts via IoT and cloud computing. This initiative marks what is often referred to as the Fourth Industrial Revolution.

AI in Industry 4.0: Transforming Manufacturing

Before the surge of the Digital Transformation (DX) wave, the concept of Industry 4.0 was already making waves. Industry 4.0, with its emphasis on the Internet of Things (IoT), involves collecting data through various sensors, processing that data on cloud computers, and leveraging Artificial Intelligence (AI) to analyze it. This integration has increasingly found applications within the manufacturing industry.

One notable example comes from a 2017 presentation at SolidWorks Japan’s annual event. The featured image was of a fire detector with weather monitoring capabilities. Beyond merely using 3D CAD and various analysis methods for design verification, the sensor data from this product was linked with the IoT platform Xively (now part of Google Cloud) to provide big data analysis via 3DEXPERIENCE NETVIBES. This allowed for the delivery of fire and weather information to users directly from the product design stage, embodying the concept of a digital twin.

The V-Process in Development: How AI Can Improve it

Are you familiar with the V-process? In development and design, projects often follow this V-shaped model. The V-process consists of two main stages: the design process and the verification process. The design phase progresses from system design to unit design and finally to component design. Once the design phase is complete, prototypes are created, and the verification phase begins to ensure product quality.

Traditionally, each round of verification often leads to redesigns and additional prototyping, increasing both development costs and timelines. However, a new V-process approach aims to mitigate these setbacks by enhancing digital models through repeated simulations, thereby reducing the need for physical prototypes and extensive verification. This innovative approach is exemplified by Model-Based Design (MBD), which has been a hot topic in the development and design industry for several years.

Can AI effectively improve the quality of the design process within the V-process framework? Let’s explore the potential of AI in various design tasks.

System Design

AI can assist designers by searching for similar existing designs, automating conceptual design tasks, and simulating cost estimates. It can also automatically generate conceptual specification documents.

Unit and Component Deisgn

AI can automate routine design tasks while providing support for non-standard design processes. It can aid in simulations and analysis, optimize designs through Generative Design and Bio Mimicry, and search for similar components.

Although full AI integration may not be immediate, leveraging digital data effectively in development and design can pave the way for broader AI adoption in the future.

Further Improvements with AI

AI can significantly enhance the design process of custom mechanical components by streamlining and optimizing various stages of development. One notable benefit is the application of Generative Design, where AI algorithms automatically create multiple design alternatives based on specified constraints and requirements. This allows engineers to explore a wider range of innovative solutions, which might be too time-consuming or complex to develop manually. AI can evaluate these designs for factors such as structural integrity, material usage, and cost efficiency, helping to identify the most optimal configurations quickly. Furthermore, AI-powered predictive analytics can foresee potential design flaws and performance issues by simulating real-world conditions, thus mitigating risks early in the development cycle and reducing the need for extensive physical prototyping.

AI also enhances collaboration by integrating seamlessly with various CAD software and engineering platforms, ensuring real-time data sharing and communication across different teams and departments. This interconnected approach supports a more cohesive workflow, where insights and updates are immediately available to all stakeholders, fostering a collaborative environment that enhances productivity and innovation. Moreover, AI-driven tools can automate routine and repetitive tasks such as drafting and documentation, freeing up engineers to focus on higher-value activities like creative problem-solving and strategic decision-making.

By harnessing the power of AI, the design process not only becomes more efficient and cost-effective but also results in higher quality and more innovative products. This technological advancement aligns with the principles of Industry 4.0, where smart technologies and data-driven insights drive the next wave of manufacturing excellence. Ultimately, AI in the design process of custom mechanical components exemplifies how digital transformation can unlock new potentials, leading to faster time-to-market, reduced production costs, and enhanced product performance.

Conclusion

AI can revolutionize the search for technical data and provide guidance in determining optimal design parameters. With AI support, designers can focus more on high-value tasks such as conceptual and detailed design.

MISUMI is at the forefront of the AI change. The AI developed by the MISUMI team is at the core of meviy, allowing designers and purchasers to get instant quotes for their custom components.