Generative AI and Additive Manufacturing: The Journey Toward Industrial Metaverse

The advantages of additive manufacturing (AM) go beyond the characteristics of physical goods and the production processes they make possible. Having experience with additive manufacturing (AM) can enhance the approach to design and provide more chances to shift into unfamiliar design tasks.

Undoubtedly, it is a well-established truth that the manufacturing industry has consistently been at the forefront of innovation, always adapting to fulfill the requirements of modern production. Nevertheless, the emergence of artificial intelligence (AI) drives this industry towards unparalleled levels of effectiveness and capacity.

First, we must understand what this means and how it changes the industrial metaverse. Another fascinating area of AI that is changing the manufacturing sector is generative AI. This technology uses machine learning algorithms to generate novel designs and enhance current ones, empowering manufacturers to fabricate increasingly ingenious functional products. According to Deloitte?projections, the AI market in the manufacturing industry is expected to reach $10.07 billion by 2027. These technologies are crucial components of the immersive 3D environments that constitute the industrial metaverse.

Understanding Generative AI and Its Role in Additive Manufacturing

Additive manufacturing, also known as 3D printing, is an area where generative AI is making a significant impact. Generative AI can streamline additive manufacturing by automatically creating optimal part designs and print parameters that align with required performance characteristics and material properties. This not only saves time and cost but also enhances the quality and performance of the parts. Utilizing AI in this manner is a revolutionary development for manufacturers seeking to use the advantages of additive manufacturing in their operations.

Figure 2: Performance-optimizing product designs emerging from generative design (GD). Top panel: New Balance's Midsoles effort, custom to user weight distribution, with options provided via GD [5]

Let us examine how generative AI enhances several aspects of various manufacturing industries.

• An aerospace manufacturer utilized generative AI

The aerospace sector has started?using generative AI?to improve the additive manufacturing process for airplane components. The designs developed by the model?decreased the amount of material used while still ensuring structural strength, resulting in a 20% decrease in material expenses and a 30% reduction in the time required for production. The aerospace parts demonstrated a 25% enhancement in performance, resulting in increased fuel efficiency and longevity of the aircraft.? The utilization of AI-powered robotics and automation is causing a significant transformation in various systems, particularly in the manufacturing sector, leading to a revolutionary change in how manufacturers conduct their operations.?

• Virtual Testing and Digital Twin in Additive Manufacturing

Figure 3: Siemens says aerospace companies using digital twinning/threading are achieving improved first pass yields of up to 75 percent for engineering designs, resulting in fewer design revisions. They are also able to reduce physical test programs by up to 25 % by using virtual testing. Siemens image 4

As an illustration, a prominent aerospace manufacturer utilized AI-driven digital replicas to model and enhance the design of a novel aircraft engine. Through the execution of several virtual tests and the utilization of artificial intelligence to analyze the outcomes, the researchers were able to ascertain the most advantageous design parameters and achieve a 50% reduction in development time.

• Process Parameter Optimization

Predictive analytics can also help manufacturers optimize process parameters. By analyzing historical process data and identifying the optimal settings for each stage of production, AI algorithms can help manufacturers fine-tune their processes for maximum efficiency and quality.

A leading chemical manufacturer used predictive analytics to optimize their batch production process, resulting in a 15% increase in yield and a 20% reduction in energy consumption. By leveraging the power of data and AI, they were able to streamline their operations and achieve significant cost savings.

General Applications of AI in Additive Manufacturing

Application of artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), in optimizing additive manufacturing (AM) processes.

• Several artificial intelligence models are employed for activities such as enhancing processes, identifying defects, optimizing materials, and determining part orientation in industries such as aerospace and healthcare. The research emphasizes the capacity of AI methodology to improve the efficiency, quality, and cost-effectiveness of AM technologies. Prominent AI techniques in this regard include Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM) models.
• ?Tod W. Parrella, NX Product Management for Siemens Digital Industries Software, explains the difference: “Topology optimization optimizes the product’s form to satisfy system requirements such as strength, weight, cooling, or flow.

Generative design involves the examination of various design ideas and possibilities on a larger scale, while also considering different aims and interdisciplinary requirements. Hence, engineers can utilize generative design to assess and contemplate numerous design options, as opposed to reviewing only one design conclusion through topology optimization.

Figure 5: Generative design. Excellent strength-to-weight ratio [6]

• US Steel is utilizing Google Cloud's generative artificial intelligence technology to develop applications that enhance operational efficiency and enhance employee experiences at the largest iron ore mine in North America. "Our collaboration with Google Cloud has significantly expedited the process of digitization at U. S. Steel." "We anticipate numerous advantages, such as accelerated repair durations, reduced periods of inactivity, and enhanced job satisfaction for our technicians, through the implementation of generative AI," said David Burritt, the president and CEO of U.S. Steel.

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References:

1.???? 2024 manufacturing industry outlook | Deloitte Insights

2.???? Optimization with artificial intelligence in additive manufacturing: a systematic review | Journal of the Brazilian Society of Mechanical Sciences and Engineering (springer.com)

3.???? 16 AI Use Cases Transforming Manufacturing in 2024 - Markovate

4.???? DIGITAL TWINNING: THE LATEST ON VIRTUAL MODELS - Aerospace Tech Review

5.???? The role of generative design and additive manufacturing capabilities in developing human–AI symbiosis: Evidence from multiple case studies - Bendoly - Decision Sciences - Wiley Online Library

6.???? Is generative design changing additive manufacturing? - 3Dnatives

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