The AI Transformation in Additive Manufacturing


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In our previous article, “Amplifying Additive Manufacturing with Artificial Intelligence,” we delved into the synergy between artificial intelligence (AI) and additive manufacturing (AM). We highlighted AI’s role in driving design innovation, enhancing production efficiency, ensuring quality control, and enabling mass customization in 3D printing. Building upon that piece, this article aims to extend our understanding of the transformative journey of AI in AM, exploring its potential and critical questions.

Current Trends in Generative AI

Generative AI, known for its ability to create new content and make informed decisions based on data patterns, has made significant strides across various industries. According to a report by McKinsey & Company (2023), generative AI could add between $2.6 trillion to $4.4 trillion annually to the global economy, signifying a paradigm shift in industrial operations.

Generative AI is bringing about a revolution in research and development (R&D) across various industries such as life sciences, chemical, software engineering, and product R&D. In the biotech pharmaceutical industry, companies such as Entos are using generative AI with automated synthetic development tools to design small-molecule therapeutics. AI integration in software engineering has significantly improved productivity, with Microsoft’s GitHub Copilot helping developers complete tasks 56% faster. In product R&D, AI can optimize virtual design and simulations, leading to more efficient physical test planning and reducing the time for physical build and testing. In customer service operations, generative AI has been found to increase issue resolution by 14% an hour and reduce the time spent handling an issue by 9%.

Work structure is also transforming. Generative AI is expected to automate 60 to 70 percent of tasks that consume a significant portion of employees’ time, particularly those requiring natural language understanding. This shift is more pronounced in knowledge-intensive roles, impacting higher-wage and educational sectors.

Labor productivity is boosted. The full realization of this potential hinges on the rate of technology adoption and the effective redeployment of workforce activities. Combining generative AI with other technologies could further amplify productivity growth.

“As a result of generative AI, experts assess that technology could achieve human level performance in some technical capabilities sooner than previously thought.”

AI in Additive Manufacturing: A Fundamental Shift

From Solving Problems to Redefining Approaches

Just a few years ago, proposing to solve a manufacturing problem with AI seemed innovative. Now, the narrative has shifted: employing AI is the primary direction for problem-solving in AM. Traditional companies have been using AI primarily for two purposes: discovering unknown problems and increasing efficiency. This approach has been centered around making existing products better, faster, and cheaper through AI. However, AI has often been a feature addition to existing products rather than the core of AI-only products.

As the frontier of AI continues to expand at an unprecedented pace, it becomes increasingly evident that a fundamental shift is required in how AI is applied within the 3D printing landscape. This necessity arises from the evolving capabilities of AI, which are rapidly approaching human-level performance. However, AI integration within AM lags behind other industries, such as finance, pharmaceuticals, education, and high-tech. AI, with its ability to learn, adapt, and make decisions, has the potential to revolutionize 3D printing. From intricate design creation to optimizing production processes, AI’s advanced cognitive capabilities can lead to groundbreaking advancements in manufacturing.

The Imperative for Startups: Beyond Efficiency

As we move forward, startups in the AM sector may face what can be perceived as challenges or opportunities: they need to go beyond merely making things better, faster, and cheaper. They need to explore how AI can be a tool not just for enhancing production efficiency but for driving genuine innovation. What approaches can harness AI to create value beyond the conventional metrics of production? How can startups define problems that are solvable only by AI?

Image Courtesy: Ai Build

Addressing the Data Challenge in AM

Data is a crucial component for AI’s advancement, but data accumulation and management remain sporadic in the 3D printing field. Competing with long-established conventional production technologies requires a significant accumulation of data. The strategy might involve extracting insights from minimal data and using those insights to reduce the need for extensive historical data. This leads to a pivotal question: how can we accelerate the accumulation of data in AM? One possible solution is collaboration among various 3D printing companies. What breakthroughs might we witness if these companies could build mutual trust and share data? 

Collaborative Data Sharing as a Possible Solution

The concept of building a network of trust and sharing open data to enable companies to exchange insights and experiences can be a valuable solution to overcome data challenges. Imagine a scenario where AM companies across the globe share their print success and failure data, material properties information, machine parameters, and design optimization strategies. This shared data pool would be a valuable resource for AI algorithms, enabling them to learn and improve at an unprecedented pace. 

The advantages of this collaborative approach could include rapid learning and innovation, enhanced predictive models, optimized material use, and cross-industry applications. With access to a broader range of data, AI algorithms could accelerate the learning curve and advance the capabilities of 3D printing technologies more quickly. With more comprehensive data, predictive models in AM could become more accurate, resulting in fewer print failures and higher quality outputs. Shared data could lead to better understanding and optimization of materials used in 3D printing, reducing waste and costs. Insights gained from one sector of AM could be applied to others, fostering innovation across various applications of 3D printing.

Forward-Thinking Questions for 2024

As we conclude, let’s review some pivotal questions that will shape the future of AI in AM:

  1. Defining AI-Only Problems: If we were to identify problems in 3D printing that can only be solved by AI, how would they differ from currently addressed problems?
  2. Collaboration for Data Sharing: How can AM companies overcome competitive barriers to share data effectively? What models of collaboration could be both secure and beneficial?
  3. Beyond Efficiency: How can AI contribute to 3D printing beyond the topics of efficiency, speed, and cost reduction? Are there unexplored territories where AI can uniquely enhance AM?
  4. Data Utilization Strategy: Given the current limitations in data availability in 3D printing, what innovative strategies can be employed to maximize the value of existing datasets?
  5. Innovation in Startups: How can startups in the AM field leverage AI to not only enhance existing manufacturing processes but also create disruptive technologies or methodologies? What kind of support or ecosystem is required to nurture such innovation?


The road ahead for AI in AM is not just about optimizing what already exists but about exploring uncharted territories, redefining manufacturing paradigms, and unlocking unprecedented levels of customization, efficiency, and innovation. As we venture further into this journey, the integration of AI into 3D printing must transcend traditional boundaries, fostering a culture of creativity, collaboration, and sustainable development. The ultimate goal is to establish a manufacturing ecosystem that is not only efficient and productive but also adaptive, responsive, and responsible. By harnessing the full potential of AI, the AM industry can pave the way for a future where manufacturing is not only about making things but about creating smarter, more personalized, and more sustainable solutions that align with society’s evolving needs and values. 

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