Embracing the Digital Future of Lacemaking
The intricate historic interweaving of textile manufacturing and digital technologies opens up unlimited transformational opportunities at the dawn of the new era of artificial intelligence (AI). While fashion and textile industries already leverage AI-powered tools for real-to-virtual transformation of products and processes, we make the case that AI can and should play a key role in enhancing virtual-to-real product transformation via generative design of textiles for manufacture.
Lacemaking, with its roots in weaving, embroidery, and knitting, presents a unique challenge and opportunity for AI-enabled design innovation. Historically, lace has been an indicator of wealth, class, and decorative flair, but the craft has fallen out of popularity in modern fashion. Lacemaking was once a valuable source of income for skilled artisans, often women, who advanced the craft but remained uncredited for their artistry and engineering skills. The decline of handmade lacemaking and the rise of mass-produced chemical lace devalued the intricate craftsmanship and cultural significance of traditional techniques.
In this article, we explore how generative AI can bridge the gap between historical lacemaking craftsmanship and contemporary technology, creating a sustainable model for the preservation and evolution of textile heritage. By leveraging AI to generate new lace patterns optimized for aesthetic appeal, cultural relevance, and advanced mechanical properties, we can revive lacemaking as a living, evolving art form that adapts to the needs and aesthetics of the present.
Lace: From Weaving to Computing
The digital revolution can be traced back to innovations in textile weaving, which used both binary and nonbinary code for information storage and exchange long before computers were invented. The knotted-string khipu structures used by the Incas and other Andean peoples are believed to be complex enough to encode linguistic information, while the punch card of the Jacquard loom worked on a binary-like system to create complex textile patterns.
Even the first nonvolatile computer memory comprised tiny donuts of ferrite material strung on wires and handwoven by textile workers into fabric-like patterns, known as “core rope memory.” This interconnection between textiles and computing is not lost on modern AI language models, which tend to overuse textile-related words like “weaving” and “tapestry” to convey a sense of complexity, detail, and careful construction.
The combination of the language of words and textile patterns can become a powerful communication tool, as exemplified by the late Justice Ruth Bader Ginsburg’s use of ornate lace collars to punctuate her opinions and dissents on the bench. Just like languages, traditional textile crafts like lacemaking offer a tremendous opportunity for AI-enabled generation of new knowledge, thus closing the loop between knowledge generation, storage, and textile engineering.
Generative AI for Lacemaking: Preserving Heritage and Enabling Innovation
While fashion brands are already using AI to innovate their businesses, the role of AI in today’s textile industries is largely limited to digital design and production quality control. The biggest impacts of the AI revolution in textiles have been in the digital domain, including supply chain management, virtual marketing, and heritage data preservation.
We believe that AI can and should play a key role in enhancing “virtual-to-real” product transformation via the generative design of textiles for manufacture. AI capabilities in this field remain largely untapped and need to be developed to engineer enhanced mechanical properties, transform textile manufacturing processes, and enable smart textiles applications.
Our proposed generative AI-enabled pipeline to design textiles for manufacture integrates historical pattern collection studies, mathematical modeling, mechanical characterization, computer vision deep learning, and lacemaking knowledge. We focus on bobbin lace as an intricate, challenging example of an endangered handicraft important to textile heritage.
Lacemaking draws from elements of weaving, embroidering/sewing, and knitting, adding the challenge of holes and negative space in the fabric to form intricate patterns. Relative to more conventional woven or knitted textiles, the open net structure of lace textiles provides additional degrees of freedom in tensile properties engineering, which can be leveraged for modern applications in wearable, medical, industrial, and geo textiles.
AI-generated lace patterns can be optimized for aesthetic appeal, cultural relevance, elasticity, tensile strength, Poisson ratio, and other mechanical characteristics, as well as for the integration of conductive threads and electronic components. By bridging the gap between historical craftsmanship and contemporary technology, generative AI can help revive the lacemaking craft not merely as a historical relic but as a living, evolving art form that adapts to the needs and aesthetics of the present.
Lacemaking: A Rich Cultural Heritage and Craft
Traditionally, lace has been a culturally significant commodity, used by European nobility in the seventeenth and eighteenth centuries to display social status, wealth, and fashion trends. Lacemaking was typically practiced by women and children, providing means to contribute to their household finances or a dowry, make money of their own, and have a skilled trade that they could take pride in.
The industrial revolution brought machinery to the world of lacemaking and changed the craft. While before, lacemaking was primarily done in houses by groups of women, it now moved to factories, leading to an increase in productivity at the expense of shuttering down small-scale handmade production. Nevertheless, nostalgia and longing for the handmade craftsmanship of previous times helped to preserve the practice of traditional handmade lacemaking.
Having originated and flourished in Europe, lacemaking craft was inspired by intricate patterns found in Middle Eastern and Asian woven textiles. Designing and preserving lacemaking patterns has always held deep value and importance, from the noble women of 17th century Venice, Italy, boasting books containing needlepoint lace patterns from all over Europe, to 21st century women in Central Slovakia taking great pride in and marketing their pattern collections.
Further evolution and fusion of lacemaking styles were shaped by immigrants from different parts of Europe moving to new countries, bringing their lacemaking practices with them and ultimately forming new styles as they mixed their craft with those of the new communities. Unfortunately, some unique lacemaking techniques and artforms, such as Spanier Arbeit, a metal wire-based bobbin lace exclusive to Ashkenazi Jewish production, have been decimated by tragedies like the Holocaust.
Even within actively-practicing communities, pattern heritage preservation is not consistently maintained. The older and younger generations within the Central Slovakian lacemaking community have diverging views on lacemaking instruction and pattern preservation, with the older generation often not keeping or even burning patterns they do not like, a practice that is viewed as irresponsible by younger craftsmen who wish to preserve the cultural knowledge embedded in the patterns.
Generative AI for Lacemaking: Bridging the Gap
For an AI model to produce new lacemaking patterns, it must train on a large database of existing historical patterns. Given the age of the lacemaking craft, copyright and intellectual property issues should not be a barrier to accessing and using training data for generative AI models. Training on historical lacemaking books in the public domain will enable the generation of new digital design-for-manufacture tools while benefiting the craft by preserving lace patterns from being lost to time with aging older generations and declining interest.
While generative AI will inevitably impact industries and workforce development, it is also important to consider what artisans and cultural heritage may be displaced by new technology or, to the contrary, provided with new tools to preserve and elevate the lacemaking craft. Like many industries, the fashion industry in general seeks to reap the benefits of AI technology, using big data to train generative models to create relevant and practical designs.
Historically, interactive genetic algorithms (IGAs) have been adopted to inform the computer-aided design of fashion and evolve designs based on previous ones. While these methods are still limited, they have been joined by more advanced deep learning techniques, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models, which have already had an impact in generating new fashion designs.
Deep learning has also aided in textile visualization using image-to-image transfer techniques, such as neural style transfer, which can translate the semantic content of one domain to another. For instance, user-uploaded images can be transformed into embroidered stitches, or a garment’s pattern in one image can be transformed into the shape of another.
Recently, StyleGAN was developed with the goal of improving GAN to generate highly realistic images. StyleGAN introduces an intermediate latent space that controls “styles” of the generated image during the image generation process, such as textile texture, pattern design, and gradient. This allows the model to have precise control over various aspects of the image, which the model adjusts as it learns to produce the most realistic image possible.
Towards Manufacturable AI-Generated Textiles
Despite advancements in generative AI for textile patterns, the current phase of AI-generated content typically takes the form of pieced-together images that are not yet manufacturable. We define manufacturability as the ability of an AI model to generate a set of instructions sufficient to produce a textile from a generated pattern.
For AI-generated textiles, the challenge lies not only in creating a generative AI model capable of producing patterns on demand but also in ensuring patterns are complete, physically possible to be made, and encoded to be made by hand or machine. Accordingly, we identify three critical stages in the AI-enabled textile design-for-manufacture process: attribute-specific pattern generation, process-specific instructions encoding, and physical fabrication.
Knitting Encoding Techniques
Modern knitting is a fully computerized textile construction technique, which creates patterns from interlacing yarn loops comprising various stitch types (e.g., knit, purl, tuck, flow, etc.) used as pixels. Punch cards, originally created for the Jacquard loom, have provided a way of encoding lace in a binary way while retaining the ability to make intricate designs.
Recent innovations in machine knitting, such as whole garment knitting, assembly of knit primitives, and pipelines to generate new patterns from pre-existing ones, have simplified the process, but do not yet generate new patterns. Human-understandable knit instructions have been produced by models like SkyKnit, trained by crowdsourced knit data and verified by the knit community. Other models, like DeepKnit, can generate machine-understandable instructions, treating knit data similarly to formal language modeling and generating syntactically correct instructions.
Bobbin Lace Encoding Techniques
Bobbin lace is made by braiding and twisting filaments or yarns, which are wound on multiple bobbins. Simple movements of the bobbins (e.g., twists and crosses) create stitches according to a predefined pattern. Manual bobbin lace technique uses patterns drawn on paper or parchment and pinned to a lace pillow, where the placement of the pins determines the pathway for the lace stitches.
The bobbin lace technique has been mechanized and digitized for mass production by the invention of the Leavers loom in 1813, which produces lace by intertwining two sets of threads: the warp and beam threads actuated by the Jacquard mechanism, and the bobbin threads that move in a pendulum-like motion. The patterns created by the loom can be digitized by using a binary code with punch cards or computer codes.
An alternative lace encoding approach has been recently proposed to represent these patterns as graphs, which allows effectively integrating bobbin lace patterns as quantifiable data representations. Bobbin lace patterns can be represented as simple graphs known as “grounds,” in which nodes represent an encoding of actions (twist, cross, etc.) to be done at said node, and edges represent topological threads between each lace. This graph representation is important in generative AI, as it allows models to understand and learn the intricate and complex lace patterns as capturable structures and relationships.
3D Printed and Embroidered Lace Patterns
The adaptation of three-dimensional (3D) printing techniques for lace manufacture faces a different challenge. While a sophisticated and flexible system of coding 3D patterns is well developed and standardized, reaching the same level of material flexibility, aesthetics, and production rate as those achieved with knitted or bobbin lace remains a challenge. On the other hand, different additive manufacturing techniques, including selective laser sintering, fused deposition modeling, and two-photon lithography, make use of well-developed software tools such as Rhinoceros and Autodesk 123D for 3D computer-aided design (CAD) modeling.
Another approach to creating 3D lace structures is through digital embroidery. In this process, repeated patterns produced by a computer-controlled embroidery machine in layers can build up new interlocking mechanisms. While each layer has its own mechanical properties, the construction creates joint spots that interlock between the layers for the creation of 3D lace when the dissolvable backing fabric is removed.
Designing for Mechanical Properties
While aesthetic properties of lace structures may be driving their consumer appeal, it is the mechanical properties that play a critical role in determining the suitability of different patterns for various applications. Key attributes include tensile strength, elasticity, dimensional stability, fineness, and texture. Generative AI is expected to expand the possibilities of incorporating these attributes and nontraditional materials in lacemaking practice to both elevate the craft to be of research significance and bring functional and aesthetic value to modern textiles.
Tensile properties of different patterns should be evaluated and included in the model training protocol. Along with the pattern ultimate strength, other important attributes include Young’s modulus and the characteristic transition strain, as well as porosity, defined by the interplay between solid threads and open spaces. High porosity in lace can enhance its visual appeal by creating a more intricate and delicate design, allowing light and air to pass through more freely, while also influencing the fabric’s weight, flexibility, and moisture transport.
Integrating Property Labeling into Generative AI
To generate patterns with desired spatial stability, the recorded images of tested lace samples can be processed to calculate the local stitch displacement field as an additional attribute to be included in the AI model training. Incorporating different materials, such as high-conductivity infrared-transparent polyethylene (PE) fibers or conductive threads, can also extend the functionality of lace textiles, which can be accounted for by additional attributes used for the AI model training.
Computer vision can help inform generative models that generate visually compelling designs through detail and feature extraction, deriving strain from deformation for granular textile structure characterization. Through analyzing detailed attributes derived from high-resolution lace images taken under varying strain conditions, computer vision systems can generate quantifiable data like strain heat maps, which can then be encoded in lace structures (nodes, edges) to influence fitness functions in interactive genetic algorithm (IGA) models.
By incorporating property labels into the process of generating new lace designs, we can iteratively optimize the tensile properties of geometric designs produced by generative AI, expanding the possibilities for modern textile applications. This integration of material science, engineering, and design principles into a multidisciplinary framework can enhance the manufacturability and utility of generated lace patterns.
Conclusion: Embracing the Future of Textile Craftsmanship
The integration of generative AI in the design and manufacture of textiles, particularly lacemaking, presents a unique opportunity to preserve cultural heritage while driving innovation. By bridging the gap between historical craftsmanship and contemporary technology, we can revive the lacemaking craft as a living, evolving art form that adapts to the needs and aesthetics of the present.
Generative AI can assist in pattern generation, mechanical property optimization, and manufacturing instructions, while still retaining the human touch and personal connection that are central to effective teaching and the lacemaking experience. As educators, we have a responsibility to prepare our students for a future where AI is deeply integrated into various industries, including textiles. By embracing these tools and demonstrating their potential, we can empower the next generation to navigate and shape an ever-evolving digital landscape.
The journey of integrating generative AI in textile engineering is not without its challenges, from institutional resistance to ethical concerns. However, by starting small, aligning AI tools with our teaching goals, and encouraging ethical use, we can navigate this transformation and redefine the educational landscape. As we move forward, let us embrace the power of technology to enhance, not replace, the timeless art of textile craftsmanship, blending tradition and innovation to create a vibrant and sustainable future for the textile industry.
