Artificial Intelligence Model to Suggest Sewing Operations Through Sketch Analysis

With experienced apparel technicians migrating abroad, inexperienced newcomers struggle analyzing sketches to determine optimal construction methods. Manual interpretation takes extensive time and multiplies sampling expenses from missteps. This research demonstrates large language models (LLMs) like GPT-4 can accurately suggest sewing sequences from drawings to slash costs. We developed an AI ensemble leveraging computer vision and LLMs to annotate sketches with likely production directives at over 70% precision. Compared to novice technicians' 60% accuracy, this automates breakdown drafting for rapid iterations. By training on past image-operation pairings, the model reliably recommends stitch types, tools and steps for new sketches. Confidence metrics identify when human oversight is beneficial. This intelligence augmentation assists upskilling novices while increasing development efficiency. The methodology decreased sampling costs by 30% and duration by 25% over manual approaches in trials. By expediting iterations with automated validations, it enables agile adjustments closer to market needs. The system can expand across garment categories given sufficient data. This pioneering research tackles industry migration effects through AI, breaking dependence on disappearing expertise. With proven cost and time reductions, adoption can accelerate sector-wide production efficiency.