The conceptual design phase represents a critical early stage in the product development process, where designers generate potential solutions that meet predefined design specifications based on functional requirements. Functional modeling, a foundational aspect of this phase, enables designers to reason about product functions before specific structural details are determined. A widely adopted approach to functional modeling is the Function-Behavior-Structure (FBS) framework, which supports the transformation of functional intent into behavioral and structural descriptions. However, the effectiveness of function-based design is often hindered by the lack of well-structured and comprehensive functional data. This scarcity can negatively impact early design decision-making and hinder the development of accurate behavioral models. Recent advances in Large Language Models (LLMs), such as those based on GPT architectures, offer a promising avenue to address this gap. LLMs have demonstrated significant capabilities in language understanding and natural language processing (NLP), making them suitable for automated classification tasks. This study proposes a novel LLM-based domain adaptation (DA) framework using fine-tuning for the automated classification of mechanical assembly parts' functions. By fine-tuning LLMs on domain-specific datasets, the traditionally manual and subjective process of function annotation can be improved in both accuracy and consistency. A case study demonstrates fine-tuning GPT-3.5 Turbo on data from the Oregon State Design Repository (OSDR), and evaluation on the A Big CAD (ABC) dataset shows that the domain-adapted LLM can generate high-quality functional data, enhancing the semantic representation of mechanical parts and supporting more effective design exploration in early-phase engineering.

Retrieving the similar solutions from the historical case base for new design requirements is the first step in mechanical part redesign under the context of case-based reasoning. However, the manual retrieving method has the problem of low efficiency when the case base is large. Additionally, it is difficult for simple reasoning algorithms (e.g., rule-based reasoning, decision tree) to cover all the features in complicated design solutions. In this regard, a text2shape deep retrieval model is established in order to support text description-based mechanical part shapes retrieval, where the texts are for describing the structural features of the target mechanical parts. More specifically, feature engineering is applied to identify the key structural features of the target mechanical parts. Based on the identified key structural features, a training set of 1000 samples was constructed, where each sample consisted of a paragraph of text description of a group of structural features and the corresponding 3D shape of the structural features. RNN and 3D CNN algorithms were customized to build the text2shape deep retrieval model. Orthogonal experiments were used for modeling turning. Eventually, the highest accuracy of the model was 0.98; therefore, the model can be effective for retrieving initial cases for mechanical part redesign.

A new database would help engineers and manufacturers to apply machine learning to mechanical parts. Computer vision researchers use machine learning to train computers in visually recognizing objects--but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers. A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3-D mechanical parts, designed to help researchers apply machine learning to those parts in actual machines. "We are in the deep learning era, using computers to search for things visually," said Karthik Ramani, Purdue's Donald W. Feddersen Distinguished Professor of Mechanical Engineering. "But no one is focusing on the parts that go into machines: pipes, bearings, motors, washers, nuts and bolts, etc. Those are the things that are important to us as engineers and manufacturers. We want to be able to point a camera at a real-world part, and have the computer tell us everything about that part or design."

Engineers and business leaders have been working on autonomous cars for years, but there's one big obstacle to making them cheap enough to become commonplace: They've needed a way to cut the cost of lidar, the technology that enables robotic navigation systems to spot and avoid pedestrians and other hazards along the roadway by bouncing light waves off these potential obstacles. After all, today's lidars use complex mechanical parts to send the flashlight-sized infrared lasers spinning around like the old-fashioned, bubblegum lights atop police cars -- at a cost of $8,000 to $30,000. But now a team led by electrical engineer Jelena Vuckovic is working on shrinking the mechanical and electronic components in a rooftop lidar down to a single silicon chip that she thinks could be mass produced for as little as a few hundred dollars. Jelena Vuckovic, the Jensen Huang Professor in Global Leadership in the School of Engineering, professor of electrical engineering and, by courtesy, of applied physics. The project grows out of years of research by Vuckovic's lab to find a practical way to take advantage of a simple fact: Much like sunlight shines through glass, silicon is transparent to the infrared laser light used by lidar (short for light detection and ranging).

An unmanned automobile competes in the i-VISTA (Intelligent Vehicle Integrated Systems Test Area) Autonomous Driving Challenge on August 18 in Chongqing, China. An unmanned automobile competes in the i-VISTA (Intelligent Vehicle Integrated Systems Test Area) Autonomous Driving Challenge on August 18 in Chongqing, China. For many, if not most Americans, the idea of a world in which we don't drive cars is a distant and possibly unlikely future. Your purchase helps support NPR programming. When autonomous, or self-driving cars make headlines, it's often for all the wrong reasons: yet another Tesla public scandal; an accident during an autonomous test drive; or the laughably terrifying face of the new autonomous Jaguar.