Intelligent design assistant: Optimizing machine specification prediction with machine learning

In engineering practices, designers and users frequently face the challenge of estimating key performance or specification data based on limited input information, a task that can often be time-intensive. To address this, there is a growing demand for tools that leverage advancements in artificial intelligence to streamline and optimize design processes. This study presents an innovative Intelligent Design Assistant (IDA) tailored to bridge the gaps in engineering optimization by enabling the prediction and estimation of machine specifications, including geometry and design parameters, using diverse input data. The core of the proposed IDA integrates multiple machine learning techniques, such as Logistic Regression, k-Nearest Neighbors and Support Vector Machine. The system’s performance was validated using two distinct real-world datasets, showcasing its robustness and efficiency in a practical application. By aiding design decisions, the ML-based IDA offers several advantages, including streamlined decision-making, preservation of organizational expertise, reduced man-hour expenditure, and enhanced computational speed and precision.