A Machine Learning Framework for Decision Support in Design and Manufacturing

The widespread adoption of computer-aided design (CAD) and manufacturing (CAM) tools has resulted in the acceleration of the product development process, reducing the time taken to design a product [46]. However, the product development process, for the most part, is still decentralized with the design and manufacturing reviews being performed independently, leading to differences between as-designed and as-manufactured component. A successful product needs to meet its specifications, while also being manufacturable. In general, the design engineer ensures that the product is able to function according to the specified requirements, while the manufacturing engineer gives feedback to the design engineer about its manufacturability. This iterative process is often time consuming, leading to longer product development times and higher costs. Recent researches in integrating design and manufacturing [24, 28, 46] have tried to reduce these differences and making the product development process easier and accessible to designers, who may not be manufacturing experts. In addition, there have been different efforts to enable a collaborative product development process and reduce the number of design iterations [8, 10, 41]. However, with the increase in complexity of designs, integrating the manufacturability analysis within the design environment provides an ideal solution to improve the product design process.