Digital Intelligent Forward Design Method and Its Application in Manufacturing Equipment and Process

Aiming at the common design problems in precision and energy efficiency of manufacturing equipment and process, a digital intelligent forward design method with generalization ability is proposed. Based on the design requirements, the energy consumption, materials consumption and reliability are calculated according to the multi-modal information of manufacturing equipment. The design method can enhance the ability of self-sensing, self-adaptation, self-learning and self-decision to satisfy forward design requirement. The manufacturing equipment is divided into control system, drive system and auxiliary system. The corresponding energy consumption model is established. Taking collaborative optimization of additive manufacturing equipment as an example, the energy consumption model is analyzed in detail from the perspective of thermodynamic energy conversion. A nonlinear transient temperature field simulation method with thermal convection and thermal radiation as the boundary is established. The energy and materials consumed in the additive manufacturing process are accurately predicted based on the simulation method. The reliability model of manufacturing equipment is established. The model is used to analyze the data of failure events in manufacturing process based on probability statistics. The mean time between failures is used to describe the reliability of manufacturing equipment. The basic probability function of mean time between failures(MTBF) is established under comprehensive conditions. Based on the geometric modeling kernel of non-uniform rational B-spline curve, the 3D non-grid conceptual design prototype of automobile fuel neck pipe is accurately expressed using high computing capability. Virtual manufacturing driven by digital twins is realized. Physically manufacturing is carried out using laser additive manufacturing equipment. The energy and materials consumed during the manufacturing process are measured. The mean absolute error of energy consumption prediction is 11 822.62 J. The mean absolute percentage error is 0.0834. The root mean square error is 16 845.69 J. The mean absolute error of materials consumption prediction is 0.003 0 g. The mean absolute percentage error is 0.071 3. The root mean square error is 0.004 1 g. The experiment results show that the digital intelligent forward design method has important application value for manufacturing equipment in multiple working conditions aimed at high efficiency, high precision and low carbon. © 2023 Chinese Mechanical Engineering Society. All rights reserved.