To greatly improve the design efficiency and the prediction of the welding properties of on-site solder joints in the welding workshop, an intelligent calculation algorithm based on CATIA. API for secondary development of the solder joint level is designed. This algorithm, based on different interference collision radiation radii, calculates the correlation accuracy between different solder joints and BIW products, and extracts the BIW product information data related to the solder joints, to realize the automatic generation technology of digital and intelligent welding attribute BOM. On this basis, we use Python to write the linear regression algorithm in machine learning to train the welding attribute parameters of the solder joints.
Aiming at the switching production mode of 4+n multi car series in a domestic luxury independent brand welding workshop, a new module production mode is formulated by combining three production modes of order production, pull production and high module production. The MES system is used to schedule the production plan of the welding workshop, reorganize and form independent module orders for each line, so as to meet the demand of 4+n car series on-line production at the same time. At the same time, by using the characteristics of each line, the position of each sub assembly line is monitored in real time through PLC, and the current production model is identified, detected, and information converted at the key position. At the same time, the corresponding solutions to the planned and unexpected situations are formulated.
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