Multimaterial-welding Ag

Valentin Bersier

Gregor Eckhard

Slobodan Glavaski

A method of manufacturing an assembly is disclosed. The method includes providing a first object, wherein the first object has an indentation with a sidewall, and providing the second object, wherein the second object has a lateral outer surface portion, and wherein one of the sidewall and the lateral outer surface portion is a thermoplastic material. The lateral outer surface portion of the second object is in physical contact with the sidewall of the first object, and mechanical energy is coupled into at least one of the first and second objects to cause energy absorption due to friction between the lateral outer surface portion and the sidewall, until a flow portion of the thermoplastic material becomes liquefiable and flows. After re-solidification of the thermoplastic material, the flow portion secures the second object to the first object.

Securing a second object to a first object

A computer implemented method comprises the steps of: providing a user interface to a computer terminal; providing a welding machine interface () to a welding machine () which is equipped with a set of sensors having a power supply sensor () configured to sense a power supplied by the welding machine () to set a connector to an object in runtime; obtaining a threshold performance metric data signal representing threshold product performance metric predefined via the user interface; obtaining a power supply data signal from the welding machine () via the welding machine interface (), which power supply data signal represents the sensed power supplied by the welding machine () to set the connector to the object; applying a machine learning model to the power represented by the obtained power supply data signal such that the machine learning model calculates a model product performance metric, wherein the machine learning model is specifically pre trained with training power sensed by the power supply sensor () of the set of sensors of the welding machine () and measured product performance metrics; comparing the calculated model product performance metric to the threshold product performance metric represented by the threshold performance metric data signal; and generating a non-consistency data signal when the calculated product performance metric does not comply with the threshold product performance metric.

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Lausanne, Switzerland

Valentin Bersier