Production of cameras with reduced rejection rate

A non-transitory machine-readable data medium on which is stored a computer program including machine-readable instructions for producing a camera, the instructions, when executed by one or more computers, cause the one or more computers in combination of a production facility for cameras controller by the one or more computers, to perform the following steps:

• providing prefabricated components;
• adjusting at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion;
• and
• adhesively bonding the at least two of the prefabricated components to one another in the adjusted state;
• wherein:
• prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding;
• and
• the prediction is used as feedback for an influencing action on the production process,
• wherein the prediction takes into account changes in the optical performance of the camera between the adhesive bonding and final completion.

One or more computers comprising:

• a non-transitory machine-readable data medium on which is stored a computer program including machine-readable instructions for producing a camera, the instructions, when executed by the one or more computers, cause the one or more computers in combination of a production facility for cameras controller by the one or more computers, to perform the following steps:
• providing prefabricated components;
• adjusting at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion;
• and
• adhesively bonding the at least two of the prefabricated components to one another in the adjusted state;
• wherein:
• prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding;
• and
• the prediction is used as feedback for an influencing action on the production process,
• wherein the prediction takes into account changes in the optical performance of the camera between the adhesive bonding and final completion.

A production facility for cameras, the production facility configured to:

• provide prefabricated components;
• adjust at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion;
• and
• adhesively bond the at least two of the prefabricated components to one another in the adjusted state;
• wherein:
• prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding;
• and
• the prediction is used as feedback for an influencing action on the production process,
• wherein the prediction takes into account changes in the optical performance of the camera between the adhesive bonding and final completion.