Abstract: A geometry of a substrate surface is received at a neural network. The neural network is trained using one or more training sets. Each training set comprises a different type of substrate geometry and a collection of manufacturing process parameters. The substrate is configured to receive at least one liquid droplet. A shape of the at least one droplet after it has been deposited on the substrate is determined based on the received geometry. An output representing the determined shape of the at least one droplet is produced.

Abstract: A computer representation of a printable product part and a plan for the printable product part to be deposited using an additive manufacturing process are received. The printable product part comprises an accumulation of material deposited by the additive manufacturing process. The plan comprises a tool-path representation of the printable product part and process parameters. A plurality of as-printed shapes of the printable product part are determined after it has been deposited according to the plan. Geometric differences between any of the plurality of as-printed shapes with the computer representation of the product part are determined.

Abstract: A plurality of scanned prints of a product part and a scan-path are received. A shape of a minimum printable feature of the product part is determined by analyzing the respective prints in a scan-path representation. A manufacturing error of the minimum printable feature is determined based on the analysis. A manufacturing error of a shape of the part is determined based on the determined manufacturing error of the minimum printable feature. An estimated manufactured shape of the part is produced based on the determined manufacturing error of the part.

Abstract: A plurality of scanned prints of a product part and a scan-path are received. A shape of a minimum printable feature of the product part is determined by analyzing the respective prints in a scan-path representation. A manufacturing error of the minimum printable feature is determined based on the analysis. A manufacturing error of a shape of the part is determined based on the determined manufacturing error of the minimum printable feature. An estimated manufactured shape of the part is produced based on the determined manufacturing error of the part.

Abstract: A geometry of a substrate surface is received at a neural network. The neural network is trained using one or more training sets. Each training set comprises a different type of substrate geometry and a collection of manufacturing process parameters. The substrate is configured to receive at least one liquid droplet. A shape of the at least one droplet after it has been deposited on the substrate is determined based on the received geometry. An output representing the determined shape of the at least one droplet is produced.

Abstract: A computer representation of a printable product part and a plan for the printable product part to be deposited using an additive manufacturing process are received. The printable product part comprises an accumulation of material deposited by the additive manufacturing process. The plan comprises a tool-path representation of the printable product part and process parameters. A plurality of as-printed shapes of the printable product part are determined after it has been deposited according to the plan. Geometric differences between any of the plurality of as-printed shapes with the computer representation of the product part are determined.