A method for manufacturing an object, the method comprising: using a processor to perform the steps of: receiving first data representing a design of the object to be manufactured, the object comprising a composite material formed from a ply and having object features that include regions of curvature; obtaining second data representing a machine-learning model, the machine-learning model representative of production data and based at least partially on object features that include regions of curvature of the object and a ply angle, and quality data that are representative of defect data generated upon inspection of the object; wherein the second data includes a classifier comprising a set of weights that define a trained neural network based on the machine-learning model and that is configured to detect when a relationship between the ply angle on the object and the curvature at regions of curvature (204A, 204B) on the object is indicative of an estimated defect rate exceeding a threshold amount, the detection being made on the basis of the ply angle relative to the gradient of the object's surface at each region of curvature (204A, 204B); generating third data using the first data and the second data, wherein the third data indicates a modification to the design of the object, wherein the modification comprises an iterative adjustment to the ply angle in the object to reduce the estimated defect rate below the threshold amount; and sending the modified design of the object to a manufacturing tool to enable production of the object, wherein the machine learning model is updated in a continuous manner as the production data is received. using a processor to perform the steps of: receiving first data representing a design of the object to be manufactured, the object comprising a composite material formed from a ply and having object features that include regions of curvature; obtaining second data representing a machine-learning model, the machine-learning model representative of production data and based at least partially on object features that include regions of curvature of the object and a ply angle, and quality data that are representative of defect data generated upon inspection of the object; wherein the second data includes a classifier comprising a set of weights that define a trained neural network based on the machine-learning model and that is configured to detect when a relationship between the ply angle on the object and the curvature at regions of curvature (204A, 204B) on the object is indicative of an estimated defect rate exceeding a threshold amount, the detection being made on the basis of the ply angle relative to the gradient of the object's surface at each region of curvature (204A, 204B); generating third data using the first data and the second data, wherein the third data indicates a modification to the design of the object, wherein the modification comprises an iterative adjustment to the ply angle in the object to reduce the estimated defect rate below the threshold amount; and sending the modified design of the object to a manufacturing tool to enable production of the object, wherein receiving first data representing a design of the object to be manufactured, the object comprising a composite material formed from a ply and having object features that include regions of curvature; obtaining second data representing a machine-learning model, the machine-learning model representative of production data and based at least partially on object features that include regions of curvature of the object and a ply angle, and quality data that are representative of defect data generated upon inspection of the object; wherein the second data includes a classifier comprising a set of weights that define a trained neural network based on the machine-learning model and that is configured to detect when a relationship between the ply angle on the object and the curvature at regions of curvature (204A, 204B) on the object is indicative of an estimated defect rate exceeding a threshold amount, the detection being made on the basis of the ply angle relative to the gradient of the object's surface at each region of curvature (204A, 204B); generating third data using the first data and the second data, wherein the third data indicates a modification to the design of the object, wherein the modification comprises an iterative adjustment to the ply angle in the object to reduce the estimated defect rate below the threshold amount; and sending the modified design of the object to a manufacturing tool to enable production of the object, wherein the machine learning model is updated in a continuous manner as the production data is received.