Weld quality inspection with domain knowledge infused adaptive-network-based fuzzy inference system

A system for weld quality inspection, the system 100 comprising:

• a memory storing instructions;
• one or more Input/Output (110) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a labeled set of a plurality of images with each of the plurality of images comprising a weld of a joint type from among a set of joint types of interest, wherein the plurality of images are labeled as a good quality weld and a bad quality weld;
• configure weightages to be assigned to each of a plurality of geometrical parameters for the weld of the one or more joint types, wherein the weightages are one of (i) default with equal weightages across the plurality of geometrical parameters, and (ii) customized with varying values of the weightages across the plurality of geometrical parameters;
• preprocess the plurality of images and marking a closed outline around the weld in each of the plurality of images;
• segment each of the preprocessed plurality of images to determine a plurality of fractals of the weld, wherein pixel coordinates of the largest fractal among the plurality of fractals are identified;
• extract the plurality of geometrical features of the weld in accordance with the joint type using the pixel coordinates of the associated largest fractal and weighing the plurality of geometrical features of the weld in accordance with the configured weightages to introduce domain biases for joint types;
• generate a quality index for each of the plurality of images based on the extracted plurality of geometrical features using domain knowledge in form of gold standard of a good quality weld of each joint type;
• encode class labels for generated the quality index for each of the plurality of images with ‘0’ indicating image with the bad weld quality and ‘1’ indicating image with the good weld quality, wherein the encoded class labels serve as a training data for training the weld inspection model;
• compute domain threshold values for each of the plurality of geometrical features associated with images encoded with class label ‘1’ associated with good weld quality;
• and
• train a weld inspection model executed by the one or more hardware processors, for weld quality inspection to predict a confidence score of the weld of the joint type of each of the plurality of images comprising a Domain Knowledge Infused Adaptive-Network-Based Fuzzy Inference System (DKI-ANFIS), wherein the DKI-ANFIS comprises a plurality of network layers infused with domain knowledge, wherein a first layer comprises a Weld Good Membership Function and a Weld Bad Membership Function with trainable parameters which generate a set of rules for each of the plurality of geometrical parameters.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a labeled set of a plurality of images with each of the plurality of images comprising a weld of a joint type from among a set of joint types of interest, wherein the plurality of images are labeled as a good quality weld and a bad quality weld;
• configuring weightages to be assigned to each of a plurality of geometrical parameters for the weld of the one or more joint types, wherein the weightages are one of (i) default with equal weightages across the plurality of geometrical parameters, and (ii) customized with varying values of the weightages across the plurality of geometrical parameters;
• preprocessing the plurality of images and marking a closed outline around the weld in each of the plurality of images;
• segmenting each of the preprocessed plurality of images to determine a plurality of fractals of the weld, wherein pixel coordinates of the largest fractal among the plurality of fractals are identified;
• extracting the plurality of geometrical features of the weld in accordance with the joint type using the pixel coordinates of the associated largest fractal and weighing the plurality of geometrical features of the weld in accordance with the configured weightages to introduce domain biases for joint types;
• generating a quality index for each of the plurality of images based on the extracted plurality of geometrical features using domain knowledge in form of gold standard of a good quality weld of each joint type;
• encoding class labels for generated the quality index for each of the plurality of images with ‘0’ indicating image with the bad weld quality and ‘1’ indicating image with the good weld quality, wherein the encoded class labels serve as a training data for training the weld inspection model;
• computing domain threshold values for each of the plurality of geometrical features associated with images encoded with class label ‘1’ associated with good weld quality;
• and
• training, a weld inspection model executed by the one or more hardware processors, for weld quality inspection to predict a confidence score of the weld of the joint type of each of the plurality of images comprising a Domain Knowledge Infused Adaptive-Network-Based Fuzzy Inference System (DKI-ANFIS), wherein the DKI-ANFIS comprises a plurality of network layers infused with domain knowledge, wherein a first layer comprises a Weld Good Membership Function and a Weld Bad Membership Function with trainable parameters which generate a set of rules for each of the plurality of geometrical parameters.