The present invention indicates an information processing system that can be exploited in a large number of manufacturing companies, which work on order.

In such companies, the experience of the workers is a strategic and essential resource; the present invention transforms the substance of such experience, from a purely humanistic entity into a technical factor in all respects: a heritage preserved in the memories of computer systems. The invention consists in teaching the use of known mathematical tools derived from identification theory, to extract and encode the aforementioned wealth of experience. The inventive step consists in the fact that, although the necessary data and information are potentially available, a sample of training data, as such, suitable for using an identification model, is in fact not available, nor it is trivial to derive it from the data that are actually available.

A CNC system and computer software program are operative to perform: receiving an design work; if the design work is incomplete, then using a convolutional neural network (CNN) to complete the design work and then using an auto-mode to snap fit components into the design; converting the compete design to CAD/CAE instructions; using a recurrent neural network (RNN) to create a step-by-step assembly instructions for the completed design work so as every connection of said design work is fulfilled; and assigning the completed design specification to be manufactured by a CNC machining tool in an array of CNC machining tools connected together and to the CNC system via a network.

A welding path generating system and a welding path generating method are provided. The welding path generating system includes an image sensor, a storage device, and a processor. The image sensor obtains a sensing image of a welding target. The storage device stores a vision analysis module, an analysis module, and a path planning module. The processor is coupled to the storage device and the image sensor. The processor executes the vision analysis module to analyze the sensing image and create scan data. The processor executes the analysis module to identify weld bead profile information according to the scan data. The processor executes the path planning module to generate welding path information according to the weld bead profile information.

A method including receiving, by a processing device, a first selection of at least one of a first fabrication process or first manufacturing equipment to perform manufacturing operations of the first fabrication process. The method can further include inputting the first selection into a digital replica of the first manufacturing equipment wherein the digital replica outputs physical conditions of the first fabrication process. The method may further include determining environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process. The processing device may further determine a modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment. The method can further include performing at least one of applying the modification to the first fabrication.

To provide a wire electric discharge machine which can appropriately perform thermal displacement correction of upper/lower guides even when the installation environment changes. Provided are a storage unit that stores temperatures of machine elements as temperature data, and a rendering unit that digitizes the installation environment and renders as environmental data. Additionally provided are a position command unit that commands a relative position of the upper/lower guides; and a relational expression calculation unit that sets the temperature data environmental data as input data, sets the relative position as training data, and calculates the relational expression by way of machine learning. Further provided are a relational expression decision unit that calculates a correction amount by substituting the temperature of the machine element into this relational expression, and in the case of error between the relative position of the upper/lower guides based on this correction amount and the relative position commanded by the position command unit being small, decides this relational expression as a formal relational expression; and a correction execution unit that performs correction on the relative position of the upper/lower guides using this relational expression.

Embodiments provide functionality to prevent collisions between robots and objects. An example embodiment detects a type and a location of an object based on a camera image of the object, where the image has a reference frame. Motion of the object is then predicted based on at least one of: the detected type of the object, the detected location of the object, and a model of object motion. To continue, a motion plan for the robot is generated that avoids having the robot collide with the object based on the predicted motion of the object and a transformation between the reference frame of the image and a reference frame of the robot. The robot can be controlled to move in accordance with the motion plan or a signal can be generated that controls the robot to operate in accordance with the motion plan.

A method and a device for simulating a machining process of a workpiece on an NC-controlled machine tool by means of a self-learning artificial neural network. Process parameters both from a machining process on a real machine tool located in a manufacturing section and a digital machine model implemented in a simulation section are provided to the artificial neural network to learn the behavior of the machine tool including the tools and workpieces used and are reformatted into input parameters by means of mathematical transformation. By learning the behavior of the machining process, the artificial neural network ca, send output files back to the simulation software of the simulation section and optimally adapt the behavior of the digital machine model to the conditions of the real machine tool by adapting the simulation parameters and make it more efficient in order to optimize the machining process on the machine tool.

A thermal displacement compensation apparatus for compensating a dimensional measurement error due to a thermal displacement of a workpiece, including a machine learning device for learning shape measurement data at the time of inspection of the workpiece, wherein the machine learning device observes image data showing the temperature distribution of the workpiece and shape data after machining as state variables representing the current state of the environment, acquires judgment data indicating the shape measurement data at the time of inspection, and learns the image data showing the temperature distribution of the workpiece and shape data after machining and the shape measurement data at the time of inspection in association with each other using the observed state variables and the acquired judgment data.

A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.

A kinematics model-free trajectory tracking method for a robotic arm includes the following steps. Obtain an actual trajectory equation r.sub.a(t) of the robotic arm at time t according to a sensor, and combines the actual trajectory equation r.sub.a(t) with a predetermined target trajectory equation r.sub.d(t) to obtain a first error function e(t). Obtain a differential equation (I) of a state change rate of a driver of the robotic arm. Obtain a second error function ?(t). Pass the second error function c(t) through the applied gradient neural network to obtain equation (IV). Jointly solve equation (I) and equation (IV) to obtain an joint state vector ?(t) of the robotic arm. Drive a motion of the robotic arm by a controller according to the joint state vector ?(t) of the robotic arm to complete trajectory tracking.