3018 results about "Manufacturing systems" patented technology

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent production scheduling method and system for a manufacturing system, and the method comprises the following steps: collecting an equipment state, a material neat rate and an order emergency degree in real time based on dynamic production data, and calculating an equipment availability coefficient, a material guarantee index and an order priority score through feature analysis; and forming a dynamic production feature set. The system constructs a production scheduling optimization model through work order-equipment matching degree calculation and process priority optimization, and performs multi-objective optimization solution by adopting an NSGA-III algorithm to realize the optimal combination of equipment utilization rate, order delivery rate and inventory balance. Meanwhile, an arbitration mechanism is introduced to coordinate process priority conflicts, and the scheme is verified through time, space and dynamic adaptability, so that feasibility and stability of production scheduling execution are ensured. According to the invention, the production scheduling efficiency of the manufacturing system can be improved, the equipment utilization rate can be improved, the order delivery delay rate can be reduced, and the adaptive ability to the change of the production environment can be enhanced.

The invention discloses an adaptive task division and decision joint optimization-based cloud edge computing resource collaborative scheduling method for a complex manufacturing system. The method comprises the following steps of 1, constructing a hierarchical cloud-edge collaborative computing network model; constructing a multi-objective optimization model, and defining an objective function and constraint conditions; 2, dynamically predicting and calculating a resource state through a resource sensing module based on an LSTM neural network, and generating a node resource prediction matrix; 3, dividing a calculation task generated by the manufacturing system into fine-granularity, medium-granularity and coarse-granularity subtask sets by adopting a multi-granularity subtask division algorithm (MSPA), and mapping the subtasks to corresponding calculation nodes; 4, constructing a task unloading decision model based on the D3QN, and dynamically selecting unloading nodes and an execution sequence of the subtasks in combination with a multi-objective optimization reward function; and 5, iteratively optimizing parameters of the D3QN model through a target network updating mechanism and a self-adaptive exploration strategy to realize real-time dynamic adjustment of a task scheduling decision.

A friction stir additive manufacturing system is provided. In one aspect, the system includes a spindle configured to rotate about a central axis, and a housing configured to receive at least a portion of the spindle, the housing configured to remain stationary relative to the spindle. The housing includes a wire inlet extending between an exterior surface of the housing and an interior surface of the housing, and a track extending from the wire inlet and partially around a circumference of an interior surface of the housing. The system also includes a feeding system configured to receive a wire from a roller and feed the wire through the wire inlet and into the track of the housing.

A friction stir additive welding screw is provided. In one aspect, the friction stir additive welding screw includes a first portion configured to be coupled to a friction stir additive welding device, and a second portion configured to penetrate a work-piece. The second portion includes a plurality of large threads, each large thread extending in a generally longitudinal direction. The second portion also includes a plurality of fine threads positioned along an edge of each large thread, and a plurality of teeth at a tip of second portion.

Disclosed is a wheel cylinder and a drum brake having the same, in which the number of position as well as other elements accompanied with the piston may be reduced, resulting in reduced manufacturing cost.

A friction stir additive welding device configured to join a first work-piece and second work-piece is provided. In one aspect, the device includes a nosecone including a channel extending along a central axis and configured to receive a rotating screw. The device also includes a slot including a first opening in an outer sidewall of the nosecone, a second opening in a wall of the channel of the nosecone, and a passageway through the nosecone between the first opening and the second opening. The slot is configured to receive a portion of a structure positioned between the first work-piece and the second work-piece.

A friction stir additive welding device configured to join a first work-piece and second work-piece is provided. In one aspect, the device includes a nosecone having a sidewall extending from a first end to a second end, a channel extending from the first end to the second end along a longitudinal axis of the nosecone, and a screw positioned within the channel. The first end is configured to couple the nosecone to a robotic arm and the second end has a surface configured to be advanced along a weld line between the first and second work-pieces. A central axis of the screw defines an angle that is less than 90 degrees relative to a plane that is perpendicular to surfaces of the first and second work-pieces as the surface of the nosecone advances along the weld line.

The invention provides a big data bamboo tableware manufacturing system based on production process monitoring, and relates to the field of intelligent detection systems.The big data bamboo tableware manufacturing system comprises an acquisition module, a prediction module, a preliminary distribution module, a disturbance response module, an optimization decision module and an abnormity self-inspection module. A dynamic stability index is calculated in combination with a dynamic image sequence under disturbance response, a rejection target is judged through an optimization decision-making module after scores are fused, a Bayesian method is introduced in the optimization process to dynamically adjust multiple decision-making parameters, a rejection action instruction is generated in real time, and an abnormity self-checking module monitors the rejection rate and score stability. Software and hardware signals are combined to realize multi-level fault diagnosis and system protection control; according to the invention, high-precision identification and dynamic elimination of bamboo tableware defects are realized, and the automation level and the production reliability are improved.

A friction stir additive manufacturing system configured to extrude a material is provided. In one aspect, the system includes a spindle configured to rotate about a central axis, the spindle including a conical portion having a plurality of threads at a deposition end of the spindle. The system also includes a housing configured to remain stationary relative to the spindle, the housing including a wire inlet extending through a side wall of the housing, an interior surface of the side wall defining a truncated cone terminating at a material exit, the truncated cone configured to receive the conical portion of the spindle. The system also includes a feeding system configured to feed a wire through the wire inlet and into a gap between the spindle and the truncated cone of the housing.

The invention discloses a mobile phone silica gel shell appearance detection system based on an optical image sensor, and relates to the technical field of appearance defect detection and intelligent visual identification. An image acquisition module acquires a high-quality multi-channel image through multi-angle synchronous acquisition; the dynamic light field regulation and control module realizes self-adaptive regulation of a regional light source based on reflectivity perception and a gray gradient change rate; the image processing and defect identification module fuses edge features, texture changes and a deep learning model to realize pixel-level defect classification; the material identification and shielding module identifies a non-silica gel area through multi-dimensional spectral features and eliminates interference; a time sequence defect evolution analysis module models a defect evolution track; the trend prediction module is used for predicting the extension trend of potential cracks and fatigue areas; the control feedback module realizes intelligent judgment and response linked with the manufacturing system; the system can realize high-precision and high-robustness automatic defect detection and risk prediction, and is suitable for online quality monitoring of large-batch flexible products.

The invention relates to the field of digital twinning, in particular to an intelligent decision analysis method and system for a digital twinning-based manufacturing system, and aims to improve the generalization ability and adaptability of a digital twinning model in an emerging market small sample scene through deep fusion of meta-learning and adversarial training. According to the method, the problem of prediction distortion caused by data scarcity is relieved, high-quality training samples are expanded in combination with a generative enhancement technology, a multi-target elastic scheduling mechanism is synchronously constructed, dynamic optimization and feasibility verification of a production strategy are realized in a digital twin environment, and finally a closed-loop optimized intelligent decision system is formed. According to the method, through deep fusion of meta-learning and adversarial training, by combining a generative enhancement technology to expand high-quality training samples and a synchronously constructed multi-target elastic scheduling mechanism, dynamic optimization and feasibility verification of a production strategy are realized in a digital twin environment, and finally a closed-loop optimized intelligent decision system is formed.

A method includes initiating a connection with a semiconductor manufacturing system. The method further includes generating a set of tool data items associated with the semiconductor manufacturing system. The method further includes providing a graphical user interface (GUI) presenting the set of tool data items associated with the semiconductor manufacturing system and receiving and via the GUI, user input selecting one or more of the tool data items. The method further includes adding configuration data associated with one or more of the tool data items to a data collection plan. The method further includes validating the configuration data by locating, in the manufacturing system, a configuration data file associated with the configuration data. The method further includes executing one or more data collection operations based on the data collection plan.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

An electrode manufacturing system includes a transferring part, a coating part, a drying part, and a heat treatment part. The transferring part is arranged downstream of the drying part to heat the electrode substrate to a high temperature and includes a transfer roller supports and transfers an electrode substrate in a long-sheet shape. The coating part applies an electrode slurry to an electrode current collector. The drying part dries the electrode slurry disposed on the electrode substrate. The heat treatment part is disposed downstream of the drying part along a transfer direction of the electrode substrate and applies heat to the electrode substrate to increase the adhesion of the electrode by changing the crystal phase of the PVdF binder of the electrode substrate during the heat treatment process, thereby preventing the separation of the electrode active material layer. A manufacturing method including the same is also provided.

A pickleball paddle with novel core characteristics and / or materials, and related systems, methods, and articles of manufacture are provided.

The invention discloses a scheduling evaluation method for low-entropy state operation of a discrete workshop manufacturing system, and the method comprises the following steps: 1) constructing operation conditions and following principles of the discrete workshop manufacturing system, analyzing the operation state of the discrete workshop manufacturing system, and constructing a static entropy function of the discrete workshop manufacturing system; 2) constructing a discrete workshop manufacturing system operation dynamic entropy function according to the situation that the system is in an out-of-control state and a fuzzy event occurs; 3) calculating the maximum feasible scheduling time limit of the scheduling scheme; and 4) analyzing and comparing the static entropy, the operation entropy and the maximum feasible scheduling time limit aiming at the problem of scheduling scheme selection, and selecting an execution scheme with small system entropy increase as an optimal execution scheduling scheme for guiding actual production scheduling. According to different scheduling schemes of the discrete workshop manufacturing system, the low-entropy state operation evaluation method is provided, the scheduling scheme with low uncertainty and high system order degree is selected, and low-entropy efficient ordered production of the discrete workshop manufacturing system is achieved.

The invention discloses a method and equipment for double-ultrasonic cooperative in-situ auxiliary electric arc additive manufacturing of a large metal component, an electric arc additive manufacturing system and an ultrasonic vibration auxiliary electric arc additive manufacturing system. The ultrasonic vibration auxiliary electric arc additive manufacturing system comprises two ultrasonic impact guns, an optical sensor, a temperature sensor and a calculation control system. The optical sensor collects optical characteristics of the deposition channel and the molten pool and converts the optical characteristics into electric signals to be transmitted to the calculation control system, and the temperature sensor is used for monitoring and collecting temperature information of the deposition channel and transmitting the temperature information to the calculation control system to draw an isotherm; the calculation control system adjusts the distance between the ultrasonic impact gun and the center of the molten pool according to the isotherm and the optical characteristics of the optical sensor mobile phone, and the ultrasonic power is adjusted; one of the ultrasonic peening guns applies ultrasound from the component side edge as materials are added to the component edge. By means of the multi-axis freedom degree and accuracy of the mechanical arm, double-ultrasonic vibration is applied to all positions of a large metal component in the material adding process, and the mechanical property is improved.

A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

The invention relates to the technical field of energy-carbon management and control of a product manufacturing process, and discloses a supply chain cluster-oriented energy-carbon management and control method of a complex product manufacturing process, which comprises the following steps of: firstly, constructing a supply chain cluster manufacturing system model of a complex product; secondly, constructing a subsystem classification model based on two core indexes of'energy consumption ratio 'and'carbon emission fluctuation', and dividing subsystems in the supply chain cluster manufacturing system into four classes; and finally, for different types of subsystems, making differentiated energy-carbon management and control strategies, including an I-class subsystem management and control strategy based on static data, II-class and III-class subsystem management and control strategies based on static-dynamic data fusion, and an IV-class subsystem management and control strategy based on dynamic data. According to the invention, an efficient solution is provided for the multi-level and diversified energy-carbon management and control requirements of supply chain leading enterprises in the complex product manufacturing process, and the precision and operability of energy-carbon management and control are remarkably improved.

Implementations disclosed describe, among other things, a system and a method of scanning a substrate with a beam of light and detecting for each of a set of locations of the substrate, a respective one of a set of intensity values associated with a beam of light reflected from (or transmitted through) the substrate. The detected intensity values are used to determine a profile of a thickness of the substrate.

A laser additive manufacturing system and method for a magnesium-based composite material. The system comprises a laser adjuster (1), a laser (2), a forming cover (3), an oxygen content adjuster (4), an inert gas cylinder (5), a powder feeder (6), a substrate (7) and a wire feeder (8), wherein the laser adjuster is used for controlling laser processing parameters of the laser; the laser is connected to a laser processing head (10) by means of an optical fiber (9); the oxygen content adjuster is in communication with the forming cover, so as to control the oxygen content of a processing environment in the forming cover; the inert gas cylinder and the powder feeder both pass through the forming cover to be connected to the laser processing head; the substrate is located inside the forming cover; the wire feeder is connected to a wire feeding head (12) by means of a wire feeding tube (11); the wire feeding head passes through the forming cover to perform laser additive manufacturing for a magnesium-based composite material, together with the laser processing head; and a real-time cooling sleeve (13) is also mounted on the wire feeding tube. Also provided are a method for preparing a magnesium-based composite material using the laser additive manufacturing system for the magnesium-based composite material, and a prepared magnesium-based composite material. By means of the manufacturing system and method, the additive processing of wires and powder is realized, and a magnesium-based composite material with a good mechanical property is prepared.

The invention discloses an isomeric low-alloy high-strength steel structure electric arc additive manufacturing process, and relates to the technical field of electric arc additive manufacturing, the isomeric low-alloy high-strength steel structure electric arc additive manufacturing process comprises the following steps: carrying out heat treatment and tensile test on a low-alloy high-strength steel wire to obtain test data, and establishing a process parameter-performance prediction model according to the test data; obtaining a performance demand of a target structural member, and drawing up a process parameter by using a process parameter-performance prediction model according to the performance demand; an electric arc additive manufacturing system is used for conducting electric arc additive manufacturing on the low-alloy high-strength steel wire, and a low-alloy high-strength steel structural part is obtained; acquiring temperature information of the low-alloy high-strength steel structural member by using an infrared thermal imager, and adjusting the process parameters in real time according to the temperature information to obtain a target structural member; gradient distribution of precipitation strengthening phases is achieved, isomerism of alloy element in-situ strengthening low-alloy high-strength steel materials is achieved, and the diversified requirements of target structural parts for different strengths and plasticity are met.

Monitoring and control system and method for additive manufacturing an in-situ macro-field thermography that uses an IR camera or near-IR camera to prioritize the data captured from an entire print layer, processes data streams layer-by-layer, and provides thermal history of previously printed layers as feedback to the control system. The system leverages one or more different mathematical thermophysical-based (MTB) models to compute a thermally related specification in order to optimize the printing sequence of islands or stripes layer-by-layer. The thermal feedback from the thermography system calibrates or updates the thermal model layer-by-layer. The system and / or method may provide real-time optimization of scanning topology in chessboard and stripe printing strategies. The method may be implemented by one or more computer programs controlling one or more computer controllers of an additive manufacturing system, such as a 3D printer implementing a metal powder bed fusion additive manufacturing process.

A method, apparatus, system, and computer program product for manufacturing an unconsolidated composite material. Sensor data is received from a sensor system for a composite material manufacturing system, wherein the sensor data is received during manufacturing of the unconsolidated composite material by the composite material manufacturing system. A set of predicted properties is determined for a number of portions of the unconsolidated composite material as completed from manufacturing by the composite material manufacturing system using the sensor data. A quality level for the number of portions of the unconsolidated composite material is identified based on the set of predicted properties for the number of portions of the unconsolidated composite material.

A method includes generating a causal graph based on a plurality of values, each value corresponding to a causal relationship between two or more sensors of a plurality of sensors in one or more manufacturing systems. The method further includes determining a causal strength index matrix. The method further includes responsive to identifying an anomalous behavior in at least one of the plurality of sensors, determining a root cause of the anomalous behavior using at least one of the causal strength index matrix or the causal graph. The method further includes causing a recommended corrective action to be issued based on the root cause of the anomalous behavior.

To provide a manufacturing system and method for creating multiple tissue constructs from cells.SOLUTION: A system can include a thaw subsystem (if the cells are provided in a frozen state), an expansion subsystem, a concentration subsystem, and a tissue maturation subsystem. Each of these subsystems is modular and can be reconfigured, and a process can be repeated depending on the specific tissue process being implemented. Multiple tissue types can be combined in multiple bioreactors. The activities of multiple bioreactors can be coordinated and controlled in an automated manner by a supervisor controller. The supervisor controller can receive user input at the start of the process, and can manage the process thereafter, alerting the user if user actions are required.SELECTED DRAWING: None

The invention belongs to the field of industrial automation, focuses on industrial robots in the field of semiconductor wafer manufacturing, and provides a system based on multi-mode sensing and intelligent decision making. A plurality of sensors are integrated on a robot and equipment, technologies of multi-sensor fusion, multi-modal data fusion, multi-physical field monitoring and the like are applied, and algorithms of dynamic trajectory planning, deep learning, adaptive control, reinforcement learning, digital twinning and virtual simulation and the like are combined; high-precision wafer carrying, rapid surface defect detection, thin film deposition process optimization, multi-robot collaborative scheduling and manufacturing process overall optimization are achieved, and the problems of precision, efficiency and quality in wafer manufacturing in the prior art are solved.

The invention relates to a complex manufacturing system optimization decision support method, which comprises the following steps of: collecting multiple types of data through distributed sensing nodes, fusing the data into a unified data model through semantic mapping, and constructing a dynamic data graph; key features are mined by using an improved deep learning model, and a multivariable prediction model is constructed to realize advanced prediction of equipment faults and the like; constructing a multi-objective optimization model, and generating and dynamically sorting a Pareto optimal scheme by adopting an adaptive algorithm; and issuing the scheme to an execution system, tracking execution and quantifying an evaluation result, and feeding back the evaluation result to a platform to optimize model parameters and feature weights to form closed-loop iteration. The invention aims to solve the problems of low data integration level, insufficient prediction precision, poor decision scheme adaptability and difficulty in continuous optimization in a complex manufacturing system.

Systems and methods for generating a shape-based graded lattice structure that can be used in additive manufacturing. A slicer computer system generates the lattice structure by simulating the packing of a planar region with variable-sized packing shapes, where packing shape sizes correspond to intensity values of a non-uniform physical field expected to be experienced by the article. An intermediate lattice structure is then generated using a first set of polygonal cells, followed by a second set of polygonal cells that refine the final lattice structure. Tailored sectioning and field-based smoothing can modify polygon packing algorithms to adapt lattice generation. The resultant multi-stage graded lattice structures, which may include multiple lattice patches and transition zones, from shape-based packing, tailored sectioning, field-based smoothing, and slicer-based additive manufacturing processing improve connectivity and manufacturability over traditional lattice structures.

The invention provides a complex manufacturing system production scheduling engine based on large language model spatio-temporal reasoning, and relates to the field of intelligent manufacturing, a scheduling demand is obtained, time-related information and space-related information are extracted, then spatial features and a time constraint graph are jointly coded into LLM analyzable spatio-temporal semantic vectors, and the LLM analyzable spatio-temporal semantic vectors are used for scheduling. And explicitly modeling a multi-dimensional interaction relationship of spatial proximity-time overlapping degree-resource competition intensity through association analysis of scheduling space-time semantics, so that LLM can realize more reliable space-time causal reasoning, and finally a scheduling scheme conforming to complex space-time constraints is generated. Thus, high-dimensional spatial-temporal characteristics in a manufacturing system can be adapted, spatial-temporal information required by user scheduling can be highlighted to adapt to an input format of LLM, then a nonlinear spatial-temporal coupling relationship in the production scheduling of a complex manufacturing system is concerned, and the generalization ability is improved.