13 results about "Lean manufacturing" patented technology

The application provides an intelligent inventory control management method and system based on a supply chain cooperation mechanism, applied to the technical field of supply chain management, preliminary demand prediction of a downstream partner is received, demand intention information containing customization component possibility weight and potential demand range is generated according to the prediction, after the upstream supplier receives the demand intention information, the influence of the demand intention information on future production resources is further evaluated, an evaluation result is obtained, finally, according to the evaluation result, the method performs flexible preparation in advance, including production window soft reservation, upstream material pre-communication and special mold preparation, effectively coping with the supply uncertainty of the customized components, avoiding the dilemma of standard component overstock and customized component shortage, realizing lean production and on-time delivery, and bringing significant economic benefits and competitive advantages to enterprises.

The application relates to the technical field of intelligent manufacturing teaching and training equipment, and discloses a lean production training platform based on production big data analysis, which comprises an entity production line module, a multi-source heterogeneous data acquisition system, an edge computing and control hub and a lean production training terminal. The entity production line module is used for simulating a production operation process in a real discrete manufacturing environment. The multi-source heterogeneous data acquisition system is distributedly arranged on the entity production line module and is used for collecting multi-dimensional data in a production process in real time. Through cooperation of an industrial sensor array, a machine vision unit, an RFID (Radio Frequency Identification) unit and a data aggregation terminal, multi-dimensional data such as equipment states, process parameters and material tracking are collected in real time, millisecond-level perception of whole-factor data such as people, machines, materials, methods and environments in the production process is realized, and the problems of single data dimension and rough perception granularity of a traditional training platform are fundamentally solved, so that a rich and accurate data basis is provided for lean analysis.

The application discloses a production rhythm dynamic evaluation method and system based on multi-attribute process constraints, relates to the tobacco technical field, and is suitable for the process manufacturing scene of mixed production arrangement of multiple brands. The method solves the problems of distortion of traditional static evaluation standards, broken cross-day production data, and difficulty in quantifying process coordination efficiency, provides dynamic and accurate benchmarks for production rhythm evaluation, and supports lean production landing in a tobacco silk workshop. In the method, step S1 realizes semantic alignment of production data, guarantees production shift analysis integrity; step S2 constructs a multi-attribute process constraint system, adapts to different process production requirements; step S3 generates differentiated theoretical working hour benchmarks, eliminates systematic deviation of the evaluation system; step S4 separates time deviation interference components, improves delay judgment accuracy; step S5 realizes automatic identification of delay events, reduces manual statistical lag; and step S6 locates key links of capacity constraints, and provides quantitative basis for production scheduling optimization.

This invention provides a control method and apparatus based on a lean manufacturing system, comprising: calculating the operational maturity value of a target lean manufacturing system to identify problems to be improved; constructing manufacturing strategic goals by comparing the target factory with business objectives based on pre-built quantitative indicators; streamlining the organizational structure based on pre-built lean processes, lean organizational structure, and lean departmental functions and responsibilities; constructing a multi-level key performance indicator system and a facilitating management meeting mechanism; and finally, constructing an implementation plan and controlling the target factory according to the implementation plan to achieve the optimization goals. This invention solves the problems of high deployment costs and poor application effects in existing technologies.

The application discloses a 3D printing device for detecting and regulating integrated insulating material and an intelligent manufacturing method. The device comprises a printing module, a regulating module and a detecting module used in cooperation. The printing module comprises a moving platform, a printing tank, a screen plate, a cleaning rod and a light-curing surface light source. The regulating module comprises an electrode assembly and a regulating power supply. The detecting module comprises a laser thermal excitation assembly and an infrared thermal imager. The laser thermal excitation assembly is used for applying thermal loading to a printing sample. The infrared thermal imager collects surface temperature evolution information of the printing sample in the thermal loading process and transmits the information to a data processing system. The orientation and distribution of the thermally conductive filler are regulated by using the auxiliary effect of an electric field, and the thermal conductivity distribution state of the material is detected in real time by means of a thermal wave imaging technology. According to the detection result, the parameters of the applied electric field are dynamically adjusted, the precise closed-loop control of the distribution and orientation of the filler is realized, and thus an effective path is provided for the lean manufacturing of high-thermal-conductivity-low-loss insulating material.

The utility model discloses plastic part detection equipment with a Type-D interface, and the plastic part detection equipment comprises vibrators, a visual test bottom, a vibrator product discharge channel, a transparent glass disc, a CCD test module, a probe, an air blowing and discharging part, a storage mechanism and a rotary motor, and the two vibrators convey products to the inner ring and the outer ring of the transparent glass disc through the product discharge channel at the same time for testing; and the tested products are conveyed to the defective product storage mechanism and the non-defective product storage mechanism for classified output. According to the invention, the defects of missing detection of a manual detection mode and confusion of different types of plastic part products are overcome, the stable detection effect of the plastic part of the Type-D interface is ensured, the detection efficiency is improved, the yield of a production line is further improved, the production operation cost can be reduced, and the goal of lean production is achieved. Under the condition that the detection quality is guaranteed, the labor cost is reduced, the operation time is short, and the operation efficiency is greatly improved.

1. Name of the designed product: Lean pipe (4.0). 2. Use of the designed product: Pipe for lean production, modular tooling and material handling system. 3. Design points of the designed product: In shape. 4. Pictures or photos that best show the design points: Design 1 perspective view 1. 5. Design 1 is designated as the basic design.

The invention provides an intelligent inventory control management method and system based on a supply chain collaboration mechanism, which are applied to the technical field of supply chain management, and the method comprises the steps: receiving the preliminary demand prediction of a downstream partner, generating demand intention information containing the possibility weight of a customized part and a potential demand range according to the prediction, and carrying out the control of the demand intention information. After an upstream supplier receives demand intention information, the influence of the demand intention information on future production resources is further evaluated, an evaluation result is obtained, finally, according to the evaluation result, flexible preparation including production window soft reservation, upstream material pre-communication and special mold preparation is carried out in advance, the supply uncertainty of customized parts is effectively dealt with, and the production efficiency is improved. The problems of overstock of standard components and shortage of customized components are avoided, lean production and on-time delivery are realized, and remarkable economic benefits and competitive advantages are brought to enterprises.

The application discloses a pipeline lean production management system and method, and relates to the technical field of ship manufacturing. The system solves the problems existing in the traditional production mode through workshop reconstruction and process optimization. The system adopts a hierarchical real-time control architecture, which comprises an edge computing gateway, a MES server and a data acquisition module. The edge computing gateway runs a hard real-time operating system and executes millisecond-level control logic. The MES server is arranged on a management layer, runs a general operating system, and executes second-level monitoring and historical analysis. The data acquisition module is arranged on production equipment of a core machining process of a pipeline workshop and is connected with the edge computing gateway and the MES server.

This invention relates to the field of engine component testing technology, and discloses a cylinder head airtightness testing device and method, including a control cabinet. The control cabinet is installed in a corresponding position via a support mechanism at the bottom. A shock-absorbing mechanism is provided on the top of the control cabinet, and an adaptive clamping mechanism is connected to the upper part of the shock-absorbing mechanism, realizing the integration of press-fitting and leak detection, which greatly improves the testing efficiency. By integrating the two processes of seal press-fitting and airtightness testing into one, there is no need for manual transfer of the cylinder head, and the single cycle is short. Compared with traditional separate equipment, the testing efficiency is improved. At the same time, the equipment occupies less space and consumes less energy, solving the pain points of traditional process disconnection, low efficiency and large footprint, meeting the requirements of lean production and showing significant progress. By establishing a database linking press-fitting parameters and leakage rate, the accumulated data can realize intelligent optimization of process parameters, providing data support for improving the quality of seal assembly, and has outstanding substantive features.

The invention provides a lean production grade evaluation method and system, belongs to the technical field of lean production in the manufacturing industry, and aims to improve the accuracy of lean production grade evaluation. According to the specific technical scheme, on the basis of the actual production environment, for a certain production plant area, key factors influencing production are recognized and determined, lean production evaluation indexes are determined, and a lean production index system combining a calculation type index and a conformance type index is constructed. And determining the weight of each level of index in lean production through an AHP-GA method. And collecting corresponding data of the indexes, and carrying out normalization processing. And determining the evaluation standard grade and the evaluation standard cloud of each level of index. An evaluation cloud picture is generated through a cloud generator, and a comprehensive evaluation result is defined. And lean production grades and influence production indexes are determined, and grade improvement implementation suggestions are obtained.

The invention provides a lithium ion battery voltage stabilizing table capable of saving space, which comprises a bracket, the bracket comprises at least two sections of sub-brackets, a telescopic matching structure which is telescopic to adjust the length of the whole bracket is arranged between the two sections of sub-brackets, and each sub-bracket is provided with a supporting leg; the chain plate comprises a plurality of sections of supporting plates which are connected in sequence, the chain plate is laid on the support, and at least one of the two ends of the chain plate is wound or unfolded through a winding mechanism. According to the invention, the space of a workshop can be saved by stretching the length of the lithium ion battery voltage stabilizing table according to the number of the formed batteries, the equipment investment can be reduced, and the method is a very good lean manufacturing method and a battery pack voltage stabilizing method. When the lithium ion battery voltage stabilizing table is not used, the lithium ion battery voltage stabilizing table can be contracted to the shortest state, and storage and transportation space is greatly saved. The core structure is a mechanical telescopic mechanism, the cost is controllable, popularization is easy, the telescopic adjusting process is easy to operate, and time and labor are saved.

This application discloses a mechanism- and data-driven product quality control method and related apparatus, relating to the field of product quality control technology. The method includes: acquiring multiple sets of historical measurement data; using the process characteristics and quality characteristics of each step in a preset process as nodes; calculating the comprehensive grey relational degree between nodes based on the multiple sets of historical measurement data; constructing a Bayesian network; acquiring the real-time values ​​of the process characteristics and quality characteristics of the preset steps in the preset process; using these real-time values ​​as input; and using the Bayesian network for inference to predict the predicted value of the quality characteristic of the last step in the preset process, thereby further controlling product quality. This application combines mechanism and data to control product quality, enabling accurate prediction and timely intervention of quality problems, achieving the high requirements of lean manufacturing.