Goods shelf moving and carrying system

Abstract

The invention discloses a goods shelf moving and carrying system which comprises a welding work station, a welding robot, a workpiece clamp and a welding parameter self-adaptive adjusting module. The suspension chain conveying system comprises a plurality of sections of suspension conveying chains which are independently driven and a dynamic speed regulation module; the carrying robot is arranged between the welding work station and the catenary conveying system and is provided with a multi-dimensional force sensor and a visual positioning system; the polishing workstation comprises a self-adaptive polishing robot and an online quality detection system; the central control system is in communication connection with each subsystem, and the central control system comprises a dynamic scheduling module which optimizes the production takt in real time based on order priority, equipment state and energy consumption data; a quality tracing module; and a cooperative control algorithm. According to the intelligent goods shelf, the specific technical problems of unstable quality, low efficiency, insufficient flexibility and the like in traditional goods shelf manufacturing are solved, and the digital, networked and intelligent upgrading of a manufacturing mode is achieved through system-level intelligent integration and innovation.

Description

A shelving moving and handling system Technical Field

[0001] This invention relates to the technical field of shelf movement, and more particularly to a shelf movement and handling system. Background Technology

[0002] In the manufacturing of shelving, storage equipment, and metal structural components, welding, handling, and grinding are core and labor-intensive production processes. Traditional production methods typically arrange each process independently, relying on manual labor or semi-automatic equipment, resulting in a series of inherent drawbacks: isolated processes and poor coordination: welding, handling, conveying, and grinding processes are usually completed by independent equipment, lacking effective information exchange and collaborative control between processes. The flow of workpieces between processes often relies on manual hoisting or simple conveyor lines, leading to unbalanced production cycles, severe work-in-process inventory buildup, and low overall production efficiency.

[0003] Unstable welding quality: Traditional welding operations rely heavily on the experience of the operators. Faced with fluctuations in material thickness and changes in assembly gaps, fixed welding parameters cannot guarantee consistent weld quality, easily leading to defects such as weld leaks, undercut, and porosity, resulting in a high rework rate. Post-weld quality inspection is delayed, and problems are often only discovered in subsequent processes or even during final inspection, causing even greater waste.

[0004] Rigid handling and conveying: Existing overhead conveyor chains or AGV systems mostly operate on fixed paths and at fixed intervals, making it impossible to dynamically adjust according to the real-time status of downstream workstations (such as equipment failures or workload levels). This easily leads to queues at bottleneck workstations and causes equipment to wait at other workstations, resulting in low logistics efficiency.

[0005] Therefore, there is an urgent need for an integrated, intelligent, and flexible racking movement and handling system to solve the above problems and achieve high-quality, high-efficiency, and low-cost production of racking. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the problems existing in the above-mentioned existing shelving moving and handling systems, the present invention is proposed.

[0008] Therefore, the purpose of this invention is to provide a shelving mobile handling system, which not only solves the specific technical problems of unstable quality, low efficiency and insufficient flexibility in traditional shelving manufacturing, but also realizes the upgrade of the manufacturing mode to digitalization, networking and intelligence through system-level intelligent integration and innovation.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a shelf-based mobile handling system, comprising: a welding workstation, including a welding robot, a workpiece fixture, and a welding parameter adaptive adjustment module; a overhead conveyor system, including multiple independently driven overhead conveyor chains and a dynamic speed regulation module; a handling robot, configured between the welding workstation and the overhead conveyor system, equipped with a multi-dimensional force sensor and a visual positioning system; a grinding workstation, including an adaptive grinding robot and an online quality inspection system; and a central control system, which is communicatively connected to each subsystem, the central control system comprising: a) a dynamic scheduling module, which optimizes the production cycle in real time based on order priority, equipment status, and energy consumption data; b) a quality traceability module, which records the welding parameters, transfer time, and grinding quality data of each workpiece; and c) a collaborative control algorithm, which coordinates the operation sequence of welding, handling, conveying, and grinding.

[0010] In a preferred embodiment of the shelf moving and handling system of the present invention, the welding parameter adaptive adjustment module adjusts the welding current, voltage, and speed in real time based on the weld visual inspection results and material thickness, specifically calculating the adjustment parameters using the following formula: ; ;in, To adjust the current, For the adjusted voltage, Based on the base current, Based on the base voltage, For material thickness deviation, For weld width deviation, For weld gap deviation, , , This is the adjustment coefficient.

[0011] As a preferred embodiment of the rack moving and handling system of the present invention, the dynamic speed regulation module of the overhead conveyor system adjusts the conveying speed in real time according to the load status of the downstream workstation and the priority of the workpiece, so as to give priority to the flow of high-priority workpieces and avoid the bottleneck workstation blockage.

[0012] As a preferred embodiment of the shelf-moving and handling system of the present invention, the handling robot is equipped with a path planning algorithm to calculate the optimal collision-free handling path based on the real-time acquired workshop equipment positions, other robot motion trajectories, and workpiece dimensions. The objective function of the path planning is: ;in, For handling time, For energy consumption, To avoid obstacles, , , These are the weighting coefficients.

[0013] As a preferred embodiment of the shelf moving and handling system of the present invention, the adaptive grinding robot includes: a three-dimensional scanning unit to acquire three-dimensional morphological data of the weld; a grinding parameter calculation module to calculate grinding pressure, rotation speed and path based on weld reinforcement height, width and material hardness; and a force-controlled grinding head to adjust the grinding pressure in real time to avoid over-grinding or under-grinding.

[0014] As a preferred embodiment of the shelf moving and handling system of the present invention, the grinding parameter calculation module adopts a machine learning model, and the training data includes historical weld morphology data, grinding parameters and post-grinding quality scores, so as to realize intelligent prediction of grinding parameters.

[0015] As a preferred embodiment of the shelving movement and handling system of the present invention, the system further includes a digital twin monitoring platform, which collects sensor data from each device in real time and synchronously simulates the production process in a virtual environment for: a) predicting potential faults and providing early warnings; b) optimizing equipment layout and production cycle time; and c) training operators to simulate operations.

[0016] As a preferred embodiment of the shelving movement and handling system of the present invention, the central control system further includes an energy consumption optimization module, which dynamically adjusts the equipment operation mode based on real-time electricity prices, production task urgency, and equipment load rate. The energy consumption optimization objective function is: ;in, For equipment Real-time power, For runtime, Time-of-use electricity pricing, For equipment The number of start-stop cycles, Energy consumption during start-up and shutdown.

[0017] The beneficial effects of this invention are as follows: This invention not only solves the specific technical problems of unstable quality, low efficiency and insufficient flexibility in traditional shelving manufacturing, but also realizes the upgrade of the manufacturing mode to digitalization, networking and intelligence through system-level intelligent integration and innovation. Through the dynamic scheduling module of the central control system, the actions and rhythm of each link of welding, handling, conveying and grinding are coordinated in real time, breaking the process islands; the dynamic path planning algorithm of the handling robot effectively avoids the risk of interference and collision between multiple devices. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Specifically: Figure 1 is a block diagram of the overall system structure of the shelf moving and handling system of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0023] Referring to Figure 1, a shelf-based mobile handling system is provided, comprising: a welding workstation, including a welding robot, workpiece fixtures, and an adaptive adjustment module for welding parameters; a overhead conveyor system, including multiple independently driven overhead conveyor chains and a dynamic speed control module; a handling robot, configured between the welding workstation and the overhead conveyor system, equipped with a multi-dimensional force sensor and a visual positioning system; a grinding workstation, including an adaptive grinding robot and an online quality inspection system; and a central control system, which is communicatively connected to each subsystem. The central control system includes: a) a dynamic scheduling module, which optimizes the production cycle in real time based on order priority, equipment status, and energy consumption data; b) a quality traceability module, which records the welding parameters, transfer time, and grinding quality data of each workpiece; and c) a collaborative control algorithm, which coordinates the operation sequence of welding, handling, conveying, and grinding.

[0024] The adaptive welding parameter adjustment module adjusts the welding current, voltage, and speed in real time based on the weld visual inspection results and material thickness. Specifically, the adjustment parameters are calculated using the following formula: ; ;in, To adjust the current, For the adjusted voltage, Based on the base current, Based on the base voltage, For material thickness deviation, For weld width deviation, For weld gap deviation, , , This is the adjustment coefficient.

[0025] Specifically, the dynamic speed control module of the overhead conveyor system adjusts the conveying speed in real time according to the load status of the downstream workstation and the priority of the workpiece, giving priority to the flow of high-priority workpieces and avoiding bottleneck workstation blockage.

[0026] The handling robot is equipped with a path planning algorithm. Based on real-time acquisition of workshop equipment positions, other robot motion trajectories, and workpiece dimensions, it calculates the optimal collision-free handling path. The objective function for path planning is: ;in, For handling time, For energy consumption, To avoid obstacles, , , These are the weighting coefficients.

[0027] The adaptive grinding robot includes: a 3D scanning unit to acquire 3D morphological data of the weld; a grinding parameter calculation module to calculate grinding pressure, rotation speed and path based on weld reinforcement height, width and material hardness; and a force-controlled grinding head to adjust grinding pressure in real time to avoid over-grinding or under-grinding.

[0028] Specifically, the grinding parameter calculation module uses a machine learning model, and the training data includes historical weld morphology data, grinding parameters, and post-grinding quality scores to achieve intelligent prediction of grinding parameters.

[0029] Furthermore, the system also includes a digital twin monitoring platform, which collects sensor data from various devices in real time and synchronously simulates the production process in a virtual environment for: a) predicting potential faults and providing early warnings; b) optimizing equipment layout and production cycle time; and c) training operators to simulate operations.

[0030] Furthermore, the central control system also includes an energy consumption optimization module, which dynamically adjusts the equipment operating mode based on real-time electricity prices, production task urgency, and equipment load rate. The energy consumption optimization objective function is: ;in, For equipment Real-time power, For runtime, Time-of-use electricity pricing, For equipment The number of start-stop cycles, Energy consumption during start-up and shutdown.

[0031] A specific implementation of a shelving mobile handling system is provided, with the following system configuration: A welding workstation is equipped with a 6-axis welding robot (model: ABB IRB 6700) and a vision sensing system. The overhead conveyor system is divided into three independently driven sections, each 15 meters long with a maximum load of 200 kg. The handling robot adopts an AGV + robotic arm composite structure with a maximum handling weight of 80 kg. A grinding workstation is equipped with a force-controlled grinding robot (model: KUKA KR 30 HA). The central control system is deployed on an industrial server, running ROS-based scheduling software. The digital twin platform is developed using Unity 3D and synchronizes with the physical system in real time. Workflow and Creative Achievements: Step 1: Intelligent Welding and Parameter Adaptation. After the operator clamps the shelving column workpiece (material Q235, thickness 6 mm) onto the fixture, the vision system scans the weld position, measuring the actual thickness as 6.2 mm (ΔT = +0.2 mm) and the weld gap as 1.5 mm (ΔG = +0.3 mm). The welding parameter adaptive module calculates and adjusts the parameters according to the formula: = 220A × [1 + 0.05×0.2 + 0.03×(0.1)] = 223.3A = 28V × [1 + 0.02×0.3] = 28.17V; The welding robot operates with the adjusted parameters, and compared with welding with fixed parameters, the weld formation quality score is improved from 85 points to 92 points (out of 100 points), and spatter is reduced by 30%.

[0032] Step 2: Collaborative Handling and Path Planning After welding is completed, the handling robot receives a pick-up command. At this time, two other AGVs are running in the workshop. The path planning algorithm of the handling robot calculates the optimal path: Option A: Straight path, shortest distance (8 meters), but may conflict with AGV #1. Option B: Detour path, 10 meters, no risk of conflict. Option C: Wait 5 seconds and then take the straight path. The algorithm calculates the score for each option based on the objective function: Option A: ×12s + ×850J + ×0.5m = 68.5 points. Option B: ×15s + ×1100J + ×0m = 72.3 points. Option C: ×17s + ×900J + ×0m = 70.1 points. Option B, which has the highest score, was selected and executed, successfully avoiding device collision and improving system security.

[0033] Step 3: When the workpiece is mounted on the overhead conveyor with dynamic speed adjustment, the RFID reader records the workpiece number #20240520001, with priority marked as "high". At this time, the grinding workstation is processing the previous batch of workpieces and is expected to receive a new workpiece in 5 minutes. The dynamic speed adjustment module calculates: Normal speed: 2 m / min, arrival time: 7.5 minutes; Low speed: 1 m / min, arrival time: 15 minutes; Mid-term pause followed by acceleration: Total time 6 minutes. Option 3 is selected: the workpiece first runs at 1.5 m / min for 3 minutes, pauses for 2 minutes to wait for the grinding station to prepare, and then runs at 3 m / min for 1 minute to arrive. Compared with a fixed speed, the waiting time is reduced by 1.5 minutes, avoiding workpiece accumulation on the overhead conveyor.

[0034] Step 4: Adaptive intelligent grinding. After the workpiece arrives at the grinding station, the 3D scanning unit acquires the 3D point cloud data of the weld seam and analyzes it to obtain: average residual height: 1.8mm (standard requirement: ≤1.0mm), maximum width: 8mm, hardness: HB 180. The grinding parameter calculation module calls the trained random forest model and outputs recommended parameters: grinding pressure: 25N, spindle speed: 8000rpm, number of reciprocations: 3, feed speed: 300mm / min. The force-controlled grinding head executes according to these parameters. After grinding, the residual height is detected to be 0.9mm, the surface roughness Ra=6.3μm, and the first-pass yield rate is increased from 78% of the traditional method to 95%.

[0035] Step 5: Digital Twin Monitoring and Prediction. Throughout the entire processing, the digital twin platform synchronizes the physical system status in real time. After the system has been running for 4 hours, the twin model, based on the equipment's current fluctuation characteristics and temperature rise trend, predicts that the welding robot's third-axis reducer may experience insufficient lubrication within the next 48 hours. The system issues an early warning, allowing maintenance personnel to perform preventative maintenance at the start of the next shift, avoiding an unplanned downtime of 8 hours.

[0036] Step 6: Energy Optimization. At 2 PM (peak electricity price period), the energy optimization module detected a relatively relaxed production workload and automatically switched non-critical equipment to energy-saving mode: the overhead conveyor speed was reduced by 20%, the welding robot entered a low-power state during standby, and the workshop lighting brightness was adjusted to 70%. Simultaneously, some tasks that could be delayed were scheduled for 9 PM (off-peak electricity price period). Calculations showed that the overall system energy consumption was reduced by 18%, and electricity costs were reduced by 22%.

[0037] The dynamic scheduling module of the central control system coordinates the actions and rhythms of welding, handling, conveying, and grinding in real time, breaking down the silos of processes.

[0038] The system optimizes scheduling based on order priority and real-time equipment status, resulting in a smoother production flow. Implementation verification shows that the system's overall production efficiency has improved. The adaptive welding parameter module adjusts current and voltage in real time through visual feedback, compensating for material and assembly deviations and steadily increasing the first-pass yield of welds. The adaptive grinding robot intelligently generates grinding parameters based on 3D scanned weld morphology data and executes them precisely through force control, significantly improving the consistency of surface quality after grinding and eliminating complete reliance on individual worker experience.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A shelving movement and handling system, characterized in that, include: The welding workstation includes a welding robot, workpiece fixtures, and an adaptive adjustment module for welding parameters; the overhead conveyor system includes multiple independently driven overhead conveyor chains and a dynamic speed control module; the handling robot is configured between the welding workstation and the overhead conveyor system, equipped with multi-dimensional force sensors and a vision positioning system; the grinding workstation includes an adaptive grinding robot and an online quality inspection system; and the central control system is communicatively connected to each subsystem, including: a) a dynamic scheduling module that optimizes production cycle time in real time based on order priority, equipment status, and energy consumption data; b) Quality traceability module, which records the welding parameters, transfer time and grinding quality data of each workpiece; c) Collaborative control algorithm, which coordinates the operation sequence of each link of welding, handling, conveying and grinding.

2. The shelving moving and handling system according to claim 1, characterized in that: The adaptive welding parameter adjustment module adjusts the welding current, voltage, and speed in real time based on the weld visual inspection results and material thickness. Specifically, the adjustment parameters are calculated using the following formula: ； ;in, To adjust the current, For the adjusted voltage, Based on the base current, Based on the base voltage, For material thickness deviation, For weld width deviation, For weld gap deviation, 、 、 This is the adjustment coefficient.

3. The shelving moving and handling system according to claim 2, characterized in that: The dynamic speed control module of the overhead conveyor system adjusts the conveying speed in real time according to the load status of the downstream workstation and the priority of the workpiece, giving priority to the flow of high-priority workpieces and avoiding bottleneck workstation blockage.

4. The shelving moving and handling system according to claim 3, characterized in that: The transport robot is equipped with a path planning algorithm. Based on real-time acquisition of workshop equipment positions, other robot motion trajectories, and workpiece dimensions, it calculates the optimal collision-free transport path. The objective function for path planning is: ;in, For handling time, For energy consumption, To avoid obstacles, 、 、 These are the weighting coefficients.

5. The shelving moving and handling system according to claim 1, characterized in that: The adaptive grinding robot includes: a 3D scanning unit to acquire 3D morphological data of the weld; a grinding parameter calculation module to calculate grinding pressure, rotation speed and path based on weld reinforcement height, width and material hardness; and a force-controlled grinding head to adjust grinding pressure in real time to avoid over-grinding or under-grinding.

6. The shelf moving and handling system according to claim 5, characterized in that: The grinding parameter calculation module uses a machine learning model, and the training data includes historical weld morphology data, grinding parameters, and post-grinding quality scores, to achieve intelligent prediction of grinding parameters.

7. The shelving moving and handling system according to claim 5, characterized in that: The system also includes a digital twin monitoring platform, which collects sensor data from various devices in real time and synchronously simulates the production process in a virtual environment for: a) predicting potential faults and providing early warnings; b) Optimize equipment layout and production cycle time; c) Train operators to perform simulated operations.

8. The shelving moving and handling system according to claim 1, characterized in that: The central control system also includes an energy consumption optimization module, which dynamically adjusts the equipment operating mode based on real-time electricity prices, production task urgency, and equipment load rate. The energy consumption optimization objective function is: ;in, For equipment Real-time power, For runtime, Time-of-use electricity pricing, For equipment The number of start-stop cycles, Energy consumption during start-up and shutdown.

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