Aluminum melting furnace door intelligent lifting control system based on Internet of Things technology

Abstract

The invention discloses an aluminum melting furnace door intelligent lifting control system based on the Internet of Things technology, an aluminum melting furnace comprises an aluminum melting furnace body, a furnace door is slidably installed at one end of the aluminum melting furnace body, the aluminum melting furnace comprises an intelligent monitoring system, and the intelligent monitoring system comprises an industrial camera and a millimeter wave radar. The industrial camera and the millimeter wave radar are both fixedly installed on the furnace door and used for collecting multi-mode sensing data of an operation area in real time. Compared with the prior art, the aluminum melting furnace door intelligent lifting control system based on the Internet of Things technology has the following beneficial effects that (1) through a multi-dimensional target detection and delay control mechanism, a dual safety protection system is established, and the accident rate is reduced; the method comprises the steps of (1) realizing dynamic checking calculation and correction of technological parameters through an Internet of Things platform, improving the smelting efficiency, (3) integrating infrared thermal imaging and visible light fusion detection, improving the recognition accuracy of complex working conditions, and (4) constructing a digital twinborn model, and improving the pre-judgment accuracy of equipment faults.

Description

Technical Field

[0002] The present invention belongs to the technical field of intelligent control, and particularly relates to an intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology. Background Art

[0003] Traditional control systems for aluminum melting furnace doors mostly adopt manual operation or simple mechanical limit control methods, which have the following technical defects: 1) There is a lack of intelligent recognition mechanism when operators enter dangerous areas, which is likely to cause safety accidents; 2) Relying on fixed program control, it is impossible to dynamically adjust lifting parameters according to real-time working conditions; 3) There are blind spots in traditional sensor detection, and multi-dimensional perception of the production environment cannot be achieved; 4) The information island phenomenon of each control unit is serious, and there is a lack of intelligent verification mechanism for process parameters; 5) Most existing systems adopt a single warning method and lack multiple safety interlock protections.

[0004] Therefore, in view of the above technical problems, it is necessary to provide an intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology.

[0005] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology, which can solve the above technical problems.

[0007] To achieve the above purpose, the technical solution provided by a specific embodiment of the present invention is as follows: An intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology, the aluminum melting furnace includes an aluminum melting furnace body, and a furnace door is slidably installed at one end of the aluminum melting furnace body, including: An intelligent monitoring system, the intelligent monitoring system includes an industrial camera and a millimeter-wave radar, both the industrial camera and the millimeter-wave radar are fixedly installed on the furnace door, and are used for real-time collection of multi-modal perception data of the operation area, and analyzing and processing the multi-modal perception data through a target detection algorithm; An Internet of Things platform, the Internet of Things platform is provided with a process knowledge base and a digital twin engine, and performs matching verification of target detection data and process regulations; An intelligent control system, the intelligent control system is integrated with a safety delay module and an audible and visual alarm module, and is configured to start a 10-second countdown control and trigger an audible and visual alarm after receiving an action instruction; An automatic lifting mechanism, the automatic lifting mechanism is equipped with a servo drive unit and a position feedback encoder to achieve millimeter-level lifting precision control.

[0008] In one or more embodiments of the present invention, the target detection algorithm adopts an improved YOLOv5 algorithm and introduces a dynamic false alarm filtering mechanism.

[0009] In one or more embodiments of the present invention, the dynamic false alarm filtering mechanism includes establishing a spatio-temporal model of molten metal splash and using Kalman filtering to track suspected targets.

[0010] In one or more embodiments of the present invention, the process knowledge base stores: A molten aluminum process parameter matrix, including a temperature-time curve and an alloy composition ratio table; A safety operation procedure tree, defining the protection levels under different working conditions; A historical fault case library, recording the abnormal feature vectors of the equipment.

[0011] In one or more embodiments of the present invention, the digital twin engine executes: A three-dimensional point cloud reconstruction algorithm to establish a kinematic model of the furnace door; Finite element thermodynamics simulation to predict the metal liquid level fluctuation; Dynamic path planning to generate an optimal lifting trajectory.

[0012] In one or more embodiments of the present invention, the automatic lifting mechanism includes multiple groups of cylinders. One end of the molten aluminum furnace body is provided with a safety monitoring chamber, and multiple groups of cylinders are fixedly installed inside the safety monitoring chamber. The output ends of multiple groups of cylinders are all fixed to the furnace door; A pair of T-shaped grooves are opened at one end of the molten aluminum furnace body, and a pair of T-shaped blocks are fixedly installed on the end face of the furnace door close to the molten aluminum furnace body. The pair of T-shaped grooves respectively match the pair of T-shaped blocks.

[0013] In one or more embodiments of the present invention, the intelligent control system further includes an intelligent controller, and the intelligent controller is fixedly installed inside the safety monitoring chamber.

[0014] In one or more embodiments of the present invention, the safety delay module includes: A multi-level interlock verification unit to verify the equipment status flag bit; A countdown visualization interface, set on the intelligent controller, to display the remaining operation time; An emergency stop circuit, including an emergency stop button and a wireless remote control interface set on the intelligent controller.

[0015] In one or more embodiments of the present invention, the sound and light alarm module includes a sound and light alarm, and the sound and light alarm is fixedly installed on the industrial camera.

[0016] In one or more embodiments of the present invention, it is matched with the servo drive unit and the position feedback encoder.

[0017] Compared with the prior art, a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology of the present invention has the following beneficial effects: 1) By means of multi-dimensional target detection and delay control mechanism, a dual safety protection system is established to reduce the accident rate; 2) The Internet of Things platform realizes dynamic verification and correction of process parameters to improve the melting efficiency; 3) Integrate infrared thermal imaging and visible light fusion detection to improve the recognition accuracy in complex working conditions; 4) Build a digital twin model to improve the accuracy of equipment fault prediction. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a flowchart of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention; Figure 2 It is a flowchart of the target detection algorithm of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention; Figure 3 It is a system block diagram of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention; Figure 4 It is a structure diagram of an aluminum melting furnace of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention; Figure 5 It is a front view of an aluminum melting furnace of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention; Figure 6 It is of a smart lifting control system for an aluminum melting furnace door based on Internet of Things technology in an embodiment of the present invention Figure 4 Enlarged view at A.

[0020] Main reference numerals description: 10, aluminum melting furnace body; 11, T-shaped groove; 20, safety monitoring bin; 30, furnace door; 31, T-shaped block; 40, intelligent controller; 41, emergency stop button; 50, industrial camera; 51, sound and light alarm; 60, cylinder. Detailed implementation mode

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0022] As Figures 1 - 6 shown, an intelligent lifting control system for the melting aluminum furnace door 30 based on Internet of Things technology in an embodiment of the present invention. The melting aluminum furnace includes a melting aluminum furnace body 10, and a furnace door 30 is slidably installed at one end of the melting aluminum furnace body 10. This system mainly consists of four parts: an intelligent monitoring system, an Internet of Things platform, an intelligent control system, and an automatic lifting mechanism.

[0023] The intelligent monitoring system includes an industrial camera 50 and a millimeter-wave radar. Both the industrial camera 50 and the millimeter-wave radar are fixedly installed on the furnace door 30. The industrial camera 50 adopts a high-temperature protection design and can work stably in a high-temperature environment; the millimeter-wave radar can penetrate the smoke and dust environment for precise detection. The industrial camera 50 and the millimeter-wave radar work together to collect multi-modal perception data of the melting aluminum furnace operation area in real time, including visible light image data and millimeter-wave radar reflection signal data.

[0024] As Figure 2 shown, the intelligent monitoring system uses a target detection algorithm to analyze and process multi-modal perception data. This target detection algorithm adopts an improved YOLOv5 algorithm and introduces a dynamic false alarm filtering mechanism. The improved YOLOv5 algorithm optimizes for the special environment of the melting aluminum furnace on the basis of the original YOLOv5 algorithm, including enhancing the target recognition ability in a high-temperature environment, improving the detection accuracy of the metal reflective surface, and reducing the influence of smoke interference on the detection results. The algorithm has a fast detection speed and can realize real-time detection of personnel, equipment, and abnormal conditions.

[0025] The dynamic false alarm filtering mechanism includes establishing a spatio-temporal model of molten metal splashing and using a Kalman filter to track suspected targets. The spatio-temporal model of molten metal splashing establishes a database of the movement trajectory, speed change, and morphological characteristics of metal splashing by analyzing historical data, and can effectively distinguish metal splashing from personnel or equipment targets. The Kalman filter is used to track suspected targets and continuously evaluate the movement state of the targets. When the movement characteristics of the targets do not match the typical movement patterns of personnel or equipment, the system will classify them as false alarms and automatically filter them. The state transition matrix of the Kalman filter is optimized for the melting aluminum environment, and the process noise covariance matrix is set in a dynamic adjustment mode and can be adaptively adjusted according to the environmental complexity.

[0026] The Internet of Things platform is equipped with a process knowledge base and a digital twin engine, which perform the matching verification of target detection data and process specifications. The process knowledge base stores a molten aluminum process parameter matrix, a safety operation procedure tree, and a historical fault case library. The safety operation procedure tree defines the protection levels under different working conditions, classifies the working conditions into three categories: normal production, equipment maintenance, and emergency situations. Each category has multiple sub-working conditions, and corresponding safety protection levels and operation restrictions are defined for each working condition. The historical fault case library records the abnormal feature vectors of equipment, including the equipment fault data that occurred in the past. Each record includes the fault type, fault feature vector, environmental parameters, treatment methods, and treatment results, providing fault warning and treatment reference for the system.

[0027] The digital twin engine executes a three-dimensional point cloud reconstruction algorithm, finite element thermodynamics simulation, and dynamic path planning. The three-dimensional point cloud reconstruction algorithm processes the data of industrial cameras 50 and millimeter-wave radars to generate a three-dimensional point cloud model of the working environment, and based on this, a kinematic model of the furnace door 30 is established, including parameters such as the mass distribution, friction coefficient, and motion constraints of the furnace door 30. The finite element thermodynamics simulation establishes a thermodynamics model of the molten aluminum process based on the geometric structure, material properties, and process parameters of the molten aluminum furnace, simulates the flow state of the molten metal under different temperatures and different additive conditions, and predicts the fluctuation of the molten metal level, providing a safety reference for the operation of the furnace door 30. The dynamic path planning calculates the optimal trajectory of the lifting of the furnace door 30 based on real-time environment perception and safety rules, considers parameters such as acceleration, deceleration, and maximum speed, generates a smooth motion curve, and avoids the impact on the equipment caused by violent start and stop.

[0028] The intelligent control system integrates a safety delay module and an audible and visual alarm module, and is configured to start a 10-second countdown control and trigger an audible and visual alarm after receiving an action instruction. The safety delay module includes a multi-level interlock verification unit, a countdown visualization interface, and an emergency stop circuit. The multi-level interlock verification unit verifies the equipment status flag bits, including multiple flag bits such as the furnace temperature status, the position status of the furnace door 30, and the surrounding safety status. Only when all flag bits are in a safe state, the lifting operation of the furnace door 30 is allowed. The countdown visualization interface is set on the intelligent controller 40 to display the remaining operation time. It is displayed in large-font LED. During the 10-second countdown, the numbers decrease second by second, and a progress bar is equipped to intuitively display the remaining time. The emergency stop circuit includes an emergency stop button 41 and a wireless remote control interface set on the intelligent controller 40. The emergency stop button 41 is designed with a red mushroom head, and when pressed, the system power source is immediately cut off; the wireless remote control interface supports the remote emergency stop function and uses an encrypted communication protocol to ensure the safety of the instructions.

[0029] The acoustic-optic alarm module includes an acoustic-optic alarm 51, which is fixedly installed on the industrial camera 50. The acoustic-optic alarm 51 integrates a high-decibel alarm and a high-brightness flashing warning light. The warning light uses red-yellow dual-color LEDs, which can effectively remind the surrounding personnel to pay attention to safety in a noisy industrial environment.

[0030] As Figures 4 - 6 shown, the automatic lifting mechanism is equipped with a servo drive unit and a position feedback encoder to achieve millimeter-level lifting precision control. The automatic lifting mechanism includes three groups of cylinders 60. One end of the molten aluminum furnace body 10 is provided with a safety monitoring chamber 20. The three groups of cylinders 60 are all fixedly installed inside the safety monitoring chamber 20, and the output ends of the three groups of cylinders 60 are all fixed to the furnace door 30. A pair of T-shaped grooves 11 are opened at one end of the molten aluminum furnace body 10. A pair of T-shaped blocks 31 are fixedly installed on the end face of the furnace door 30 close to the molten aluminum furnace body 10. The pair of T-shaped grooves 11 respectively match the pair of T-shaped blocks 31 to form a stable guiding structure, ensuring that the furnace door 30 runs smoothly along the predetermined track during the lifting process, preventing deviation and jamming.

[0031] The intelligent control system also includes an intelligent controller 40, which is fixedly installed inside the safety monitoring chamber 20. The intelligent controller 40 adopts an industrial-grade PLC control system, which has high reliability and anti-interference ability, supports multiple communication protocols, and can be seamlessly connected to the factory's upper-level management system. The intelligent controller 40 is equipped with a large-inch touch screen to display the system operation status, parameter settings, and alarm information. The operation interface is humanized designed, which is convenient for workers to quickly get started.

[0032] The three groups of cylinders 60 are matched with the servo drive unit and the position feedback encoder. The position feedback encoder adopts an absolute encoder, which can achieve high control precision of the position, ensuring the precise control of the lifting position of the furnace door 30. The servo system adopts closed-loop control, monitors the position deviation in real time and compensates it, making the lifting process of the furnace door 30 stable and controllable, and avoiding overshoot and oscillation.

[0033] The system working process is as follows: First, the industrial camera 50 and the millimeter-wave radar collect multi-modal perception data of the operation area in real time; then, these data are analyzed and processed through the improved YOLOv5 algorithm and the dynamic false alarm filtering mechanism to identify personnel, equipment, and potential hazards in the operation area; next, the digital twin engine of the Internet of Things platform performs matching calculations based on the target detection results and the regulations in the process knowledge base to determine whether the current conditions meet the operating conditions of the furnace door 30; if the conditions are met, the system generates an operating instruction for the furnace door 30; after receiving the instruction, the intelligent control system starts a 10-second safety countdown and triggers an audible and visual alarm to remind the surrounding personnel to pay attention to safety; after the countdown ends, if there is no emergency stop instruction, the servo drive unit of the automatic lifting mechanism starts to work, and under the precise control of the position feedback encoder, the smooth lifting and lowering of the furnace door 30 is realized; during the whole process, the system continuously monitors the environmental status, and once an abnormality is detected, the safety protection mechanism is immediately triggered.

[0034] Furthermore, in addition to establishing a spatio-temporal model of molten metal splashing and using Kalman filtering to track suspected targets, the dynamic false alarm filtering mechanism also adds a target classification and verification module based on deep learning. This module adopts the ResNet50 network structure to perform secondary classification and verification on the detected targets, further reducing the false alarm rate. The spatio-temporal model has also been optimized by adding a trajectory prediction algorithm based on physical laws, which can more accurately distinguish metal splashing from normal targets.

[0035] Furthermore, in addition to storing the molten aluminum process parameter matrix, safety operation procedure tree, and historical fault case library, the process knowledge base also adds an equipment maintenance knowledge graph and an energy consumption optimization model. The equipment maintenance knowledge graph adopts a graph database structure to record information such as the association relationships between components of the equipment, maintenance cycles, and standards for replacing vulnerable parts, providing decision-making support for preventive maintenance. The energy consumption optimization model is based on historical operation data and establishes a relationship model between the operation of the furnace door 30 and energy consumption, which can automatically calculate the optimal operation timing and method of the furnace door 30 according to production requirements, reducing unnecessary heat loss.

[0036] Furthermore, in addition to performing three-dimensional point cloud reconstruction algorithms, finite element thermodynamics simulations, and dynamic path planning, the digital twin engine also adds functions for equipment health status assessment and predictive maintenance. The equipment health status assessment is based on multi-sensor fusion technology, collects multi-dimensional data such as equipment vibration, temperature, and current, and establishes an equipment health status model through machine learning algorithms to evaluate the health status of each component of the equipment in real time. The predictive maintenance function is based on the results of the equipment health status assessment, combines historical maintenance data and failure mode analysis to predict the possible failure time and type of the equipment, and arranges the maintenance plan in advance to avoid unexpected shutdowns.

[0037] The 3D point cloud reconstruction algorithm has also been upgraded. By adopting the deep learning-assisted point cloud registration technology, the accuracy and speed of point cloud stitching have been improved. The finite element thermodynamics simulation has added the multi-physics field coupling analysis function, which can simultaneously consider the mutual influence of heat conduction, fluid flow, and structural deformation, making the simulation results closer to the actual situation. The dynamic path planning algorithm has introduced the reinforcement learning technology. Through continuous learning and optimization, a smoother and more efficient motion trajectory is generated.

[0038] Furthermore, the safety delay module of the intelligent control system has enhanced the function of the multi-level interlock verification unit. In addition to verifying the device status flag bits, it has also added the operation permission verification and operation rationality evaluation. The operation permission verification uses face recognition or RFID card recognition technology to confirm that the operator has the corresponding operation permission. The operation rationality evaluation is based on the current production status and process requirements to judge the necessity and rationality of the furnace door 30 operation, avoiding unnecessary operations that may cause energy waste or safety risks.

[0039] Furthermore, the countdown visual interface has been upgraded to a multimedia interaction interface. In addition to displaying the remaining operation time, it also shows the current environmental status, safety inspection results, and operation suggestions, providing more comprehensive information support for the operator. The emergency stop circuit has added a voice control function. The operator can use specific voice commands to urgently stop the system operation, which is applicable to emergency situations where both hands are unable to operate the button.

[0040] Furthermore, the audible and visual alarm module has been upgraded to a multi-level intelligent alarm system. This system automatically adjusts the alarm method and intensity according to the danger level, including a three-level alarm mechanism: the first-level alarm is a reminder level, using a green indicator light and a low-volume reminder sound to remind that the operation is about to start; the second-level alarm is a warning level, using a yellow flashing light and a medium-volume alarm sound to warn personnel to pay attention to safety; the third-level alarm is an emergency level, using a red fast-flashing light and a high-volume alarm sound to require personnel to immediately evacuate the dangerous area.

[0041] In addition to the main alarm fixed on the industrial camera 50, multiple distributed alarms have been added around the operation area for the audible and visual alarm 51, forming an all-round alarm coverage. The alarm system is also connected to the factory's public address system and personal mobile devices, and can push alarm information through the broadcast and mobile phone APP to ensure that all relevant personnel can receive safety reminders in a timely manner.

[0042] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0043] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An intelligent lifting control system for the melting aluminum furnace door based on Internet of Things technology. The melting aluminum furnace includes a melting aluminum furnace body, and a furnace door is slidably installed at one end of the melting aluminum furnace body. It is characterized in that, Including: An intelligent monitoring system, which includes industrial cameras and millimeter-wave radars. Both the industrial cameras and millimeter-wave radars are fixedly installed on the furnace door, used to collect multi-modal perception data of the operation area in real time, and analyze and process the multi-modal perception data through target detection algorithms; An Internet of Things platform, which is provided with a process knowledge base and a digital twin engine to perform matching verification between target detection data and process regulations; An intelligent control system, which integrates a safety delay module and an audible and visual alarm module, and is configured to start a 10-second countdown control and trigger an audible and visual alarm after receiving an action instruction; An automatic lifting mechanism, which is equipped with a servo drive unit and a position feedback encoder to achieve millimeter-level lifting precision control.

2. The intelligent lifting control system for the molten aluminum furnace door based on the Internet of Things technology according to claim 1, wherein The target detection algorithm adopts an improved YOLOv5 algorithm, introducing a dynamic false alarm filtering mechanism.

3. The intelligent lifting control system for the molten aluminum furnace door based on the Internet of Things technology according to claim 2, wherein The dynamic false alarm filtering mechanism includes establishing a spatio-temporal model of molten metal splash and using Kalman filtering to track suspected targets.

4. The intelligent lifting control system for the molten aluminum furnace door based on the Internet of Things technology according to claim 1, wherein, The process knowledge base stores: A molten aluminum process parameter matrix, including a temperature-time curve and an alloy composition ratio table; A safety operation procedure tree, defining the protection levels under different working conditions; A historical fault case library, recording the abnormal feature vectors of the equipment.

5. The intelligent lifting control system for the molten aluminum furnace door based on the Internet of Things technology according to claim 1, wherein, The digital twin engine executes: A three-dimensional point cloud reconstruction algorithm to establish a kinematic model of the furnace door; Finite element thermodynamics simulation to predict the metal liquid level fluctuation; Dynamic path planning to generate an optimal lifting trajectory.

6. The intelligent lifting control system for the molten aluminum furnace door based on the Internet of Things technology according to claim 1, wherein, The automatic lifting mechanism includes multiple groups of cylinders. One end of the molten aluminum furnace body is provided with a safety monitoring chamber. Multiple groups of cylinders are fixedly installed inside the safety monitoring chamber, and the output ends of multiple groups of cylinders are fixed to the furnace door; A pair of T-shaped grooves are opened at one end of the molten aluminum furnace body. A pair of T-shaped blocks are fixedly installed on the end face of the furnace door close to the molten aluminum furnace body. The pair of T-shaped grooves respectively match the pair of T-shaped blocks.

7. An intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology according to claim 6, characterized in that, The intelligent control system further includes an intelligent controller, which is fixedly installed inside the safety monitoring chamber.

8. An intelligent lifting control system for a molten aluminum furnace door based on Internet of Things technology according to claim 7, characterized in that, The safety delay module includes: A multi-level interlock verification unit to verify the equipment status flag bit; A countdown visualization interface, which is set on the intelligent controller to display the remaining operation time; An emergency stop circuit, including an emergency stop button and a wireless remote control interface set on the intelligent controller.

9. An intelligent lifting control system for an aluminum melting furnace door based on Internet of Things technology according to claim 1, characterized in that, The audible and visual alarm module includes an audible and visual alarm, which is fixedly installed on the industrial camera.

10. An intelligent lifting control system for the melting aluminum furnace door based on the Internet of Things technology according to claim 1, characterized in that, It matches with the servo drive unit and the position feedback encoder.

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