Robot unit, method for controlling metal products moving in a device, and method for manufacturing metal reels

Abstract

A robotic unit (10) for automatically controlling a moving metal product (P) and an associated computerized method are configured for manipulating or cutting parts of said metal product (P). The invention also relates to an apparatus (100) and a method for manufacturing metal reels (200).

Description

[Technical Field]

[0001] The present invention relates to a robotic unit and related methods for controlling moving metal products, in particular moving metal products such as rods and wires, in industrial processes, particularly in metal reel manufacturing, starting from but not limited to hot rolling processes, the robotic unit being configured to identify, control, manipulate and possibly remove parts of the product that may cause problems or cobbles, or be used for simple sampling.

[0002] The present invention also relates to an apparatus including the robot unit described above, and an associated method for the production of metal reels. [Background technology]

[0003] Apparatus is known for producing metal reels in which hot-rolled metal products, such as smooth or ribbed rod or wire, are fed continuously or semi-continuously (e.g., in a "billet-to-billet" feed) to a winding unit where a laying or coil-forming head forms a continuous series of coils that are longitudinally staggered onto a continuous transport conveyor (usually a rollerway) where the coils are cooled.

[0004] The product thus positioned finishes its run in the reel forming unit and is dropped into a well containing a trolley with a centering rod in the center and on which the coils are stacked one above the other to form the skein / spool.

[0005] It is known that the first and / or last part of the product may not be formed correctly. It is therefore necessary to check the condition of at least the last coil, or more generally the condition of the last part of the product, while the product is progressing on the roller ways, to ensure that the automatic reel forming unit can operate without risk of being blocked on the roller ways along the continuous transport conveyor.

[0006] Coil jamming along the roller ways or in the forming unit is one of the most common problems and must be avoided as it can lead to deformation of the hank, plant shutdowns, damage to the structure, increased maintenance interventions, and, if operators are present, a high risk of accidents.

[0007] In conventional rolling plants, the last portion of the advancing product is manually controlled by one or more operators monitoring the terminal zone of the roller way through which the product passes at a temperature of approximately 500°C. Difficulties can arise when manually cutting off the tail portion of a moving product due to the high relative speed of the roller way to the operator, the temperatures involved, and the weight of the product portion being removed from the roller way, as well as product diameter and other factors, particularly when considering sampling and quality control.

[0008] Automated systems have been proposed to identify, cut, and remove the final portion of a product during processing. However, these systems cannot reliably distinguish the final portion from the previous homogeneous portion, nor can they accurately distinguish whether the final portion has defects or a location / shape that could cause subsequent problems during reeling. As a result, the final portion is often removed, often resulting in more of the final portion than is actually needed, resulting in reduced plant productivity and increased processing waste. Alternatively, the final portion may not be recognized and removed, resulting in the creation of knots or substandard reels.

[0009] WO 2017 / 082908 relates to rolling mills in general for producing hot-rolled products, and in particular to trimming and removing the leading and trailing ends of such products and correctly positioning the new ends after trimming. WO 2017 / 082908 does not provide a way to identify the presence of defects in the ends, heads, or tails of such products. Thus, WO 2017 / 082908 does not provide a system that can optimize the process, taking into account that, as noted above, the removed portion of the product is often greater than the amount actually needed.

[0010] Other examples of methods and devices for cutting the leading and trailing ends of metal wire that is subsequently wound onto a reel are described in U.S. Patent No. 4,995,251 and U.S. Patent No. 3,756,289. Neither U.S. Patent No. 4,995,251 nor U.S. Patent No. 3,756,289 mentions defect detection, and therefore suffer from the same drawbacks as WO 2017 / 082908.

[0011] Korean Patent Publication No. 2019-0032908 and the paper by BLUG A. et al., "On the potential of current CNN cameras for industrial surface inspection," relate to the detection of microscopic surface defects, rather than macroscopic shape defects or cobble defects, in the manufacturing of metal products using artificial intelligence.

[0012] JP 07-98217 A relates to an apparatus for identifying defects in three-dimensional objects, which can be installed in a production line for the three-dimensional objects.

[0013] Thus, none of these documents allows the operation of an apparatus and method for the manufacture of metal reels to be optimized in its function of selectively removing the head or tail of the metal product being worked.

[0014] Therefore, there is a need to provide a robotic unit and method for controlling moving metal products, and an apparatus and method for manufacturing metal reels, that overcomes at least one of the drawbacks of the prior art.

[0015] One object of the present invention is to provide a robotic unit capable of carrying out quality control on a product in process, in particular on at least one of its final parts, thereby reducing the false identification of defects and therefore waste due to excessive removal of material.

[0016] Another object of the present invention is to provide a computerized method for managing robotic units, preferably according to a self-learning logic.

[0017] Another object of the present invention is to create an apparatus for manufacturing metal reels that is more productive and requires fewer plant shutdowns than apparatus known in the prior art.

[0018] Another object of the present invention is to provide a method for manufacturing metal reels that is particularly efficient and allows automated, on-the-fly control of the product being processed.

[0019] Applicant has invented, tested and embodied the present invention to overcome the shortcomings of the existing art and to obtain these and other objects and advantages. Summary of the Invention

[0020] The invention is set forth and characterized in the independent claims, while the dependent claims describe other features of the invention or variants of the main inventive idea.

[0021] Corresponding to the above mentioned objects, a robotic unit for controlling a moving metal product, preferably a rod or a hot rolled wire, comprises at least one robotized articulated arm and an acquisition and control unit including a video monitoring system and a processing system configured to acquire and process a sequence of frames of said metal product and to send actuation signals to said robotized articulated arm in order to manipulate or cut a part of said metal product.

[0022] According to one aspect of the present invention described above, the processing system implements artificial intelligence configured to at least a) recognize a terminal portion of the metal product, and b) identify whether the terminal portion has a macroscopic defect, such as a shape defect or a cobble defect.

[0023] The processing system can further selectively manage the movement of the robotized articulated arm (manipulation, repositioning, or cutting with possible subsequent repositioning) in a substantially linear movement along the feed direction of the metal product based on the identified defects.

[0024] This allows for automated targeted manipulation of the workpiece only if there is a defect in the tail, and in this case, specific manipulations can be performed depending on the type of defect, thereby optimizing the robot unit, the devices to which it is attached, and the corresponding methods, thereby reducing waste during processing and improving the quality of the final product.

[0025] According to another aspect of the present invention, the robotic articulated arm can be moved preferably in at least six degrees of freedom, more preferably seven degrees of freedom, thereby allowing manipulation of metal products, particularly but not limited to their ends, regardless of their position, shape, or configuration.

[0026] According to another aspect of the invention, the video monitoring system includes at least one video camera located on a fixed support, on the robotic articulated arm, or on the manipulating head of the arm, which allows for on-the-fly imaging of products, i.e., tracking of the products, thereby improving image quality and accuracy.

[0027] According to another aspect of the present invention, the robot unit further comprises an interception member, the interception member being vertically movable between a rest position below the surface of movement of the metal product and not interfering with the metal product, and an interception position incident on the surface of movement to temporarily stop a portion of the metal product intended to be manipulated / cut by the robotic articulated arm, the member permitting stretching of the metal product coil to facilitate grasping, manipulation, possible cutting, and extraction of the portion of the product.

[0028] The invention also relates to a computerized method for controlling a moving metal product, preferably a rod or a hot rolled wire, comprising acquiring by means of a video monitoring system of an acquisition and control unit a sequence of frames of said metal product processed by a processing system, transmitting actuation signals to a robotized articulated arm and driving said robotized articulated arm based on said actuation signals.

[0029] According to one aspect of the present invention, the method provides for artificial intelligence implemented in the processing system to at least a) recognize the edge of the metal product, and b) identify whether the edge has a macroscopic defect, such as a shape defect or a cobble defect.

[0030] According to another aspect of the invention, based on the type of defect detected, the robotized articulated arm is moved to reposition the tail portion on the fly by means of its gripping tool or moved to cut the tail portion on the fly by means of its cutting tool.

[0031] According to another aspect of the invention, the artificial intelligence has a recognition algorithm for identifying a first point and a second point for each frame of the sequence of frames, the first point and the second point defining a control zone between them, and identifying the frame as either a center frame, a tail frame, or an empty frame of the metal product based on the width and position of the control zone, thus obtaining a macroscopic identification of the entire length of the product.

[0032] According to another aspect of the invention, the artificial intelligence comprises a convolutional neural network comprising: an input level or layer receiving each frame of the sequence of frames; one or more convolutional levels or layers capable of identifying macroscopic geometric and visual characteristics of the metal product; at least one fully connected level configured to classify the frames based on information received from at least one of the one or more convolutional levels or layers; and an output level or layer defined by a binary class vector comprising a first class and a second class, both classes characterized by respective confidence indices for the absence and presence of defects.

[0033] According to another aspect of the invention, if the confidence index associated with the second class exceeds a predetermined threshold, the convolutional network establishes that the analyzed frame is associated with a metal product having a defect and generates a specific actuation signal to send to the robotized articulated arm for correction of the defect.

[0034] According to another aspect of the invention, the at least one fully connected level classifies each of the frames into a class of belonging comprising tail frames with shape defects and tail frames with cobble defects based on the information received from at least one of the one or more convolutional levels or layers. The present invention also relates to an apparatus for producing metal reels, the apparatus comprising: a winding unit having a laying head and configured to receive a metal product in a linear form and form it into a coil, an automatic reel-forming unit capable of receiving the coiled metal product and forming it into a reel, and a roller way configured to move the coiled metal product from an initial end corresponding to a position where the winding unit is located to a terminal end corresponding to a position where the automatic reel-forming unit is located.

[0035] According to one aspect of the present invention, the apparatus further includes a robotic articulated arm disposed at a position on the roller way that is included between the starting end and the ending end.

[0036] One aspect of the present invention relates to a method for producing metal reels, in which a winding unit receives a metal product in wire form, a laying head forms it into a coil, an automatic reel-forming unit receives the coiled metal product and forms a reel, and a roller way moves the coiled metal product from a start point corresponding to a position where the winding unit is located to a finish point corresponding to a position where the automatic reel-forming unit is located.

[0037] According to one aspect of the invention, in the method, a robotic articulated arm moves laterally to a position included between the starting end and the ending end of the roller way to manipulate, reposition, or cut the metal product. These and other aspects, features and advantages of the present invention will be explained, by way of non-limiting example, in the following illustrative embodiments taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0038] [Figure 1] 1 is a perspective view of a robotic unit for controlling moving metal products according to the present invention; FIG. [Figure 2] FIG. 1 is an enlarged detail view showing an interception member capable of intercepting a moving metal product. [Figure 3] FIG. 1 is a schematic diagram illustrating the operation of artificial intelligence to identify defects that may be present in a metal product and its tail. [Figure 4] 1 is a top view of an apparatus for manufacturing metal reels according to the present invention; [Figure 5] 1 is a side view of an apparatus for manufacturing metal reels according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] It must be made clear that the phraseology and terminology used in this specification, and the figures of the accompanying drawings similarly described, have the sole function of better illustrating and explaining the invention, the scope of which is defined by the claims, and that their function is to provide a non-limiting example.

[0040] To facilitate understanding, the same reference numbers have been used wherever possible in the drawings to identify identical common elements. It will be understood that elements and features of one embodiment may be suitably combined with or incorporated into other embodiments without further description.

[0041] With reference to FIG. 1, this figure shows a robot unit 10 for controlling a metal product P according to the present invention, which may be a smooth or ribbed rod or a metal wire moving in a substantially straight feed direction X.

[0042] The metal product P is laid down in a feed direction X on the basis of a series of coils arranged in an offset configuration using a machine of known type, for example a coil forming head.

[0043] The metal products P are moved on a movement plane Z which is substantially horizontal, but which may possibly be inclined upwards or downwards, for example in the feed direction X.

[0044] After its movement on the plane of movement Z, the metal products P can be packed into reels or skeins 200 .

[0045] The unit 10 comprises at least one robotic articulated arm 11 having an operating head 12 each equipped with a gripping tool 13 configured to manipulate the metal product P and a cutting tool 14 for cutting the metal product P, i.e. for separating a specific portion of the metal product P.

[0046] This particular portion may be the terminal or tail portion P1 (see FIG. 3) or, for example, a sample portion located at a position comprised between the tail portion P1 and the initial or head portion P2 of the metal product P. Between the head portion P2 and the tail portion P1 is a central portion P3 (see FIG. 3) having a uniform, laterally aligned, coiled, substantially planar structure.

[0047] The robot articulated arm 11 can be moved preferably according to six degrees of freedom, but can also be moved according to an additional seven degrees of freedom. For this purpose, the robot unit 10 can be provided with a slider or rail 15, for example parallel to the feed direction X, on which the arm 11 is mounted to slide, so that the arm 11 can follow the product P to be processed.

[0048] The robot unit 10 may also be provided with an interception member 23 (see Figure 2) that is vertically movable between a rest position located below the plane of movement Z of the metal product P and not interfering with the metal product P, and an interception position that is incident on the plane of movement Z to hold a coil of metal product P that is intended to be manipulated / cut by means of the robot articulated arm 11.

[0049] The robot unit 10 may also comprise a depositing unit 24 with an arm 24a on which the removed portion of the metal product P is deposited.

[0050] The robot unit 10 has an acquisition and control unit equipped with a video monitoring system 18 for acquiring video sequences and / or sequences of digital images or frames, in particular sequences of frames 19 of the metal product P being processed (see Figure 3).

[0051] The sequence of frames 19 may be continuous or may be acquired at regular or irregular intervals.

[0052] The video monitoring system 18 may comprise at least one video camera 18a, preferably located on the robotic articulated arm 11 (see Figures 1 and 2).

[0053] The video camera 18a may be positioned on the scanning head 12 and point in a direction substantially perpendicular to the feed direction X.

[0054] In this case, the sequence of frames 19 may be acquired "with tracking" through both the inherent mobility of the robot articulated arm 11 and the translational movement of the arm 11 along the rails 15, if provided.

[0055] Alternatively, the video camera 18a can be positioned along or on the plane of movement Z in a fixed position relative to the metal workpiece P being processed so as to guide the robot articulated arm 11.

[0056] The acquisition and control unit also includes a processing system 21 that is connected to the video monitoring system 18 (see FIG. 3).

[0057] The processing system 21 processes the sequence of frames 19 and sends actuation signals S to the arm 11 to manipulate, reposition and / or sever the tail portion P1 or the sample portion.

[0058] The processing system 21 implements artificial intelligence configured to at least (a) recognize the termination P1 and (b) identify whether the termination P1 is defective.

[0059] The defects in question may be of the macro type, i.e., shape defects, geometric defects or cobble defects, independent of microscopic surface defects, where a portion of the metal product P may have a configuration that "exits" from the moving surface Z.

[0060] The artificial intelligence is therefore able to identify a particular section of the metal product P along its length in the feed direction X in accordance with the presence of a defect at its end P1.

[0061] This artificial intelligence is therefore particularly optimized since it is first able to very quickly identify the sequence of frames 19 containing the portion of interest, for example the end portion P1, and subsequently to recognize the presence of possible macroscopic defects.

[0062] Compared to prior art solutions, these operations need to be performed almost instantaneously, since the detection of the defect is followed by the actuation of the arm 11 (operation, repositioning, or cutting with possible subsequent repositioning) advantageously without interruption or slowdown.

[0063] Furthermore, the macroscopic analysis performed by the artificial intelligence of the present invention presupposes a "global" comparison along the entire length of the metal product P and therefore cannot be assimilated to a microscopic surface inspection.

[0064] According to a possible implementation, the artificial intelligence as described above may also be configured to c) classify the type of defect, for example a shape defect or a cobble defect.

[0065] In the particular case of shape defects, the processing system 21 is configured to identify the position and geometrical characteristics of the terminal portion P1 relative to the homogeneous portion P3, and to recognize possible variations in position relative to the homogeneous portion P3, for example.

[0066] The processing system 21 may include a CPU 21a and a commercially available storage module 21b that may be connected to the programmable CPU 21a, such as, for example, a random access memory (RAM), a read-only memory (ROM), a floppy disk, a hard disk (HARD DISK), non-volatile memory (NOR FLASH and NAND FLASH), mass memory, or other form of digital storage or electronic database.

[0067] The storage module 21b can store one or more artificial intelligence based recognition algorithms, such as, for example, neural networks, SVMs (support vector machines), neuro-fuzzy networks, genetic algorithms, and the like.

[0068] The CPU 21a may be any type of microprocessor or processor capable of executing the recognition algorithms described above.

[0069] In a possible embodiment, to take advantage of the level of evolution and sophistication reached by image recognition neural networks, algorithms based on state-of-the-art deep convolutional networks (CNN or DCNN) designed for image classification can be stored in the storage module 21b. These architectures typically consist of one or more convolutional layers and can end with a fully connected layer module. The cascaded application of convolutional layers allows for the extraction of local features of the acquired image at various scale levels. The convolutional neural network then processes the starting image to provide an alternative representation of it. Finally, this output can be processed and used in multiple tasks, such as image classification and object recognition.

[0070] Examples of this type of architecture are for example EfficientNet, MobileNet, ResNet, or VGG16.

[0071] In the embodiment described here, the artificial intelligence is implemented on a storage module 21b comprising a convolutional neural network 25 comprising an input level or layer 26 which receives each time a frame 19a, 19b, 19c of a sequence of frames 19, represented as a matrix of pixels, that the convolutional neural network 25 has to analyze.

[0072] The convolutional neural network 25 also includes one or more convolution levels 27 that are capable of identifying visual features, such as curves, lines, edges, etc., depicted in the frames 19a, 19b, 19c.

[0073] Frames 19a, 19b, 19c are then processed sequentially by the layers of the convolutional network: each filter of the nth convolutional level 27, which can be identified as a scalar matrix containing numerical values ​​or weights, is applied to all sectors that make up the output of the previous level, according to a predetermined step, to obtain a filtered image, also known to those skilled in the art as an activation map.

[0074] Between the nth convolution level 27 and the nth+1 convolution level 27, there may be nonlinear levels or layers whose function is to introduce nonlinearity into the system that is computing a substantially linear operation during the convolution level 27. After some nonlinear levels, there may be so-called "pooling" levels or layers. There may be additional regularization layers, such as so-called "batch normalization" layers.

[0075] The possibility of composing multiple functional components of convolutional networks allows for a wide variety of possible architectures. It is common practice to use architectures studied in the literature that demonstrate good predictive capabilities on similar tasks (e.g., EfficientNet-type architectures).

[0076] The convolutional neural network 25 comprises at least one fully connected level or layer 28 downstream of the convolutional layer 27 and is configured to classify the filtered image based on information received from the last previous layer. The fully connected layer 28 comprises a series of nodes trained to recognize whether the metal product P identified in the filtered image has a defect.

[0077] Downstream of the fully connected level 28 is an output level 29 defined by a binary class vector containing a first class ("defective product") and a second class ("good product"), each characterized by a reliability index PD1 and PD2, respectively.

[0078] If the confidence index PD2 for the second class exceeds a predetermined threshold PD, the convolutional neural network 25 establishes that the just analyzed frame 19a, 19b, 19c contains a defective metal product P and generates and sends a specific actuation signal S to the robot articulated arm 11 to correct the defect.

[0079] The convolutional neural network 25 is first trained in a manner known per se using a "training set" containing sample images showing different configurations of the coils that characterize the metal product P. In this way, the weights of the convolutional network 25 will be appropriately trained. If the convolutional network 25 even partially replicates an architecture known in the literature (e.g., ResNet, EfficientNet), it is possible to train the network weights starting from a network configuration trained on a different dataset, usually trained on a larger dataset by a technique known as "transfer learning".

[0080] According to one embodiment, before transmitting the frames 19a, 19b, 19c to the input level 26 of the convolutional neural network 25, a recognition algorithm 30 can be applied in advance, which has the function of identifying one or more frames 19b of the series of frames in which the tail, i.e. the end, of the metal product P is visible.

[0081] In particular, the recognition algorithm 30 can recognize a first point A and a second point B on each frame 19a, 19b, 19c of the series of frames 19, which define a control strip therebetween, and according to the width and position of the control strip, can identify whether the frame in question is a central frame 19a or a tail frame 19b of the metal product P, or an empty frame 19c.

[0082] The central frame 19a is a frame in which the central portion P3 is visible, the tail frame 19b is a frame in which the end portion P1 is visible, and the empty frame 19c is a frame in which the metal product P has already been completed and is therefore invisible.

[0083] A reference system (x,y) can be associated with each frame, where the abscissa x is parallel to the feed direction X of the metal product P and the ordinate y is perpendicular to the feed direction X. Points A and B, shown as dotted lines in Figure 3, are projected onto the abscissa x and define a control strip between them.

[0084] Only tail frames 19b can be analyzed by the convolutional neural network 25. Alternatively, the convolutional neural network 25 can be configured to accept any frame 19a, 19b, 19c at input, and recognition can be performed directly within it.

[0085] According to one possible embodiment, the convolutional neural network 25 may comprise an additional fully connected level configured to classify the filtered images into their respective classes, including tail frames 19b with shape defects and tail frames 19b with cobble defects, based on the information received from the last previous level.

[0086] According to some embodiments, both during the training step of the convolutional neural network 25, and possibly thereafter, a verification process is provided in which an operator records: a) the type of frames 19a, 19b, 19c, b) for tail frames 19b, whether there are defects, and c) for tail frames 19b that have defects, the type of defect.

[0087] According to some embodiments, the processing system 21 may include an automation module 21c associated with the arm 11 and configured to receive the information processed by the convolutional neural network 25 and manage the movement of the robotic articulated arm 11 as needed.

[0088] In the event that the convolutional neural network 25 identifies a defect in the metal product P being processed, the movement of the robot articulated arm 11 This may include repositioning the tail portion P1 to align with the central portion P3 by means of a gripping tool 13 in the case of a shape defect, or cutting off the tail portion P1 by means of a cutting tool 14 in the case of a cobble defect.

[0089] The acquisition and control unit can also be automatically or manually managed to command the movements of the robotic articulated arm 11 to perform sampling of the metal product P. In this case, the recognition algorithm 30 can be used to identify the central frame 19a corresponding to the central portion P3 along which the sample portion is to be taken.

[0090] 4-5, in some embodiments, an apparatus 100 for producing a metal reel 200 from, for example, a hot rolled metal product P is shown.

[0091] The apparatus 100 includes a winding unit 110 having a winding head 111 and configured to receive linear metal products P and form them into coils, and an automatic reel forming unit 113 capable of receiving the metal products P formed into coils and grouping them into a reel 200.

[0092] The apparatus 100 comprises a roller way 112 or cooling belt on which the coiled metal product P is placed. The roller way 112 has a starting end 112b corresponding to where the winding unit 110 is located and a terminal end 112b corresponding to where the automatic reel forming unit 113 is located.

[0093] The roller way 112 defines the movement plane Z of the metal product P and can have an integrated cooling system to bring the temperature of the metal product P from about 1000°C at the exit from the winding unit 110 to about 500°C at the end of the roller way 112.

[0094] The apparatus 100 includes a robot unit 10 disposed at a position included between a start end 112 a and a finish end 112 b of a roller way 112 .

[0095] The robot unit 10 is disposed on the side of the roller way 112 so that the robot articulated arm 11 can cooperate with the processing of the metal product P.

[0096] The rails 15 of the robot unit 10 can be arranged parallel to the roller ways 112 .

[0097] The interception member 23 can be configured to be inserted between the rollers of the roller way 112 to intercept one or more coils that the robotic articulated arm 11 must manipulate or cut.

[0098] Some embodiments also relate to a method for manufacturing a metal reel 200, in which a winding unit 110 receives a linear metal product P, a laying head 111 forms it into a coil, an automatic reel forming unit 113 receives the coiled metal product P and forms the metal reel 200, and a roller way 112 moves the coiled metal product P from a starting end 112a to a terminal end.

[0099] The robot unit 10 operates at positions included between the starting end 112a and the ending end 112b to manipulate, reposition or cut the metal product P as previously described.

[0100] It will be apparent that modifications and / or additions of components may be made to the robotic unit and method for controlling moving metal products and the apparatus and method for manufacturing metal reels described hereinabove without departing from the field and scope of the present invention.

[0101] It is also clear that although the invention has been described with reference to some particular examples, a person skilled in the art can certainly realise many other equivalent forms of robotic units and methods for controlling moving metal products, and many other equivalent forms of devices and methods for producing metal reels, which have the features set out in the claims and therefore all fall within the field of protection defined by the claims.

[0102] In the following claims, references in parentheses have the sole purpose of improving readability and shall not be considered as limiting factors with regard to the field of protection defined by the same claims.

Claims

1. A robotic unit (10) for controlling a moving metal product (P), preferably a rod or a hot rolled wire, comprising: At least one robotic articulated arm (11); an acquisition and control unit, a video monitoring system (18); a processing system (21) configured to acquire and process a sequence of frames (19) of the metal product (P) and to send actuation signals (S) to the robotized articulated arm (11) in order to manipulate or cut a portion of the metal product (P); an acquisition and control unit including: The processing system (21) includes at least a) recognizing the tail portion (P1) of the metal product (P); b) configured to identify whether the tail portion (P1) has macroscopic defects or not; We will implement artificial intelligence that can the processing system (21) is configured to selectively manage movement of the robotized articulated arm (11) based on the identified defects; A robot unit (10) characterized in that, based on the type of defect detected, the robotized articulated arm is moved to reposition the tail part (P1) on the fly by means of its gripping tool (13) or to cut the tail part (P1) on the fly by means of its cutting tool (14).

2. 2. The robot unit (10) according to claim 1, characterized in that the robotized articulated arm (11) can be moved in at least six degrees of freedom.

3. 2. The robot unit (10) according to claim 1, characterized in that the video monitoring system (18) comprises at least one video camera (18a) located on the operating head (12) of the robotized articulated arm (11).

4. 2. The robot unit (10) according to claim 1, characterized in that the video monitoring system (18) comprises at least one video camera (18a) located on a fixed support.

5. Further comprising an interception member (23), 2. The robot unit (10) according to claim 1, characterized in that the interception member is vertically movable between a rest position, located below the plane of movement (Z) of the metal product (P) and not interfering with the metal product, and an interception position, where it enters the plane of movement (Z) to temporarily stop a part of the metal product (P) intended to be manipulated / cut by the robotized articulated arm (11).

6. 1. A computerized method for controlling a moving metal product (P), preferably a rod or a hot rolled wire, comprising: acquiring, by means of a video monitoring system (18) of an acquisition and control unit, a sequence (19) of frames of said metal product (P) to be processed by a processing system (21); Sending an actuation signal (S) to a robotic articulated arm (11) and driving the robotic articulated arm (11) based on the actuation signal (S); The method comprises at least a) recognizing the tail portion (P1) of the metal product (P); b) identifying whether the tail portion (P1) has macroscopic defects; 10. A method according to claim 9, wherein said robotic articulated arm is provided with an artificial intelligence implemented in said processing system (21) such that, based on the type of defect detected, said robotic articulated arm is moved to reposition said tail part (P1) on the fly by means of its gripping tool (13) or to cut said tail part (P1) on the fly by means of its cutting tool (14).

7. 7. The method according to claim 6, wherein the artificial intelligence has a recognition algorithm (30) for identifying a first point (A) and a second point (B) for each frame (19a, 19b, 19c) of the sequence of frames (19), the first point (A) and the second point (B) defining the area between them as a control zone, and identifying the frame as either a central frame (19a) or a tail frame (19b) of the metal product (P), or an empty frame (19c), based on the width and position of the control zone.

8. The artificial intelligence comprises a convolutional neural network (25); The convolutional neural network (25) an input level or layer (26) receiving each one of the frames (19a, 19b, 19c) of said sequence of frames (19); one or more convolution levels or layers (27) that function to identify macroscopic geometric and visual characteristics of the tail portion (P1); at least one fully connected level (28) configured to classify the frame (19a, 19b, 19c) based on information received from at least one of the one or more convolutional levels or layers (27); an output level or layer (29) defined by a binary class vector including a first class and a second class, both classes being characterized by respective confidence indices (PD1, PD2) for the absence and presence of a defect; 8. The method of claim 6 or claim 7, comprising:

9. 9. The method according to claim 8, characterized in that, for the frame (19a, 19b, 19c), the confidence index (PD2) associated with the second class is analyzed, and if the confidence index (PD2) exceeds a predetermined threshold (PD), the convolutional network (25) establishes that the analyzed frame (19a, 19b, 19c) is associated with a metal product (P) having a defect and generates a specific actuation signal (S) to be sent to the robotized articulated arm (11) for the correction of the defect.

10. 9. The method of claim 8, wherein the at least one fully connected level (28) is configured to classify each of the frames (19a, 19b, 19c) into a class of belonging comprising tail frames (19b) with shape defects and tail frames (19b) with cobble defects based on the information received from at least one of the one or more convolutional levels or layers (27).

11. 10. The method of claim 9, wherein the at least one fully connected level (28) is configured to classify each of the frames (19a, 19b, 19c) into a class of belonging comprising tail frames (19b) with shape defects and tail frames (19b) with cobble defects based on the information received from at least one of the one or more convolutional levels or layers (27).

12. An apparatus (100) for the production of metal reels (200), comprising: a winding unit (110) having a winding head (111) and configured to receive the metal product (P) in linear form and form it into a coil; an automatic reel-forming unit (113) capable of receiving the metal product (P) formed into a coil shape and forming it into a reel (200); a roller way (112) configured to move the metal product (P) formed into a coil shape from a start end (112a) corresponding to a position where the winding unit (110) is disposed to a finish end (112b) corresponding to a position where the automatic reel forming unit (113) is disposed; Equipped with An apparatus (100) characterized in that it is arranged at a position included between the starting end (112a) and the ending end (112b) of the roller way (112), and further comprises the robot unit (10) according to any one of claims 1 to 5.

13. A method for producing a metal reel (200), comprising the steps of: The winding unit (110) receives the wire-shaped metal product (P), and the laying head (111) forms it into a coil; An automatic reel forming unit (113) receives the metal product (P) formed into a coil shape and forms a reel (200); A roller way (112) moves the metal product (P) formed into a coil shape from a start end (112a) corresponding to a position where the winding unit (110) is disposed to a finish end (112b) corresponding to a position where the automatic reel forming unit (113) is disposed; 6. A method according to claim 1, wherein the robot unit (10) according to any one of claims 1 to 5 is moved laterally to a position included between the starting end (112a) and the ending end (112b) of the roller way (112) in order to manipulate, reposition or cut the metal product (P).

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