Multi-layer and multi-pass tracking method for submerged arc welding in deep and narrow space welds based on arc sensing

Abstract

The present invention relates to the field of deep and narrow space submerged arc welding weld tracking, and is a deep and narrow space weld submerged arc welding multi-layer and multi-pass tracking method based on arc sensing. Aiming at the problem that the weld tracking method is difficult to adaptively select due to the different weld groove types in the deep and narrow space submerged arc welding multi-layer and multi-pass welding process, a deep and narrow space weld submerged arc welding multi-layer and multi-pass tracking method based on arc sensing is proposed; the arc swing amplitude is planned by using an arc swing amplitude planning method based on a scanning point laser; the weld left groove deviation, the weld right groove deviation and the weld centering groove deviation are identified by using an arc sensing-based deep and narrow space weld submerged arc welding multi-layer and multi-pass weld deviation identification method; and the corresponding weld deviation identification mode is selected by using a weld deviation identification mode adaptive selection method.

Description

Technical Field

[0001] The invention relates to the field of submerged arc welding seam tracking, and is a multi-layer and multi-pass tracking method for submerged arc welding of deep and narrow space welds based on arc sensing. Technical Background

[0002] As a thick plate welding method, deep and narrow space submerged arc welding, multi-layer and multi-pass welding is widely used in large lifting equipment, aerospace, nuclear power construction and other fields. In the deep and narrow space submerged arc welding, multi-layer and multi-pass welding process, the tracking of the weld has a crucial impact on the weld quality, but different types of weld grooves have different weld tracking modes. At present, there is no method that can adaptively select the weld tracking mode according to the weld groove type. To solve this problem, the present invention discloses a deep and narrow space weld submerged arc welding multi-layer and multi-pass tracking method based on arc sensing, which can select the corresponding weld deviation recognition mode according to different weld groove types, thereby realizing weld tracking. Summary of the invention

[0003] The multi-layer and multi-pass tracking method of deep and narrow space welds based on arc sensing is used to track the multi-layer and multi-pass welds in deep and narrow spaces by submerged arc welding. The flow chart is as follows Figure 2 As shown, it is characterized in that: the multi-layer and multi-pass tracking method of deep and narrow space weld submerged arc welding based on arc sensing is realized by a multi-layer and multi-pass tracking system of deep and narrow space weld submerged arc welding based on arc sensing; the multi-layer and multi-pass tracking system of deep and narrow space weld submerged arc welding based on arc sensing is composed of a submerged arc welding power supply, a welding robot, a scanning point laser system, an arc signal acquisition system, and a submerged arc welding walking trolley; the submerged arc welding power supply is used to provide the energy required for welding; the welding robot includes three degrees of freedom, which are used to control the welding gun to move left and right, up and down, and swing perpendicular to the welding direction; the scanning point laser system is used to scan the weld groove, and by identifying the groove position and the weld width, the starting point position is determined and the number of welds and the arc swing amplitude are planned; the arc signal acquisition system is used to collect welding voltage; the submerged arc welding walking trolley is used to drive the welding robot to walk along the welding direction.

[0004] The arc sensing-based multi-layer and multi-pass tracking method for deep and narrow space weld submerged arc welding is characterized in that: the arc swing amplitude W is planned by using an arc swing amplitude planning method based on a scanning point laser, and the weld groove is scanned by a scanning point laser system to obtain the weld groove surface height information, thereby planning the arc swing amplitude W; the arc swing amplitude planning method based on a scanning point laser includes a base welding and a filling welding arc swing amplitude planning method; the base welding arc swing amplitude planning method is to select a preset maximum arc swing amplitude W when performing the first layer of weld (i.e., base welding) welding. maxPerform welding; the method for planning the arc swing amplitude of the fill welding is when welding the second layer and above welds (i.e., fill welding), the weld bead width W h is compared with the preset maximum arc swing amplitude W max . Let W h / W max =n + x, where n is the integer part and x is the part after the decimal point. When 0 ≤ x ≤ 0.5, let the number of weld bead passes of this layer be n + 1, and the arc swing amplitude be W = W h / (n + 1). When 0.5 < x < 1, let the number of weld bead passes of this layer be n + 2, and the arc swing amplitude be W = W h / (n + 1); the weld bead width W h is the distance between the intersections of the weld bead surface and the two side grooves, and is extracted by using the weld bead surface recognition method based on slope analysis; the weld bead surface recognition method based on slope analysis is realized by calculating the slope k between two adjacent scanning points and comparing and analyzing. When 0 < k ≤ k set , it is considered that these two points belong to the weld bead surface at this time. When k > k set , it is considered that these two points belong to the right side of the weld groove. When k < -k set , it is considered that these two points belong to the left side of the weld groove, where k set is the set slope threshold. The schematic diagram of the weld groove surface height information obtained by the scanning point laser system is as shown in Figure 3 .

[0005] The multi-layer and multi-pass tracking method for submerged arc welding of deep and narrow space welds based on arc sensing is characterized in that: the deviation recognition of multi-layer and multi-pass welds of submerged arc welding of deep and narrow space welds is realized by using the multi-layer and multi-pass weld deviation recognition method for submerged arc welding of deep and narrow space welds based on arc sensing; the multi-layer and multi-pass weld deviation recognition method for submerged arc welding of deep and narrow space welds based on arc sensing includes a weld right groove deviation recognition mode, a weld left groove deviation recognition mode, and a weld center groove recognition mode; the weld right groove deviation recognition mode is realized by calculating the difference between the integral value I rp of the first half arc signal in the swing period and the integral value I rl of the second half cycle arc signal, and comparing and analyzing with the preset left and right deviation thresholds. When I rp -I rl > ΔI rset1 , it is considered that the welding torch is deflected to the right. When I rp -I rl < ΔI rset2 , it is considered that the welding torch is deflected to the left. Otherwise, it is considered that there is no deviation, where ΔI rset1 and ΔI rset2is the right deviation and left deviation threshold of the welding gun when welding the right groove of the weld; the swing cycle refers to the time required for the swing arc to swing from the swing center to the left to a certain amplitude, then swing to the right and finally return to the swing center, wherein the swing amplitude of the arc to the left and to the right with the swing center as the reference is equal, both are W / 2; the left groove deviation recognition mode of the weld is through calculating the arc signal integral value Ill of the second half of the swing cycle and the arc signal integral value Ill of the first half of the swing cycle lp The difference is calculated by comparing the left and right deviation thresholds. ll -I lp >ΔI lest1 , it is considered that the welding gun is biased to the left. lp -I ll <ΔI lest2 When , the welding gun is considered to be right-biased, otherwise it is considered that there is no deviation, where ΔI lset1 With ΔI lset2 is the left deviation and right deviation threshold of the welding gun when welding the left groove of the weld; the weld centering groove deviation recognition mode is to calculate the difference between the integral values ​​of the arc signal of the first and second half cycles in the swing cycle I cp -I cl And compare and analyze the realization, when I cp -I cl >0, the welding gun deviates to the right. cp -I cl <0, the welding gun is biased to the left, otherwise it is considered that there is no deviation. Figure 4 Shown

[0006] The arc sensing-based deep narrow space weld submerged arc welding multi-layer multi-pass tracking method is characterized by: using a weld deviation recognition mode adaptive selection method to adaptively select a weld deviation recognition mode; the weld deviation recognition mode adaptive selection method is implemented by a welding start point position planning method based on a scanning point laser; the welding start point position planning method based on a scanning point laser is implemented by a weld type planning method; the weld type planning method is implemented by converting the lowest height value h in the scanning point data into min With the preset height threshold h set Comparative analysis is achieved when h min ≤h set When the base welding is carried out, the starting welding point is the position corresponding to the lowest scanning point in height, and the weld centering groove deviation recognition mode is selected to track the weld. min >h setWhen filling welding is performed, the starting point of the weld is one of the intersections of the weld surface and the two side grooves. When the left intersection is selected as the starting point, if the number of welds is n+1 at this time, select the left groove deviation recognition mode of the weld to track the first n welds, and select the right groove deviation recognition mode of the weld to track the last weld. If the number of welds is n+2 at this time, select the left groove deviation recognition mode of the weld to track the first n+1 welds, and select the centering groove deviation recognition mode of the weld to track the last weld. When the right intersection is selected as the starting point, if the number of welds is n+1 at this time, select the right groove deviation recognition mode of the weld to track the first n welds, and select the left groove deviation recognition mode of the weld to track the last weld. If the number of welds is n+2 at this time, select the right groove deviation recognition mode of the weld to track the first n+1 welds, and select the left groove deviation recognition mode of the weld to track the last weld. Schematic diagrams of different weld types are shown in the figure below. Figure 5 shown.

[0007] Advantageous Effects of the Invention

[0008] The present invention relates to the field of deep and narrow space submerged arc welding weld tracking, and is a deep and narrow space weld submerged arc welding multi-layer multi-pass tracking method based on arc sensing. Aiming at the problem that the weld position is difficult to track during the deep and narrow space submerged arc welding multi-layer multi-pass welding process, a deep and narrow space weld submerged arc welding multi-layer multi-pass tracking method based on arc sensing is proposed; the arc swing amplitude is planned using an arc swing amplitude planning method based on a scanning point laser; the weld left groove deviation, weld right groove deviation and weld centering groove deviation are identified using an arc sensing-based deep and narrow space weld submerged arc welding multi-layer multi-pass weld deviation identification method; and the corresponding weld deviation identification mode is selected using a weld deviation identification mode adaptive selection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 Schematic diagram of the multi-layer and multi-pass tracking system for submerged arc welding in deep and narrow space welds based on arc sensing.

[0010] Figure 2 The flowchart of the multi-layer and multi-pass tracking method of submerged arc welding in deep and narrow space welds based on arc sensing.

[0011] Figure 3 The weld groove surface height information is obtained using the scanning point laser system.

[0012] Figure 4 Schematic diagram of different weld grooves.

[0013] Figure 5 Schematic diagram of weld types. DETAILED DESCRIPTION

[0014] To better illustrate the technical solutions and beneficial effects of the present invention, the following further elaborates on the present invention in conjunction with the accompanying drawings and implementation cases. The implementation methods of the present invention are not limited thereto.

[0015] Step 1: Planning the arc swing amplitude W.

[0016] Before starting welding, planning the arc swing amplitude is crucial. An overly large or small swing amplitude will seriously affect the welding quality. To address this problem, this paper proposes a method for planning the arc swing amplitude based on a scanned point laser. Before welding starts, preset the maximum arc swing amplitude W max , and use the Figure 1 scanned point laser system in it to scan the welding groove and obtain the lowest height point h min and compare it with the preset height threshold h set . When h min ≤h set , it is the root pass welding. Select the position corresponding to this point as the starting welding point. The welding robot moves to this point and selects the maximum arc swing amplitude W max for welding; when h min >h set , it is the fill pass welding. Identify the weld bead surface by calculating the slope k of two adjacent scanned points, obtain the weld bead width W h and select one of the intersection points of the weld bead surface and the坡口 on the left and right sides as the starting welding point. Let W h / W max =n + x, where n is the integer part and x is the decimal part. When 0≤x≤0.5, let the number of weld passes in this layer of the weld bead be n + 1, and the arc swing amplitude be W = W h / (n + 1). When 0.5 < x < 1, let the number of weld passes in this layer of the weld bead be n + 2, and the arc swing amplitude be W = W h / (n + 1). Finally, the welding robot controls the welding torch to move to the starting welding point position and selects this arc swing amplitude for welding.

[0017] Step 2: Identifying the weld deviation.

[0018] During the multi-layer and multi-pass submerged arc welding process in a deep and narrow space, it is necessary to track the weld in real time to prevent defects such as weld bead biting and overflow. To address this problem, the present invention discloses a method for identifying the deviation of multi-layer and multi-pass submerged arc welds in a deep and narrow space based on arc sensing, which is used to identify the weld deviation so as to be able to track the weld in real time. This method includes a weld deviation identification mode on the right side of the weld, a weld deviation identification mode on the left side of the weld, and a weld alignment deviation identification mode. During welding, the welding robot controls the welding torch to move to the starting welding point position. After arc ignition, Figure 1 the arc signal acquisition system in it starts to collect arc signals and perform filtering.

[0019] When tracking the weld using the right groove deviation recognition mode, the welding robot calculates the arc signal integral value I in the first half of the swing cycle. rp The arc signal integral value I rl The difference, when I rp -I rl >ΔI rset1 When I rp -I rl <ΔI rset2 When , the welding gun is considered to be biased to the left, and the welding gun is controlled to move to the right. Otherwise, it is considered that there is no deviation, and the swing position of the welding gun remains unchanged, where ΔI rset1 With ΔI rset2 It is the left and right deviation threshold of the welding gun when welding the right groove of the weld.

[0020] When tracking the weld using the weld groove deviation recognition mode on the left side, the welding robot will calculate the arc signal integral value I in the second half of the swing cycle. ll The difference between the arc signal integral value of the first half cycle and I ll -I lp , when I ll -I lp >ΔI lset1 , we think the welding gun is biased to the left, and control the welding gun to move to the right. lp -I ll <ΔI lset2 When , the welding gun is considered to be biased to the right, and the welding gun is controlled to move to the left. Otherwise, it is considered that there is no deviation, and the swing position of the welding gun remains unchanged, where ΔI lset1 With ΔI lset2 It is the left and right deviation threshold of the welding gun when welding the left groove of the weld.

[0021] When using the weld centering groove deviation recognition mode for weld tracking, the welding robot calculates the arc signal integral value I of the first half of the swing cycle within the swing cycle. cp The arc signal integral value I cl The difference, when I cp -I cl >0, the welding gun is considered to be biased to the right, and the welding gun is controlled to move to the left; when I cp -I cl When <0, the welding gun is considered to be biased to the left and the welding gun is controlled to move to the right. Otherwise, it is considered that there is no deviation and the swing position of the welding gun remains unchanged.

[0022] Step 3: Adaptive selection of weld deviation recognition mode.

[0023] In the process of multi-layer and multi-pass submerged arc welding in deep and narrow space, it is necessary to select a corresponding weld tracking method according to the weld groove type to track the weld. To solve this problem, the present invention discloses a weld deviation recognition mode adaptive selection method. When performing backing welding, the weld centering groove deviation recognition mode is selected to track the weld. When performing filling welding, the corresponding weld deviation recognition mode is selected according to the number of weld passes required for this layer of weld. When the intersection of the weld surface and the left groove is selected as the starting point, if the number of weld passes is n+1 at this time, the weld left groove deviation recognition mode is selected to track the first n welds, and the weld right groove deviation recognition mode is selected to track the last weld. If the number of weld passes is n+2 at this time, the weld left groove deviation recognition mode is selected to track the first n+1 welds, and the weld centering groove deviation recognition mode is selected to track the last weld; when the intersection of the weld surface and the right groove is selected as the starting point, if the number of weld passes is n+1 at this time, the weld right groove deviation recognition mode is selected to track the first n welds, and the weld left groove deviation recognition mode is selected to track the last weld. If the number of weld passes is n+2 at this time, the weld right groove deviation recognition mode is selected to track the first n+1 welds, and the weld centering groove deviation recognition mode is selected to track the last weld.

Claims

1. A multi-layer and multi-pass tracking method for submerged arc welding of deep and narrow space welds based on arc sensing is used to track multi-layer and multi-pass welds in deep and narrow space by submerged arc welding, and is characterized by: The arc sensing-based multi-layer and multi-pass tracking method for deep and narrow space welds submerged arc welding is realized by an arc sensing-based multi-layer and multi-pass tracking system for deep and narrow space welds submerged arc welding; the arc sensing-based multi-layer and multi-pass tracking system for deep and narrow space welds submerged arc welding is composed of a submerged arc welding power supply, a welding robot, a scanning point laser system, an arc signal acquisition system, and a submerged arc welding walking trolley; the submerged arc welding power supply is used to provide the energy required for welding; the welding robot includes three degrees of freedom for controlling the welding gun to move left and right, up and down, and to swing perpendicular to the welding direction; the scanning point laser system is used to scan the weld groove, and determine the groove position and the weld width by identifying the groove position and the weld width. The starting point position is used to plan the number of welds and the arc swing amplitude; the arc signal acquisition system is used to collect welding voltage; the submerged arc welding walking trolley is used to drive the welding robot to walk along the welding direction; the deep narrow space weld submerged arc welding multi-layer and multi-pass tracking method based on arc sensing includes three steps, step 1 is to plan the arc swing amplitude W using the arc swing amplitude planning method based on scanning point laser, step 2 is to realize the deep narrow space weld submerged arc welding multi-layer and multi-pass weld deviation identification method based on arc sensing, and step 3 is to adaptively select the weld deviation identification mode using the weld deviation identification mode adaptive selection method.

2. The arc sensing-based multi-layer and multi-pass tracking method for deep and narrow space welds according to claim 1 is characterized in that: Plan the arc swing amplitude W using the arc swing amplitude planning method based on scanned point laser. Use the scanned point laser system to scan the weld groove to obtain the height information of the weld groove surface, thereby planning the arc swing amplitude W. The arc swing amplitude planning method based on scanned point laser includes the root pass arc swing amplitude planning method and the fill pass arc swing amplitude planning method. The root pass arc swing amplitude planning method is to select the preset maximum arc swing amplitude W max for welding during root pass welding. The fill pass arc swing amplitude planning method is to compare the weld bead width W h with the preset maximum arc swing amplitude W max . Let W h / W max = n + x, where n is the integer part and x is the part after the decimal point. When 0 ≤ x ≤ 0.5, let the number of weld beads in this layer of weld be n + 1, and the arc swing amplitude be W = W h / (n + 1). When 0.5 < x < 1, let the number of weld beads in this layer of weld be n + 2, and the arc swing amplitude be W = W h / (n + 1). The weld bead width W h is the distance between the intersection points of the weld bead surface and the two sides of the groove, and is extracted using the weld bead surface recognition method based on slope analysis. The weld bead surface recognition method based on slope analysis is realized by calculating the slope k between two adjacent scanned points and comparing and analyzing it with the preset slope threshold k set . When 0 < k ≤ k set , it is considered that these two scanned points belong to the weld bead surface. When k > k set , it is considered that these two scanned points belong to the right side of the weld groove. When k < -k set , it is considered that these two scanned points belong to the left side of the weld groove.

3. The arc sensing-based multi-layer and multi-pass tracking method for deep and narrow space weld submerged arc welding according to claim 1 is characterized in that: The multi-layer and multi-pass weld deviation identification method for deep and narrow space welds based on arc sensing is used to realize the multi-layer and multi-pass weld deviation identification of deep and narrow space welds; the multi-layer and multi-pass weld deviation identification method for deep and narrow space welds based on arc sensing includes a weld right groove deviation identification mode, a weld left groove deviation identification mode and a weld centering groove deviation identification mode; the weld right groove deviation identification mode is to calculate the integral value I of the arc signal in the first half of the swing cycle rp The arc signal integral value I rl The difference is compared with the preset left and right deviation thresholds. rp -I rl >ΔI rset1 When I rp -I rl <ΔI rset2 When , the welding gun is considered to be biased to the left, otherwise it is considered that there is no deviation, where ΔI rset1 With ΔI rset2 is the right deviation and left deviation threshold of the welding gun when welding the right groove of the weld; the swing cycle refers to the time required for the swing arc to swing from the swing center to the left to a certain amplitude, then swing to the right and finally return to the swing center, wherein the swing amplitude of the arc to the left and to the right is equal based on the swing center, both of which are W / 2; the left groove deviation recognition mode of the weld is achieved by calculating the integral value I of the arc signal in the second half of the swing cycle ll The first half of the arc signal integral value I lp The difference is realized by comparing the left and right deviation thresholds. ll -I lp >ΔI lset1 , it is considered that the welding gun is biased to the left. lp -I ll <ΔI lset2 When , the welding gun is considered to be right-biased, otherwise it is considered that there is no deviation, where ΔI lset1 With ΔI lset2 is the left deviation and right deviation threshold of the welding gun when welding the left groove of the weld; the weld centering groove deviation recognition mode is to calculate the difference between the integral values ​​of the arc signal of the first and second half cycles in the swing cycle I cp -I cl And compare and analyze the realization, when I cp -I cl >0, the welding gun deviates to the right. cp -I cl When <0, the welding gun is deviated to the left, otherwise it is considered that there is no deviation.

4. The arc sensing-based multi-layer and multi-pass tracking method for deep and narrow space weld submerged arc welding according to claim 1 is characterized in that: The weld deviation recognition mode is adaptively selected by using the weld deviation recognition mode adaptive selection method; the weld deviation recognition mode adaptive selection method is realized by using the welding starting point position planning method based on the scanning point laser; the welding starting point position planning method based on the scanning point laser is realized by using the weld type planning method; the weld type planning method is realized by converting the lowest height value h in the scanning point data into min With the preset height threshold h set Comparative analysis is achieved when h min ≤h set When the base welding is carried out, the starting welding point is the position corresponding to the lowest scanning point in height, and the weld centering groove deviation recognition mode is selected to track the weld. min >h set When the filler welding is performed, the starting point of the welding is one of the intersections of the weld surface and the two side grooves. When the left intersection is selected as the starting point, if the number of welds is n+1 at this time, the left groove deviation recognition mode of the weld is selected to track the first n welds, and the right groove deviation recognition mode of the weld is selected to track the last weld. If the number of welds is n+2 at this time, the left groove deviation recognition mode of the weld is selected to track the first n+1 welds, and the centering groove deviation recognition mode of the weld is selected to track the last weld. When the right intersection is selected as the starting point, if the number of welds is n+1 at this time, the right groove deviation recognition mode of the weld is selected to track the first n welds, and the left groove deviation recognition mode of the weld is selected to track the last weld. If the number of welds is n+2 at this time, the right groove deviation recognition mode of the weld is selected to track the first n+1 welds, and the centering groove deviation recognition mode of the weld is selected to track the last weld.

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