4467results about "Arc welding apparatus" patented technology

The invention relates to a multi-product-line-oriented robot welding integration method, device and equipment and a medium, and the method comprises the steps that three-dimensional geometrical characteristics and material attribute data of multiple types of workpieces are collected, and geometric parameters and material mechanical properties of weld joints are extracted; an order demand template is intelligently matched, and initial welding parameters and a clamp configuration scheme are generated; the health state of production equipment is evaluated in real time, and the collision-free movement track of the mechanical arm is planned; dynamically analyzing the joint angle sequence to generate a high-precision servo control signal; the welding process is monitored in real time, and current and molten pool temperature parameters are corrected; and iteratively optimizing the algorithm weight of the process knowledge base to improve the adaptive capacity. Through cooperation of multi-source data fusion and an intelligent algorithm, rapid switching of multiple kinds of workpieces, self-adaptive compensation of the equipment operation state and closed-loop optimization of welding process parameters are achieved, and the equipment utilization rate and the welding quality stability in a flexible production scene are remarkably improved.

A method for performing an automated additive manufacturing (AM) process comprises obtaining a current state that includes a set of first AM factors and a set of first characteristics, which are geometric or physical properties of a first AM output are generated using the first AM factors. The current state is provided to a reinforcement learning (RL) framework designed to adjust welding parameters in real-time based on the current state and reward feedback. The RL framework includes an actor network and a critic network, and safety limits are enforced through a custom loss function. The first AM factors are updated based on the critic network's evaluation, resulting in second AM factors used to perform the second AM process and generate a second AM output.

The invention relates to the field of welding process control, and discloses a pipeline all-position automatic TIG (Tungsten Inert Gas) welding method which comprises the following steps: collecting pipeline geometric parameters, welding position angles and material attribute data in real time through multi-source data fusion; constructing a Gaussian process regression dynamic response model, coupling a nonlinear mapping relationship among the process parameters, the molten pool morphology and the corrosion tendency index, and dynamically adjusting the weight of the model based on the welding position angle; a hierarchical strategy of Bayesian optimization and model prediction control is adopted to generate a parameter solution set meeting the fusion depth constraint and the corrosion threshold value, and the molten pool oscillation frequency is used as feedback to correct the current in real time; the heat accumulation evolution trend is predicted through a hidden Markov chain, and parameter closed-loop migration and trajectory compensation are achieved in combination with molten pool flow field coupling correction. The problems of uneven forming quality and corrosion risk caused by space displacement, dissimilar metal interface effect and dynamic disturbance in all-position welding are solved.

An automatic welding production line for a large complex workpiece includes a base station, a welding robot, a displacement worktable, an auxiliary robot, a feeding system and a blanking system. The displacement worktable has multi degrees of freedom, the position and angle of the workpiece can be flexibly adjusted during welding to meet the welding requirements of any position and angle of the welding equipment, thereby realizing the welding operation of large complex special-shaped three-dimensional components, and multi-point welding at any point can be realized through the cooperation of the welding robot and the auxiliary robot. The control system controls the robot and the feeding and blanking system to cooperate with the displacement worktable to realize automatic feeding, welding and blanking operations, the welding parameters and operations can be automatically adjusted for the special components of the structure, to realize integration and intelligence, improve production efficiency and reduce production costs.

The invention belongs to the technical field of robot welding, and discloses a welding robot motion path optimization planning method based on multi-sensor fusion. Comprising the steps that sensor data are obtained, and the sensor data comprise a welding seam three-dimensional point cloud, a molten pool image, acceleration and angular velocity of the tail end of a welding gun in three axial directions, welding current and welding voltage; converting the sensor data into a unified coordinate system to obtain converted sensor data; performing filtering processing on the converted three-dimensional point cloud of the welding seam to obtain a filtering point cloud, and performing feature extraction on the filtering point cloud based on an RANSAC (Random Sample Consensus) algorithm to obtain a center line feature of the welding seam; in combination with the converted three-dimensional point cloud of the welding seam and the characteristics of the center line of the welding seam, an initial welding path is obtained through planning based on an improved A * algorithm; intelligent and precise adjustment of welding parameters is achieved, the stability of welding quality is guaranteed, and the performance of motion path planning and welding operation of the welding robot is improved in an all-around mode.

The invention discloses a welding seam monitoring system based on an electric welding machine, and relates to the technical field of electric welding, an initial setting module is used for setting a normal fluctuation threshold range of each working parameter by collecting working information of the electric welding machine in a fault-free state, and the working information at least comprises welding current, voltage and welding speed; when it is determined that one or more pieces of working parameter information collected in real time exceed the preset normal fluctuation threshold range, whether short-term fluctuation or fault fluctuation exists is judged by judging the change of the parameters, and therefore parameter adjustment caused by misjudgment of the electric welding machine in the short-term fluctuation process can be prevented; and the quality defect of the welding seam is caused.

A method for performing an automated additive manufacturing (AM) process comprising obtaining a set of first AM factors associated with performing a first AM process and obtaining a set of first characteristics of a first AM output, wherein the first AM output is generated by performing the first AM process using the set of first AM factors. A predictor is trained using the set of first AM factors and the set of first characteristics, which is a machine learning model, and a mathematical model is generated based on extracting internal parameters of the predictor. The mathematical model comprising represents an estimated relationship between first AM factors and first characteristics. A set of target characteristics is obtained and, using the mathematical model, a set of second AM factors is identified. The set of second AM factors are then used to perform a second AM process, generating a second AM output.

The invention relates to an in-situ alloying method for an electric arc additive ultrahigh-strength aluminum alloy, in particular to an in-situ alloying method for a double-wire feeding electric arc additive ultrahigh-strength aluminum alloy. The invention aims to solve the problems that the microscopic uniformity of the material is poorer and the ideal mechanical property is difficult to realize due to the fact that the aluminum alloy is easy to form a thick organization structure in a deposition state, and the compactness and the quality stability of the material are seriously influenced due to the fact that solidification shrinkage cavities are easy to appear in the deposition process. By adjusting the wire feeding speed and angle of the two wires, it is ensured that the two wires are molten in the same molten pool, and in-situ alloying of ultrahigh-strength aluminum alloy additive manufacturing is achieved. By optimizing the components and the organization structure, the method effectively improves the component uniformity, the forming stability and the mechanical property of the aluminum alloy, and the high-performance aluminum alloy additive component with high strength, excellent toughness and durability is prepared. The invention belongs to the technical field of additive manufacturing.

The invention relates to the technical field of welding platforms, in particular to a multifunctional welding process experiment platform and a process optimization system.The process optimization method comprises the following steps that a welding process setting module is constructed, and a welding mode, a groove type and a heat input scheme are preset; outputting the initial process configuration parameter set to drive a welding execution unit to perform automatic welding operation according to the initial process configuration parameter set; extracting a multi-dimensional process response vector including a stability index, metal fusion depth distribution and a heat affected zone expansion degree; outputting a welding seam grade result according to a preset quality grading standard, and generating a feedback identifier; and after updating, inputting to the step S2 again to form closed-loop optimization control. According to the method, digital modeling and evaluation of key indexes such as the welding stability, the fusion depth and the heat affected zone are achieved, and the problem that a traditional process depends on visual inspection and experience judgment is solved.

The invention relates to the technical field of intelligent tracking of welding seams, in particular to an intelligent tracking method and system for a free curve welding seam through a mechanical arm. The method comprises the following steps that a laser contourgraph is deployed to scan a welding seam on a mechanical arm, geometric feature data of the welding seam are generated, and surface state mapping is conducted; thirdly, analyzing the distribution trend of pores of the welding seam, estimating the welding compactness loss, and further evaluating the fracture risk of the welding seam; according to the fracture risk data, welding heat output is regulated and controlled so as to optimize the welding quality; and finally, constructing an intelligent welding tracking model based on a random forest algorithm, and uploading the intelligent welding tracking model to a cloud platform to realize intelligent tracking welding control of the mechanical arm on the free curve welding seam. According to the invention, the intelligent tracking technology of the welding seam is optimized, so that the intelligent tracking technology of the welding seam is more perfect.

The invention relates to the technical field of welding, and discloses an intelligent control method and system for the welding process of a thin-wall metal component, and the intelligent control method for the welding process of the thin-wall metal component comprises the steps that an initial welding parameter set is determined, and time synchronization is conducted; obtaining multi-source original data with timestamps in the welding process; time alignment is carried out; respectively calculating a current stability index, a heat accumulation index and a molten pool width measured value to obtain a key deviation index; based on the key deviation index, current adjustment amount, pulse parameter adjustment amount and position compensation amount are obtained through calculation; obtaining an adjustment amount set; the current welding parameters are subjected to amplitude limiting updating, and an updated current welding parameter set is obtained through a step-by-step transition method and a high-priority fast instruction; recording a welding process data log, and judging a welding state; carrying out comprehensive evaluation and data storage on the welding process; according to the method, various dynamic changes in the welding process are comprehensively coped with, and the stability and consistency of the welding quality are improved.

The invention relates to the technical field of metal welding automation control, in particular to a metal welding seam automatic tracking welding system based on machine vision. Comprising a data acquisition module used for acquiring initial three-dimensional geometric parameters of a welding seam and monitoring welding process parameters including welding current, arc voltage and welding speed; the thermal deformation prediction module is used for solving a weld joint thermal deformation displacement vector; the dynamic deviation evaluation module is used for calculating a dynamic deviation index; the risk grading module is used for judging the trajectory deviation risk as safety, first-level risk or second-level risk; the defect tendency prediction module is used for calculating an incomplete fusion index and a hot crack index; the comprehensive risk assessment module is used for determining a comprehensive process risk factor; and the track correction module is used for generating a corrected welding gun target track so as to carry out closed-loop correction control on the welding process. According to the method, the problem of trajectory deviation caused by thermal hysteresis and dynamic deformation is solved, and the tracking precision and stability of the welding path are remarkably improved.

The invention relates to the technical field of welding parameter correction, and provides a welding parameter correction method for real-time molten pool morphology feedback, which comprises the following steps of: capturing a molten pool image, an arc spectrum and an infrared radiation signal in real time, accurately extracting geometrical characteristics and contour vibration frequency of a molten pool, and dynamically evaluating arc stability and a thermal attenuation state. And when defects are detected, welding parameter correction is generated based on a process knowledge graph and Bayesian reasoning, collaborative correction of parameters such as welding current, welding voltage and welding speed is achieved through reinforcement learning optimization weight distribution, and the change trend of morphology characteristics of a molten pool is continuously monitored after correction. According to the method, the depth belief network is judged and optimized by combining the descending rate of the morphology defect probability, and the corrected successful case is supplemented to the process knowledge graph, so that the welding quality stability is remarkably improved, the method is particularly suitable for automatic production of a high-precision welding scene, the defect rate can be effectively reduced, and the production efficiency can be effectively improved.

The invention provides a method for obtaining the optimal low-temperature impact toughness and a welded body, and the method comprises the following steps: S1, obtaining chemical components of sample steel, and calculating the cooling rate corresponding to the bainite phase change curve nose tip temperature of the sample steel; s2, the cooling rate serves as the key cooling rate of the welding heat cycle process, and the corresponding welding heat input amount is calculated; s3, cutting a plurality of cuboid thermal simulation initial samples of which the length directions are parallel to the rolling direction of the steel plate from the sample steel; s4, two-pass welding thermal simulation cycle experiments at different temperatures are set; s5, two thermocouples are welded to the center of the cuboid thermal simulation initial sample for temperature measurement, and a welding simulation experiment is completed; and S6, size machining is conducted on the welding sample, a low-temperature impact experiment is conducted, impact energy under different technologies is measured, and the optimal impact toughness of CGHAZ and ICCGHAZ is determined. The method can be used for developing and optimizing the welding process with insufficient low-temperature impact toughness of the submerged-arc welding heat affected zone of the low alloy steel.

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, which provide for welding techniques for manufacturing robots, such as seam localization, gap measurement, or both. For example, the welding techniques may include, during illumination of one or more objects by a light source, controlling a camera to capture images of the one or more objects along at least a portion of a length of a seam formed by the one or more objects. The techniques further include differentiating, in the images, the seam from the one or more objects. In a first aspect, the techniques also include triangulating the differentiated seam to identify a position of the seam relative to a reference point. In a second aspect, the techniques also include determining, based on the differentiated seam, gap information along a portion of the seam. Other aspects and features are also claimed and described.

The invention provides a pipeline welding all-position welding seam identifying and tracking system and method, and belongs to the technical field of pipeline welding automation. In order to solve the problems of strong arc light interference, low tracking precision, slow response speed and insufficient robustness in the existing pipeline all-position welding tracking technology, the system comprises a sensing layer, a processing layer, a decision-making layer and an execution layer. The welding seam identifying and tracking method comprises the following steps: S1, calibrating the welding seam identifying and tracking system; s2, all-position welding tracking is carried out; and S3, performing adaptive adjustment on different scenes. According to the method, dynamic weighted fusion and fuzzy PID control are adopted, multi-posture welding such as flat welding, vertical position welding, horizontal position welding and overhead position welding can be automatically adapted, parameters do not need to be manually adjusted, and the operation threshold is lowered.

The invention relates to the technical field of process optimization, in particular to a collaborative optimization analysis method for multi-station tower drum welding. In order to solve the problem that cooperative control of tower drum multi-station welding is difficult, firstly, multi-mode sensing data of welding seam vision, arc sensing, thermal field distribution and welding voiceprint are loaded, a fusion evaluation model is constructed to obtain a welding seam deviation value and a sensor confidence score, and the health state of a sensor is judged according to a threshold value; and a corresponding fault-tolerant reconstruction control mode is triggered to obtain welding tracking control parameters, then a multi-station welding heat source model is loaded, thermal interference is decoupled in combination with the control parameters and thermal field data, multiple stations are cooperatively regulated and controlled, a cooperative control instruction is output and executed, and welding process optimization is achieved.

The invention discloses a method for reducing pores by repair welding of internal defects of a thin-wall aluminum alloy casting. The method comprises the following steps: confirming and marking X-ray positions of the internal defects of the aluminum alloy casting; internal defects at the marked position are cleaned, the defects are required to be completely eliminated, and the cleaning position is smooth and has a certain groove angle; cleaning oil stains and oxidation films at the repair welding position of the casting; preheating the whole casting; welding a reverse support and adding a blowing protection tool; manual argon arc welding filling surfacing is carried out, arc surfacing is carried out from the periphery to the center, and excess weld metal is reserved; removing redundant weld joint materials of surfacing bulges of the casting; x-ray detection is conducted on the repaired casting, whether the pore defects generated after surfacing repair can meet related requirements or not is emphatically confirmed, and if pores gathered on the surface layer of a weld joint exist, the pores can be discharged through a hot pressing method. According to the method, through hole internal defect cleaning, reverse supporting, blowing protection increasing and arc drawing surfacing methods are adopted for conducting surfacing repairing on the internal defects of the casting, the quality of aluminum alloy casting products is guaranteed, and the rejection rate and the production cost are reduced.

The invention provides a multi-field regulation and control excitation welding device and system and a magnetic field control method. The welding device comprises a welding gun; the excitation magnetic poles are symmetrically arranged on the two sides of the welding gun. The at least one group of common magnetic poles are arranged on one side of the welding gun and are respectively connected with a pair of excitation magnetic poles; in a first power-on state, one of the pair of excitation magnetic poles and the common magnetic pole are communicated with first current at the same time; in a second power-on state, the other one of the pair of excitation magnetic poles and the common magnetic pole are communicated with second current at the same time; and when the welding gun is in a working state, the first current and the second current are alternately started to form double-path pulse current so as to generate a pulse composite magnetic field capable of acting on the electric arc, the molten pool and the molten drops. According to the welding device, molten pool flowing and molten drop transition can be regulated and controlled in the transverse direction, the longitudinal direction and the vertical direction correspondingly, electric arc energy distribution is optimized, the penetration depth and molten pool swinging are increased, the welding speed is increased, root penetration is enhanced, weld joint forming is improved, welding heat input is reduced, welding deformation is reduced, and the manufacturing cost is reduced.

The invention discloses an isomeric low-alloy high-strength steel structure electric arc additive manufacturing process, and relates to the technical field of electric arc additive manufacturing, the isomeric low-alloy high-strength steel structure electric arc additive manufacturing process comprises the following steps: carrying out heat treatment and tensile test on a low-alloy high-strength steel wire to obtain test data, and establishing a process parameter-performance prediction model according to the test data; obtaining a performance demand of a target structural member, and drawing up a process parameter by using a process parameter-performance prediction model according to the performance demand; an electric arc additive manufacturing system is used for conducting electric arc additive manufacturing on the low-alloy high-strength steel wire, and a low-alloy high-strength steel structural part is obtained; acquiring temperature information of the low-alloy high-strength steel structural member by using an infrared thermal imager, and adjusting the process parameters in real time according to the temperature information to obtain a target structural member; gradient distribution of precipitation strengthening phases is achieved, isomerism of alloy element in-situ strengthening low-alloy high-strength steel materials is achieved, and the diversified requirements of target structural parts for different strengths and plasticity are met.

The invention relates to the technical field of welding quality detection, in particular to a surfacing process quality monitoring method, system and equipment, and the method comprises the steps that a dynamic parameter set of the surfacing process is obtained in real time, and the dynamic parameter set comprises arc characteristic parameters, temperature field distribution parameters and mechanical state parameters; setting an edge computing node, accessing the welding quality prediction model, inputting a dynamic parameter set, and outputting a quality evaluation result containing a defect probability and a fusion depth prediction value; a welding parameter adjusting instruction is generated based on the quality evaluation result, and the operation states of a welding power source and a wire feeding mechanism are adjusted in real time; and updating the dynamic parameter set according to the welding parameter adjusting instruction and transmitting the dynamic parameter set to the industrial control switchboard based on the edge computing node. According to the invention, the problem of uncontrollable instantaneous quality fluctuation caused by strong coupling of multiple physical fields in the surfacing process is effectively solved.

The invention discloses a welding robot self-adaptive control system and method based on digital twinning, and relates to the field of welding robots. Comprising a multi-source data acquisition module which acquires real-time information by using a multi-mode sensor array; the multi-source data fusion module is used for fusing the collected information, fusing the data by adopting an improved self-adaptive Kalman filtering algorithm, introducing a nonlinear compensation item to carry out data compensation, and determining the posture of the welding gun; the digital twin model is used for establishing a simulation model by adopting a thermal-mechanical coupling field and determining output parameters of a welding gun in combination with welding requirements; and an adaptive control module. According to the method, the state equation and the observation equation of the welding gun are established, the process noise and the observation noise are introduced, control errors caused by the noise can be prevented on the basis of achieving closed-loop feedback control over the pose of the welding gun, meanwhile, a nonlinear compensation item is added, and the control precision of the pose of the welding gun is improved.

The invention relates to the technical field of intelligent welding and welding processes, in particular to a double-wire welding process parameter prediction method based on a machine learning method. The method comprises the following steps that welding parameters and weld joint morphology parameter data are obtained through a welding process test, the data are preprocessed, and a data set is established; constructing a back propagation neural network model based on the data set, constructing a support vector machine model, and training the back propagation neural network model and the support vector machine model by using the data set; evaluating the trained back propagation neural network model and the support vector machine model, and selecting an optimal model as a model for actual industrial application; and inputting parameters required by a weld bead to be welded into the selected model to obtain double-wire welding process parameters. According to the design, the welding process parameters are predicted through the method based on machine learning, so that the quality stability and efficiency of double-wire welding are improved.

The invention relates to the technical field of metal net welding, in particular to a welding control method and system for the metal net production process, and the method specifically comprises the steps that firstly, functional areas are divided based on the geometric structure of a metal net, temperature changes in thermal imaging images of all the functional areas are analyzed in real time, and weights of different areas are defined; environmental parameters and equipment state data are collected in real time through multiple types of sensors, and comprehensive deviation of materials, environments and equipment at all moments is calculated in combination with material characteristic parameters; adjusting the proportion parameter of each functional area at each moment based on the weight and the comprehensive deviation, and dynamically adjusting the welding power of each functional area at each moment in combination with a self-adaptive PID (Proportion Integration Differentiation) algorithm; the problems that under traditional unified parameter control, the weld quality is not uniform, and the production line rhythm and energy consumption cannot be considered at the same time are solved, and the production efficiency is improved while the welding quality is guaranteed.

The invention discloses a method for assisting high-temperature alloy TLP connection by adding high-entropy alloy nanoparticles to a middle layer, and relates to the technical field of welding. The method comprises the following steps: preparing high-entropy alloy nanoparticles; mixing the high-entropy alloy nanoparticles with the powder intermediate layer to obtain a composite intermediate layer; and under the vacuum condition, the high-temperature alloy to be welded is welded through the composite middle layer based on the TLP welding method, and a welded part is obtained. According to the method, the high chemical mixed entropy filling material is reasonably selected and introduced, so that elements of the middle layer and the high-alloying base metal are fully interacted, the variety and content of chemical components of a welding area are greatly increased, the chemical mixed entropy of each area is correspondingly increased at the same time, and each area of the joint is in a high entropy state; by adding the nanoparticles, growth of intermetallic compounds / eutectic phases can be inhibited and delayed, the crystal nucleus size is reduced, and the method has an effective effect on improving the mechanical properties, especially plasticity and toughness, of diffusion welding joints.

The invention relates to an automatic tracking system and method for a large-scale space welding seam mobile mechanical arm. The system comprises the mobile mechanical arm, a visual sensor, a welding gun and a computer. The visual sensor collects large-scale to-be-tracked weldment surface point cloud data in a multi-view mode and transmits the data to the computer. The computer registers and splices the point cloud data through an algorithm, identifies and extracts weld joint feature points, plans a tail end moving track of the six-degree-of-freedom mechanical arm and a moving path of the three-degree-of-freedom moving chassis according to space tracks of the weld joint feature points, and converts the moving tracks and the moving path into motion instructions; the computer sends a motion instruction to the movable mechanical arm, the six-degree-of-freedom mechanical arm drives the welding gun to finish precise tracking of a space trajectory of a welding seam feature point along a tail end moving trajectory, the three-degree-of-freedom movable chassis tracks and moves along a moving path, and the welding seam feature point is obtained through alternate trajectory tracking of the six-degree-of-freedom mechanical arm and the three-degree-of-freedom movable chassis. And moving tracking of the large-scale space welding seam is achieved.

A reinforcement beam for an automotive component is continuously formed with a metal sheet that is roll formed to have at least one tubular portion that extends along a length of the reinforcement beam. A solid state forge weld is formed between an edge of the metal sheet and an intermediate portion of the metal sheet to close a seam that extends along the tubular portion of the reinforcement beam. Prior to forming the solid state forge weld, select portions of the metal sheet are heated to a desirable welding temperature with a high frequency current delivered by electrical contacts to opposing sides of the weld seam. The desired welding temperature may burn off a galvanized coating on the metal sheet at the select portions prior to forming the solid state forge weld that is generally void of zinc inclusions.

The invention discloses a laser-assisted helium arc penetration fusion welding method, and belongs to the technical field of welding. The problems that according to an existing paraxial laser-electric arc composite structure, the welding track is difficult to control, the energy coupling efficiency is low due to laser energy loss, and weld joint partial melting and undercut defects are caused due to uneven distribution of a welding temperature field due to a generated asymmetric heat source are solved. The welding method comprises the steps of 1, pretreatment; 2, after being focused by a focus lens, the laser is divided into three laser beams through a spectroscope, then the three laser beams are converged again through a plurality of reflectors, three fan-shaped light spots are formed on the surface of the to-be-welded plate after convergence, and finally the focal plane position of the laser is adjusted to be located on the surface of the to-be-welded plate; 3, setting welding current and arc voltage; and fourthly, laser is used for assisting helium arc penetration fusion welding. The method is used for laser-assisted helium arc penetration fusion welding.

The invention discloses a Zr / TiC composite reinforced 7075 aluminum alloy wire as well as a preparation method and application thereof, and the aluminum alloy wire comprises the following chemical components in percentage by mass: 5.8%-6.1% of Zn, 2.1%-2.4% of Mg, 1.4%-1.7% of Cu, 0.6%-2.0% of Zr, 0.3%-2.8% of Ti, less than or equal to 0.15% of Cr, less than or equal to 0.15% of Fe, 0.1%-0.4% of C and the balance of Al and other inevitable impurities. The preparation method comprises the following steps: smelting an aluminum alloy ingot by using a pure Al ingot, pure Zn particles, pure Mg particles, pure Cu particles, an Al-Zr intermediate alloy and an Al-Ti-C intermediate alloy, and carrying out homogenization, hot extrusion and drawing reducing deformation processing on the ingot to obtain the wire. An electric arc additive structural part prepared from the aluminum alloy wire is stable in quality and good in mechanical property.

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, for operation of an assembly robotic system. In one aspect of the disclosure, the assembly robotic system includes a tool coupled to a robot device and configured to be selectively coupled to a first object. The assembly robotic system also includes a welding tool, one or more sensors configured to generate sensor data, and a controller. The controller is configured to control the tool to couple the tool to the first object based on the sensor data, control the robot device to bring the first object into a spatial relationship with a second object, and generate a weld instruction to cause the weld tool to weld a seam formed between the first and second objects. Other aspects and features are also claimed and described.