University of Tennessee, KnoxvilleFrom the SelectedWorks of Jing Wu

Fall September 21, 2014

Research evolution onintelligentized technologies for arcwelding process.pdfS.B. Chen, Shanghai Jiaotong University

Available at: https://works.bepress.com/jing-wu/29/

Journal of Manufacturing Processes 16 (2014) 109–122

Contents lists available at ScienceDirect

Journal of Manufacturing Processes

j ourna l h o me page: www.elsev ier .com/ locate /manpro

Technical paper

Research evolution on intelligentized technologies for arcwelding process

S.B. Chen ∗, N. LvIntelligentized Robotic Welding Technology Laboratory (IRWTL), School of Materials Science and Engineering, Shanghai Jiao Tong University (SJTU),Shanghai 200240, PR China

a r t i c l e i n f o

Article history:Received 18 April 2013Received in revised form 9 July 2013Accepted 18 July 2013Available online 21 September 2013

Keywords:Intelligentized technologyWelding manufacturingArc welding processVisual sensingMulti-information integrationKnowledge modelingIntelligent controlRobotic weldingGuiding and trackingAutonomous welding robot

a b s t r a c t

This paper presents some new evolutions of research works in the IRWTL at SJTU on intelligentizedtechnologies for arc welding dynamic process and robot systems, including multi-information sensingof arc welding process, such as characteristic extraction of weld pool image, voltage, current, and sound,arc-spectral features; multi-information fusion algorithms for prediction of weld penetration; intelli-gentized modeling of welding dynamic process; intelligent control methodology for welding dynamicprocess; intelligentized technologies for robotic welding, such as guiding and tracking seam technol-ogy and intelligent control of weld pool and penetration in robotic welding process; and developmentof autonomous welding robot system for the special environment. The ideas of intelligentized weld-ing manufacturing technology (IWMT) and intelligentized welding manufacturing engineering (IWME)are presented in this paper for systematization of intending researches and applications on intelligen-tized technologies for modern welding manufacturing. The studies of intelligentized welding presentedin this paper establish the foundation work of intending researches and applications on intelligentizedtechnologies for modern welding manufacturing.

© 2013 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.

1. Introduction

With development of modern manufacturing technologies, itbecomes an inevitable trend to realize automatic, robotic, flex-ible and intelligentized welding manufacturing [1–7]. As is wellknown, welding technology has been developed and evolved fromthe original handworked craft to the modern systemic technicalscience, and it is related to material, mechanical, electrical, con-trol, computer sciences and other extensive subject technical fields.Studying and simulating intelligent action and function of welder’soperation is significant for development of intelligentized roboticwelding [8–11]. To realize automatic welding similar to welder,three essential technical steps are necessary. The first is to senseand acquire information of the welding dynamic process [12–23],similar to human sensing organs for detecting the interior and exte-rior welding conditions; the next is to identify characteristics ofthe welding process [24–29], i.e. modeling of the welding dynamicprocess; the third is to develop the human-brain-like controller toreason controlling strategies [30–36].

∗ Corresponding author. Tel.: +86 021 34202740.E-mail address: sbchen@sjtu.edu.cn (S.B. Chen).

As far as easing welder works, one of the main functions inmodern welding manufacturing systems is to substitute or par-tially replace the physical force and brains calculation/function ofa welder with machines. It is one of hot topics in advanced weld-ing manufacturing technologies to simulate and realize a welder’sactions by some intelligent machines, e.g., autonomous weldingrobot.

The most current welding robots serving in practical productionstill are the teaching and playback type robot, frequently, which cannot well meet quality and diversification requirements of weldingproduction because this type of the robots do not have the auto-matic function to adapt to circumstance changes and uncertaindisturbances during welding process [37,38]. In practical weld-ing production, welding conditions are often changing, such as theerrors of pre-machining and fitting work-piece would result in dif-ferences of gap size and position, the change of work-piece heatconduction and dispersion during welding process would bring onweld distortion and penetration odds. Moreover, manufacturing ofsome large equipments need continuous and moving welding inlong path and all position [39,40]. In order to overcome or restrainvarious uncertain influences on welding quality, it is promising todevelop and improve intelligent technologies for welding robots,such as vision sensing and multi-sensing of robotic welding pro-cess, recognizing welding surroundings, autonomously guiding and

1526-6125/$ – see front matter © 2013 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.http://dx.doi.org/10.1016/j.jmapro.2013.07.002

110 S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122

tracking seam, and real-time intelligent control of robotic weld-ing process. Therefore, developing intelligentized technology forimproving current teaching and playback welding robot is neces-sary to meet high quality and flexible requirements for weldingproducts and advanced manufacturing [41–49].

Aiming at the bottleneck technological problem/issue of effec-tive control of weld quality during automatic and robotic arcwelding process, this paper will present mainly researching workson intelligentized methodology for arc welding dynamical pro-cess in the Intelligentized Robotic Welding Technology Laboratory(IRWTL), Shanghai Jiao Tong University, which involves visualinformation acquiring, knowledge modeling and intelligent controlof arc welding dynamical process.

In our previous work [5–7] contains some research resultson technical composition of intelligentized welding, the Ref.[9] showed a functional realization of intelligentized weldingrobot systems it’s called as the locally autonomous intelligentizedwelding robot systems, which could realize some primary intelli-gentized functions of welding robot systems. Based on our previousresearch, this paper presents some further evolutions of the intelli-gentized technologies for robotic welding [19–24], which containsmulti-information acquirement of arc welding process, such asextraction of weld pool image, voltage, current, and sound, spec-tral features; multi-information fusion algorithms for predictionof weld penetration [17–24]; intelligentized modeling of weldingprocess and robot system [26–29]; intelligent control methodol-ogy for welding dynamic process and robot systems [9,35–38,42];guiding and tracking seam technology in robotic welding process bycombining arc sensing and visual sensing [39–41]; Furthermore, anew autonomous welding robot scheme with combined wheel andfoot for getting across obstacles in all space position motion will beshown in this paper [44,45].

As is well known, arc welding is one of the most representa-tive welding techniques with wide application in modern weldingmanufacturing, this paper discusses main intelligentized techni-cal problems related to the intelligentized welding manufacturingtechnology (IWMT) and intelligentized welding manufacturingengineering (IWME) combining with an arc welding technics,which could be also useful to other welding techniques.

2. Ideas on intelligentized welding manufacturing

According to the scientific and technical contents related todevelopment of modern welding manufacturing technology, a con-cept on intelligentized welding manufacturing technology (IWMT)as shown in Fig. 1 is introduced in this paper. It shows for the keyscientific and technical formwork of the IWMT, which containsthree advanced manufacturing fields: flexible welding manufac-turing and technology (FWMT), intelligentized robotic weldingtechnology (IRWT), and agile welding manufacturing and technol-ogy (AWMT); and key technical elements and system techniquesof the IWMT [8–11].

The intelligentized welding manufacturing technology (IWMT)is mainly related to key intelligent technical elements: sensingwelding process for imitating welder’s sense organ function,knowledge extraction and modeling of welding process for imitat-ing welder’s experience reasoning function, and intelligent controlof welding process for imitating welder’s decision-making oper-ation function. Fig. 2 shows some key scientific and technicalproblems in IWMT.

3. Information sensing during welding dynamical process

The arc welding dynamic process contains complicated,stochastic and uncertain information. Monitoring the state of

Intel lige ntized Wel ding

Manufacturing Technology - IWMT

Intelligentized Robotic

Wel ding Technolo gy --IRWT

Welding

technical

exper t

system

Welding

proce ss

kno wledg e

mod eling

Welding

equipment

intelli-

gentization

Welding

process

infor mation

sens ing

Welding

proce ss

intelligent

control

Welding

quality

insp ect

evaluating

Welding

machine

flexible

system

Flexible Welding

Manuf.&Tech.-FWMT

Agile Welding

Manuf.&Tech.-AWMT

Fig. 1. The framework of intelligentized welding manufacturing technology(IWMT).

Main Problems of

Intelligentized

Welding

Technology

Simulation &optimization of welding technique

Designing of welding technical expert system

Sensing of multi-information in welding process

Knowledge modeling of welding dynamics

Intelligent control of welding dynamic process

Intelligentized welding robot & equipments

Intelligentized inspection of welding quality

Extration of characteristics of welding process

Intelligentized technology for welding engineering

Fig. 2. The key scientific and technical problems in IWMT.

welding process and sensing of arc welding dynamic process isvery important for real-time controlling the welding process. Manysensing methods for welding process have been used in consid-eration of the disturbance from arc, high temperature, vibration,electromagnetic fields and the features of the process, such asultrasonic for penetration, arc pressure method and arc light forvibration information of the weld pool, infrared thermo scope forwelding temperature field, X-ray for shape of weld pool, acousticand visual sensing [12–16]. Thus, in order to obtain the effec-tual features of arc welding process for real-time control of weldquality, various signal processing methods have been applied forinformation of welding process, such as processing algorithms for

Arc welding

process

multi-inform

Multi-inform

fusion

processing

Weld quality

characteristics

Mov.-position

Volt.-current

Pool-vision

Arc-spectrum

Sound-gas

Fig. 3. Multi-information fusion for arc welding dynamical process.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 111

M1

M2

M3

M4

M5

M6

M7M8

CCD

Filters1

Filt ers3

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Route 3

Route 2

Route 1

Filt ers2

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Fig. 4. Three light path visual sensing system.

arc voltage, current, pool, acoustic, visual, arc-spectrum, thermic,optical, mechanical information [17–26]. Fig. 3 is a scheme of multi-information fusion for arc welding dynamical process [8].

3.1. Visual sensing and characteristic extraction of weld pooldynamical process

In order to investigate the backside width and penetration, aparticular visual sensing system with topside and backside lightpaths and filters were developed to capture topside and backsideimages of Al alloy GTAW pool simultaneously in the same frame[5,19]. Aiming at arc spectrum features of aluminum alloy weldingprocess, a visual sensing system with the composite filtering for Alalloy weld pool during pulsed GTAW was developed in [19,20].

The Ref. [20] developed a three light path visual sensing systemas in Fig. 4 for Al alloy pulsed GTAW pool. A typical Al alloy pulsedGTAW weld pool images in a frame under designed pulsed currentconditions is shown as Fig. 5. Fig. 6 shows distinct penetration statesfrom pool images.

3.2. Penetration strengthen

Taking the wavelet transform (WT)’s advantages, such asanti-noise, precise edge localization, a special image processingalgorithm was developed for extraction of Al alloy weld poolduring pulsed GTAW process. Fig. 7 shows the processing of three-direction pool images. The real-time calculating time for a frameimage was not more than 30 ms for the top image and 20 ms forback image. It was sufficient for real-time closed loop control ofwelding dynamic process as detailed in [20].

The height of weld pool surface plays an important role in real-time control of welding process, but the characteristics of weldingprocess and pool make it very difficult to measure and calculatesurface height of weld pool. In our study, the shape from shading(SFS) method [7,21] was used to calculate surface height of objectbased on single image by solving reflectance map equation of objectsurface, the key of which is modeling of reflectance map of object

Fig. 5. A frame typical Al alloy pool images.

surface and solving reflectance map equation. The imaging char-acteristics of pulse GTAW weld pool surface are analyzed and thereflectance map model was developed. The surface height of alu-minum alloy weld pool is calculated based on reflectance mapmodel of aluminum alloy weld pool, which verifies the rational-ity of general reflectance map equation of pulse GTAW weld poolas detailed in [21].

3.3. Audio sensing and characteristic extraction of arc soundinformation

Investigations show that the arc sound signal is an importantinformation for monitoring of welding dynamic and quality char-acteristic [15,23,24]. Fig. 8 shows that the weld sound intensityis used as the variable reflecting changes of welding status. Thetime domain, frequency domain and wavelet packet features wereextracted at different frequency band under different penetrationstates like partial penetration, full penetration and excessive pen-etration. Fig. 9 shows different features of three penetration states.Also some other algorithms were developed for extraction of weld-ing status, e.g. penetration characteristics [22,23].

The artificial neural network (ANN) in Fig. 10 was developedfor predicting different penetration states based on all the featuresof arc sound signal [23]. The features of arc sound signal in timedomain and frequency domain are essential factors for setting upa predication model of welding quality control. The recognitionrate of the prediction mode is shown as Figs. 3–8 and detailedin [23].

3.4. Spectrum characteristic extraction of welding dynamicalprocess and defects

Arc plasma transfers energy from power source to the work-piece and emits large amount of spectra to the surrounding space.

Fig. 6. Penetration state variation.

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Fig. 7. Processing of three-direction pool images.

0 5 10 15-1.5

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11.5

2

Time /(s)

Arc

sou

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ress

ure

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0.2

0.3

0.4

0.5

0.6

Sample point

Sou

nd p

ress

ure

s(v)

PulsePeak

PulsePeak

Fig. 8. The original arc sound signal.

The arc spectra contain abundant information related to weldingdynamic characteristics and quality status. The arc spectra dur-ing Al Mg alloy pulsed GTAW have been acquired and investigated[24,25]. The experimental systems are shown as in Figs. 3–8. Somearc spectra processing algorithms were developed for character-istic signals extraction from the original spectral information. Therelationships among these extracted signals and the defects causedby wire feed have been studied. Fig. 11 shows the processingschematic of spectrum signal of welding defect [24,25]. The defects

of seam oxidation are produced by different disturbances, i.e., theoil painted on the surface of the plate and the non-removed insol-uble oxide film of aluminum alloy.

3.5. Multi-information acquirement and fusion extraction ofcharacteristics during arc welding process

Ref. [26] developed the experimental system with multi-sensorfor acquisition of multi-information of welding dynamical process

0 50 100 1500

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0.004

0.006

0.008

0.01

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ratio

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Partial penetrationFull penetrationExcessive penetration

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0.8

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Pulse number

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ket e

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7 Partial penetrationFull penetrationExcessive penetration

Fig. 9. The characteristic extraction of arc sound signal.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 113

Fig. 10. The recognition rate of penetration by the prediction model based on arc sound features.

Fig. 11. The characteristic extraction of spectrum signal.

114 S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122

Fig. 12. Schematic diagram of the experiment systems.

and quality shown in Fig. 12. The system consists of an electronicsignal collecting module, a sound signal collecting model and a weldpool image collecting module.

By combining the three collecting modules, weld pool image,welding current, arc voltage, welding sound and spectral featurescould be collected at the same time. Therefore the welding param-eters could be controlled by the control module.

Fig. 13 shows the collected weld pool, current, voltage and soundinformation in five pulses. From the current, voltage and soundwaveforms, it is apparent that the welding process can be dividedinto weld pulse peak period and weld pulse base period. The weldpool images were collected at the pulse base period to avoid theintense disturbance in pulse peak period by the welding arc toobtain the clear weld pool image [26].

Fig. 13. Weld pool, current, voltage and sound information in five pulses.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 115

Fig. 14. Fusion model of electronic, image and sound signal.

The multi-sensor fusion model of the three sensors is shown inFig. 14. The information obtained from each of the sensors was firstprocessed by back-propagation (BP) neural networks individually.Because welding process was influenced by heat inertia, respondedto welding parameters with a time delay, the historical informationshould also be included to obtain more precise prediction resultsfor the back-side bead width. The BP neural network was trainedto obtain the BPA (Basic Probability Assignment) for each sensor.The D–S evidence theory was used to combine the BPAs and obtainthe final fusion BPA and obtain the prediction results [26].

The experiment and analysis results showed that the multi-sensor could obtain better results than a single sensor. Theprediction result by fusing three sensors was better than that by

fusing two sensors. It shows that multi-sensor information fusioncould obtain more information about the welding process andtherefore describe the process more roundly and precisely [26].

4. Knowledge extraction and modeling of welding process

As one of intelligent technical elements, developing model ofwelding process by math and knowledge methods is an essen-tial technology for the IWMT [27–31]. Intelligentized weldingrequires knowledge description of human welding manipulatingexperiences. One of key intelligent technologies is to establishknowledge model from extracting welder manipulations so thatthe computer or robotic systems could play back human knowl-edge and intelligent decision-making function. A feasible way isextracting knowledge from measured experimental data by fuzzycomputing, rough set theory and other soft-computing methods[27–31].

4.1. Knowledge model of Al alloy GTAW welding process by theRS method

Based on our previous works on knowledge modeling of thewelding process by the basic Rough Sets (RS) theory [28], the fur-ther research was completed and some knowledge rule modelsfor weld pool dynamics of aluminum alloy GTAW by the variableprecision rough set (VPRS) methods were developed as detailed in[9,29].

4.2. Knowledge extraction of arc welding dynamics by SVMmethod

Ref. [30] investigated the application of the support vectormachine-based fuzzy rules acquisition system (SVM-FRAS) formodeling of the GTAW process. The characteristic of SVM inextracting support vector provides a mechanism to extract fuzzyIF–THEN rules from the training data set. The fuzzy inferencesystem using fuzzy basis function was constructed. The gradient

Fig. 15. Schematic diagram of SVM-based fuzzy rules discovery system.

116 S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122

Table 1Part rules extracted by SVM in modeling for aluminum alloy pulsed GTAW process.

I O

t − 3 t − 2 t − 1 t

I WT LT WB I WT LT WB I WT LT WB I WT LT WB

191 10.5 17.3 8.2 216 10.5 17.2 8.6 171 8.9 17.3 5.6 192 9.5 16.1 5.7164 9.9 15.9 6.8 223 11.9 22.2 9 165 10.6 18.5 6.6 194 10.8 17.2 8.5226 10.5 19.3 12.3 160 9 16.8 7 172 9.6 13.1 5.2 183 10.1 13.4 3.2219 11.1 18.7 9.4 220 11.7 19.6 9.4 230 11.4 18.2 10.8 163 10 20.7 8.9

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Fig. 16. The AIC scheme based on SVM-FRDS for GTAW process.

Fig. 17. The seam shape by constant welding parameters: (a) topside and (b) backside.

technique is used to tune the fuzzy rules and the inference system.The schematic diagram of SVM-Based Fuzzy Rules Discovery Sys-tem is depicted in Fig. 15. Using the proposed SVM-FRAS method,we obtained the rule-based model of the aluminum alloy pulseGTAW process, shown in Table 1. Experimental results show thatthe SVM-FRAS model possesses good generalization capability aswell as high comprehensibility.

4.3. Mixed logical dynamical (MLD) modeling of pulsed GTAWduring robotic welding

The mixed logical dynamical (MLD) modeling method is usedto model the hybrid systems with interacting physical laws, logi-cal rules, and operating constraints. The study in [31] presented anovel MLD modeling framework for robotic welding process andsystems. The MLD model is then established and gives a good pre-diction quality of the back bead width of pulsed GTAW process with

misalignment during robotic welding. The study [31] shows thatthe MLD framework is a good modeling method for pulsed GTAWprocess and robotic welding systems.

5. Intelligent control methodology for welding process andsystems

Based on the knowledge models established above, some intel-ligent control schemes have been developed as will be shown asfollows [32–38].

5.1. The adaptive inverse control scheme based on SVM-FRDS forpulsed GTAW

Based on the SVM-FRAS model of the aluminum alloy pulseGTAW process in Section 4.2, the adaptive inverse controller (AIC)for pulsed GTAW process shown in Fig. 16 was developed. Here

Fig. 18. The seam shape by the closed AIC regulating welding parameters: (a) TOPSIDE and (b) backside.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 117

Fig. 19. The control curves with the closed AIC regulating welding parameters.

RSController

GTAW Process

1zubsetW )(te

bpreW Prediction Model

bW

SensingSystem

Welding pool sizes

Welding parameters

u

SignalConversion

Fig. 20. The schematic diagram of RS closed-loop control system of pulsed GTAW.

the I-controller means the inverse controller, the P-model meansthe process model for welding process, and the predict-M meansthe predicted model of the backside width of weld pool duringpulsed GTAW. The experimental results of the closed-loop controlsystem have been explained in detailed in [35]. Fig. 17 shows thecontrol effect of dumbbell work-piece in constant welding param-eters based on visual information. Fig. 18 shows the control effectof dumbbell-shaped workpiece in counstant welding parametersbased on visual information. Fig. 19 shows the weld widths undervaried heat sink in constant welding parameters.

5.2. Closed-loop control schemes based-on the knowledge modelby the RS theory

Based on the obtained knowledge rule models for weld pooldynamics of aluminum alloy GTAW by RS methods, some controlschemes were developed for realization of the closed-loop real-time control of Al alloy weld pool during pulsed GTAW as follows:

(1) The controller based-on RS for the closed-loop control sys-tem of pulsed GTAW.

The primary control scheme in Fig. 20 was used to regulate thebackside pool width by the RS controller during Al alloy pulsedGTAW.

(2) The compound control scheme with the RS and MS-PSD con-trollers for weld penetration and face-height of Al alloy pulsedGTAW.

In order to control weld penetration and the height of topsideseam during Al alloy pulsed GTAW at the same time, the advancedcontrol strategy compounded the RS controller and MS-PSD con-troller for the closed-loop control system scheme was developedas shown in Fig. 21. The controller algorithms and welding exper-iments can be found in [37]. Fig. 22 shows the photographs ofworkpiece using RS and MS-PSD controllers, the details are omittedhere.

5.3. The model-free adaptive control of pulsed GTAW

Arc welding is characterized as inherently multi-variable, non-linear, time varying and having a coupling among parameters. Inaddition the variations in the welding conditions cause uncertain-ties in the welding dynamics. Therefore, it is very difficult to designan effective control scheme by conventional modeling and controlmethods. A model-free adaptive control algorithm has been devel-oped to control the welding process [38], which only needs theobserved input output data and no modeling requirement for con-trolled welding process. Thus, the developed model-free adaptivecontrol provides a promising technology for GTAW quality controlas detailed in [38].

6. Intelligentized technologies for robotic welding processand systems

Based on the signals collected during the welding process,their features could be used for seam tracking of robotic weld-ing, as well as for monitoring and controlling of welding dynamicprocess [39–45]. Developing intelligentized robotic welding tech-nology (IRWT) is crucial and necessary for realization of IWMT.The function of IRWT system showing as Fig. 23 should involvevisual sensing welding environment, recognizing weld workpiece,seam type, guiding weld starting, tracking seam, instructing tech-nics, programming paths and parameters, dominating weld pooldynamical characters, control seam forming and quality, diagnos-ing failures, and so on. Hence, the IRWT can be considered “anelement” in IWMT systems, a platform integrated by intelligen-tized welding technologies. Based on single intelligent weldingrobot with collaborating positioner, an intelligentized weldingflexible manufacturing cell (IWFMC) could be established [48].

RSController

1zIbsetW )(2 te

bpreW Prediction Model

bW

SensingSystem

Welding pool sizes

Welding parameters

I

SignalConversion

MS- PSDController

1z

fV1fse tH )(1 te

fH

fV1

GTAW Process

fpreH

Feed forwardCon troller

fV

fV2g

Fig. 21. The compound control systems with the RS and MS-PSD controllers for Al alloy pulsed GTAW.

118 S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122

Fig. 22. Photographs of work piece using the RS + MS-PSD controller.

Fig. 23. The technological composition of intelligentized robotic welding technology (IRWT).

The IWFMC could be considered as “a molecule” in the IWMTsystems, which could autonomously complete a certain weldingtask.

Realizing applications in a mass production in practical weld-ing engineering through IRWT, IWFMC and MAWFMC/S, in anotherwords, replacing human welding manipulation with intelligentizedmachinery, is a pursuing goal of the IWMT.

6.1. The seam tracking during robotic welding by visual sensing

The tracking seam technique and on-line quality control for thecurve weld during robotic pulsed GTAW process was developedbased on passive visual sensing system. Fig. 24 shows seam trackingsystem of the visual sensor for robotic welding [7,43]. Fig. 25 showsthe visual image collected when implemented seam tracking.

Fig. 24. S welding robot system with visual sensor.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 119

Fig. 25. Weld pool image during robotic welding.

6.2. The 3D seam tracking during robotic welding by combiningarc sensing and visual sensing

The guiding and tracking seam technique for three dimension(3D) curve during robotic pulsed GTAW process was developed bythe combination of arc sensing for torch height or arc length withpassive visual sensing for correct an error of seam or torch deflexion[45]. The seam tracking scheme of robotic welding process is shownin Fig. 26. Processing of weld pool image and identifying of seamand gap changes are realization through the image as in Fig. 27. Thetracking control algorithms and experiments can be found in [45].

The arc voltage signals of AC pulsed GTAW in one cycle con-tain base value and peak value voltage. The base value voltage isproduced during piloting arc process, which cannot be used to char-acterize the arc length. Whether the quality of welding is goodlargely depends on peak currents, while the corresponding peakvoltage is strongly related to the arc length [45].

Another robotic welding system with seam tracking functionsfor pulse-MAG welding was also developed. The system couldachieve the seam tracking function at straight line and curve lineseam welding process. Fig. 28 shows the seam tracking effect of theMAG robotic welding [45].

6.3. Real-time control of pool and penetration during roboticwelding by visual and arc sensing

The real-time control of pool and penetration during roboticwelding is very important for achieving intelligentized welding.The visual information and arc signal could be used for on-linemonitoring of the welding quality [7,50]. Fig. 29 shows the experi-mental system and visual sensing system. Fig. 30 shows the imagesof different penetration states. The closed-loop control results ofpenetration and seam shape of robotic welding with visual sensingare presented in Fig. 31.

7. Development of autonomous welding robot system forspecial environment

In many practical welding manufacturing sites, such as weld-ing for ship structures and large tanks, there is a need for the

Fig. 26. Seam tracking scheme of welding robot system.

Fig. 27. Weld pool image during robotic welding.

120 S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122

Fig. 28. The seam tracking results of the robotic welding.

Fig. 29. Picture of robotic welding system.

autonomous moving welding in a long distance and complicatedspace position. It also requires the welding robot with adsorbentand climbing functions for all position motion and flexible posechanges for various joints, such as the filet, lap, vertical, inclined

welding, and so on. Hence, a primary autonomous moving weldingrobot system with a combination of wheels and foot for adsor-bent climbing and getting across obstacle was developed. It hassome intelligentized functions to realize robotic welding, such asvisual and ultrasonic sensing, automatic program of welding pathand technical parameters, autonomous guiding and tracking weld-ing. The photo of the welding robot system for autonomous movingand across obstacle is shown as in Fig. 32. It integrates the aboveintelligentized technologies for robotic welding and realizes somewelder’s intelligent functions, such as detecting and recognizingweld surroundings by visual sensing technology, identifying theinitial position of weld seam, autonomously guiding weld torch tothe weld starting and tracking the seam, real-time control of arcweld pool dynamics.

In order to ensure the obstacle-crossing and the welding processare conducted in the same time, it is necessary to analyze the tra-jectories of each wheel in simulation. It can determine the instantprocess of the obstacle-crossing by the trajectories of each wheel.Through this method, the total welding time can be reduced, and

Fig. 30. Images of weld pool over different penetration states.

Fig. 31. The closed-loop control results of penetration and seam shape of robotic welding with visual sensing.

S.B. Chen, N. Lv / Journal of Manufacturing Processes 16 (2014) 109–122 121

Fig. 32. An autonomous wall-climbing welding robot system.

Fig. 33. The track programming of different wheel motion during the robot obstacle-crossing.

Fig. 34. An autonomous welding robot system during welding process.

the process of obstacle-crossing can be observed from the results.The study carried on the tangent point on one of front, middleand rear wheels as the reference point. When obstacle-crossing isjust half-finished, the reference point is in local minimum positionin the curve. The programming tracks of robot obstacle-crossingmotion are showed in Fig. 33 [47]. Fig. 34 shows the welding processof the welding robot system. The details are in [46,47].

8. Conclusions

This paper presents some new evolutions of research worksin the IRWTL of SJTU on intelligentized technologies for sensing,modeling and control of arc welding dynamic process and robotsystems. Based on the intellgentized welding related to scien-tific and technical researching contents, the ideas of IWMT andIWME are presented in this paper for systematization of intend-ing research fields, technical developing directions and applications

on intelligentized technologies for modern welding manufacturing.The studies of intelligentized welding manufacturing technology(IWMT) and intelligentized welding manufacturing engineering(IWME) in this paper are established the foundation work of intend-ing researches and applications on intelligentized technologies formodern welding manufacturing.

At present, there is an evident trend that artificial intelligenttechnology is penetrating into almost all manufacturing and engi-neering processes. The motivation of introducing the ideas IWMTand IWME in this paper is to promote systematization researchfor forming an effective combination of modern welding manufac-turing and artificial intelligent technology. And the development ofmodern welding technology is changing from traditional handicraftto modern science manufacturing; from controlling the weldingquality only through the shape of welding pool, but also the form-ing mechanism control; and also from the cellular manufacturingto scale manufacturing.

Acknowledgments

The works of this paper were supported by National Natu-ral Science Foundation of China, No. 51075268; No. 60874026,and Shanghai Sciences & Technology Committee under Grant No.11111100302, Shanghai, PR China.

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