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Expert Systems - An Application to Welding Adaptive Control
EXPERT SYSTEMS· AN APPLICATION TO WELDING ADAPTIVE CONTROL
E. Si ores Department of Mechanical Engineering University of Wollongong ABSTRACT Preprogrammed robots are nowadays being replaced by intelligent ones equipped with sensors and suitable electronics and software technology capable of controlling the welding process in an adaptive manner. The key elements for such technological progress are robust sensors and expert systems. Ultrasonic sensors are used in this work for the reliable interrogation of both the seam path and the weld bead penetration depth. They are positioned on both sides of the seam to derive a whole picture of sequential events transpiring in the target environment. The expert system provides the mechanism for making intelligent decisions based on sensors information in conjunction with a rule base to reconcile competing goals regarding cost, quality and productivity. In this paper an attempt is made on developing a generic approach to autonomous robotic arc welding thus offering a viable solution to the problems encountered in small batch size manufacturing industries.
predominant function is to identify the interrelationship between process input (welding path and speed, current, arc voltage and wire feed) and output parameters (geometric weld size and shape), and subsequently feed them back into the system to compensate for variations between the predicted and actual dynamic weld conditions. Although such additional parameters as solidification and cooling rates of weld pool material with respect to its composition are essential for the overall weldment quality evaluation and can be monitored no tangible adaptive control scheme has effectively emerged yet (2).
2.
APPLICA TIONS OF EXPERT SYSTEMS IN AUTOMATED WELDING
Electronic hardware interfacing and software implementation are the two vital components for providing communications network between the welding facilities. robotic controller and sensor devices. A great deal of process data is generated during the course of welding manufacturing. These data can be divided into three prime categories representing the production stages of designing, processing and inspection. A considerable effort is being devoted worldwide to implement artificial intelligence in welding engineering to manipulate these data. Expert systems which form the basis of interactive consultation systems are used to interpret welding data and to derive conclusions in a robust manner.
a
1. INTRODUCTION The move towards automation was to extend beyond the capabilities of human welder in terms of efficiency. consistency and resistance to fatigue. With the continuous developments in automated systems for applications into an increasingly demanding welding production environment the urge for robotisation evolved. An important factor contributing to the development of robotic welding systems is the high degree of flexibility which enables them to be adapted for diversified welding applications. Early industrial robots, however, suffered from a serious drawback; lack of accountability of in-process joint variations and workpiece misalignments encountered mainly due to material imperfections and heat distortion.
Early versions of such expert systems operated in an interactive mode by asking the operator a series of questions and subsequently recommending a suitable answer. Since no reasoning mechanism was built in. the operator was asked to provide answers to a string of questions, without any feedback. Recently developed expert systems. however, are re-structured and an immediate feedback on the effect of each answer is given thus acting as advisory rather than instructing systems. This friendlier updated approach has had a dramatic impact on user acceptability
The most promising solutions emerged when adaptive control strategies are applied using sensors to bring the process into conformance with the workpiece (I). The variations commercially available sensors employed for joint tracking can be broadly classified as tactile and remOle. whereas the sensing techniques as direct or indirect. Their 93
leaving them with the impression that they are in control of the system. rather than vice-versa (3).
Commercially available systems are now available for assisting in storage and retrieval of welding procedures. Their capabilities extend beyond searching and identifying suitable procedures from the documented ones to new areas of more diffuse knowledge, deducing new welding parameters and qualifying the resulting specification to comply with relevant standards and approved codes of practice (5). The derivation of welding parameters using knowledge-based expert systems is carried out utilising mathematical models extracted from empirical data. Their implementation allows the user to compromise between process tolerance and productivity issues (Figure 1).
Although the interpretation of welding manufacturing design is considered to contain elements of ambiguity. the ultimate goal state is to join structural components from prescribed conditions and constraints. Factors involved in the welding design stage include choice of welding technique. workpiece preparation method. consumable material type and welding procedures to produce welds of sound quality. Many expert systems have been developed covering the wide spectrum of all attributes involved in welding design . These procedure generator expert systems aid the decision making process rather than being decision makers themselves. Expert systems assisting in the choice of welding process are enriched with static knowledge on estimated costs and quality with the final decision made by the user. while choice of weld preparation expert systems are enhanced with graphic capabilities used as a design aid in calculating the volume of proposed weld preparations. Additionally. they are capable of deciding how many weld beads are required for each pass and the number of passes needed to fill the entire joint geometry. Expert systems have also been devised to accommodate techniques for filler material selection according to workpiece material. The closest match to the required properties is indicated from an exhaustive search of material characteristics stored in a knowledge-base.
,. EXPERT SYSrn.1
.................................. MICROPRCCESSOR CONTRalER
... PO'NER saJRCE
----- - -_ ... -- .. ---- ... WIRE FEEDER
j
t ,,~--...... MANIPU LATORARM
.. ................ SENSOR f-O...DER
At runtime. expert systems cycle through matching. conflict resolution and action operational phases and try to select and execute appropriate actions. While Fig. 1 _~l:Itomated welding process control using an Expert System the focus of this in-process approach centres on weld execution, its impact is distributed over selected aspects of all stages of welding. Thus. during weld 3. INTELLIGENT WELDING planning the expert system receives data about the SYSTEMS FOR ROBOTIC weld set-up from the welding design knowledge base. ASSEMBL Y OPERATIONS Information includes part geometry and weld input parameters such as current. voltage. travel speed. wire diameter and feed. number of passes and bead Essential ingredients of an intelligent welding system sequence. During weld execution. the expert system are: sensors for measuring process performance. a receives information about the weld operation from knowledge base filled with information on process various sensors. Based on its evaluation of this operation and a strategy for welding manufacturing information. expert systems can identify conditions sequences. The implementation of direct and indirect varying from the pre-set weld procedure. analyse sensing methodologies is of an extreme importance. these conditions and modify the procedures Placing the transducers in the weld's close proximity accordingly. They can subsequently advise the gives rise to problems associated with the harsh welding control subsystem as to how to handle these environment around the vicinity of the arc. Indirect anomalies when occurring during welding . As part approaches utilise the sensors in either the prepass or of its analysis of weld conditions. the system may preview mode. While the first technique avoids the provide in-process inspection and fault detection and hostile welding zone it is insensitive to variations diagnosis. Although such control systems are highly during welding, the latter is able to compensate for autonomous and require little operator intervention using near real-time corrective actions. With the their decision can be depicted in a visible and direct sensing approach. however. results are obtained transparent manner. After welding, the data can be by monitoring the joint at the point of welding thus. further analysed to identify and extract any errors in alleviating any time delays exhibited using the upstream operations. system performance. scheduling indirect approach. information and statistics about the weld operation. such as total arc-on time, cycle time, deposition rate Although sensor hardware is still in a period of per hour, and maintenance time. development and flux the following is an outline of current technologies available. In response to the
94
needs of the welding industry early developments concentrated on tactile sensing devices making use of the seam configuration as a mechanical guide. Although their low cost has attracted many users their implementation was limited due to their incompatibility to interrogate complex weld centreline geometries and to keep constant contact with rough workpiece surfaces.
4.
Welding quality requirements are demanding and increasing levels of quality are achieved only if both product and process are inherently capable of attaining them. Post-welding non-destructive evaluation of components is usually carried out to assess product integrity. The interpretation of data, however, requires intensive use of human expertise and this labour intensive work is the principal reason for high inspection costs. Even the most sophisticated and automated test systems are hampered by human ability to interpret test results. On-line quality evaluation in welding may lead to errors and reduction in reliability, while during offline monitoring the vast amount of data generated by the test require sometimes several man hours to extract relevant information and to make interpretation possible. Expert systems can adequately cope with this dilemma, either by rapidly sentencing repetitive but simple date on-line, or by filtering the large quantities of data produced off-line. A sophisiticated non-destructive testing application in welding manufacturing is the one of ultrasonic sensing for welding process monitoring and adaptive control. The technique makes use of the ultrasonic time of flight principles in which high frequency electronic pulses are transformed into mechanical vibrations through piezoelectric crystals and in turn into elastic acoustic waves (8). Returned signals having amplitude and analogues to size, shape and orientation of reflectors, and time of flight separation proportional to distance between transducer and reflector are collected and analysed in real-time. Both these parameters are digitised of all three consecutive
The remote sensing method encompasses a plethora of techniques such as vision, electromagnetic, thermal and others utilised to control joint tracking and well fill adaptively (6). Within this group one successful contribution has been the development of the through·the-arc sensing. Due to its ease of use, the relatively low cost and the ability to compensate for the heat distortion this technology has been established into a wide variety of welding applications. It makes use of an oscillating torch to track across the weld seam with the electrode tip acting as a transducer. Weld filling is accomplished after both sidewalls have been interrogated and the torch is positioned over the weld centreline. Sensors inabilities, however, to follow complex seam contours and to weld non-ferrous materials are the only major drawbacks accompanying this technology. The most advanced remote sensing technology that has emerged is machine vision and is capable of generating three-dimensional profiles of weld joints. Projected planes of laser light produce illuminated stripes on the weld joints which are viewed by a solid state camera. Digitised images portray such essential ingredients for adaptive control as seam centreline, surface convexity, wetting points and undercut. Latest developments in laser/vision sensors include adaptive control using expert systems to optimise the welding process with respect to weld quality levels by correlating between the manufacturing conditions and the severity of the technical specifications (7). I.P
A/IAl..OQUE INPUT SICIIAl. FROII ULTRASOHASCOPE
ROBOTIC WELDING PROCESS MANAGEMENT WITH EXPERT SYSTEMS AND ULTRASONIC SENSORS
p
SIQlAL COHDITIONEltS
SIQW.
Fig. 2 Schemata of welding process sensing systems 95
OEF"Ecr AHPtIruDE REF"ERElICE
echoes that appear on a single trace. This phenomenon of variable echoes is caused due to beam spread effects and the characteristic control fashion that acoustic waves propogate in media. Data from scanned weld pool sidewalls are simultaneously collected and deconvoluted during the process of welding. Newly formed weld pools are interrogated from both sides and data are utilised for accomplishing the adaptive control (Figure 2). The expert system implemented is prompting for process parameter settings if results are different from the expected and awaits for instructions to commence scanning following the weld centreline. During the interrogation procedure ultrasonic echoes from both sensors placed alongside the seam are compared, and contrasted with the predetermined set values in a continuous sampling fashion . The inference engine comprises of rules containing information about the welding process in concern such as input parameters and their inter-relation with weld pool size. If the corresponding echoes from each multiplexed transducer comply with the preset signals no action is taken. If they don't, then action is taken to restore process harmony by acting upon both the weld pool placement and shape adjusting the electrode tip position relative to weld centreline and/or the melting rate respectively so that modelled requirements are fully met (Figure 3). This in-process equilibrium maintenance with respect to desired matching of reference quality tolerances is a distinct characteristic of preventative quality designed into the product leaving any further post weld acceptance criteria relegated to supplementary roles. Some more attractive features of the intelligent ultrasonic sensing welding system include capabilities such as welders and inspectors training since it can display three dimensional images of weld pool silhouettes (9) .
5.
REFERENCES. I.
Boughton, 0 ., et ai, "Towards the automation of arc welding" , Central Electricity Generating Board, Research Report, Vol. 9, June, 1979. Cook, G.E., "Feedback and adaptive control in automated arc welding systems", Metal Construction, Vol. 13, No. 9, 1981. Lucas, W. and Brightmore, A., "Expert systems in welding", First Int. Conf. on Computer Technology in Welding, Paper 47, London, UK, June 1986. Yapp , D., et aI, "The potential of intelligent computer systems in welding", First Int. Conf. on Computer Technology in Welding, Paper 44 , London, UK, June 1986. The Welding Institute, "Annual research report - product literature", Abington, UK, 1986. Hanright, S. , "Robotic arc welding under adaptive control - A survey of current technology". Welding Journal, Vol. 65, No. 11 , November 1986. Servo-Robot, "Advancing the vision of intelligent adaptive welding" , Product Literature, Quebec, Canada, 1989. Siores, E., et aI, "Adaptive control in arc welding utilising ultrasonic sensors", Second Int. Conf. on Developments in Automated and Robotic Welding. Paper 8, London, UK, November 1987. Siores, E. , "An autonomous welding and inspection system", Thirty Seventh Int. Conf. on Welding Technology for Profit, Paper 6, Sydney, Australia, November 1989.
2. 3.
4.
5. 6.
7. 8.
9.
l
DYNAMIC) DATA
.
I I
CONCLUSIONS
The simultaneous management of several goals is one of the major challenges in such a system. The goals of maximised productivity. quality, cost and management policy can often conflict when variations occur in the workpieces to be welded. During welding, these goals are traded-off in light of the facts of the particular circumstances, and decisions regarding changes in welding parameters are made in real-time.
MODaL",) DATA
I II
WELDING ?RCXESS
Il
,
( "OIN T I ifCSITICN,
_ _ _ _ _Y _e_s
'I
s,z~ )
(> I
• E.X?~~T
Intelligent welding management systems comprising expert systems and sophisticated sensory devices will increasingly continue to provide solutions to the welding manufacturing industry. A great deal of research and development has been devoted on implementing such advanced technology and undoubtedly the hesitant enhancements in both software and hardware taken today will lay the foundations for the welding manufacturing systems of the future .
'1,'8.!l
~
SYSiE\1
( I Nr~:;ENC"i (KN:)M.EGE! =''-G:NE SAS" I
!L
l
(
~ EAL "7"IM E CA -;-A
r ig. 3 K.1owledge based adaptive control flow chart
96
E. Si ores Department of Mechanical Engineering University of Wollongong ABSTRACT Preprogrammed robots are nowadays being replaced by intelligent ones equipped with sensors and suitable electronics and software technology capable of controlling the welding process in an adaptive manner. The key elements for such technological progress are robust sensors and expert systems. Ultrasonic sensors are used in this work for the reliable interrogation of both the seam path and the weld bead penetration depth. They are positioned on both sides of the seam to derive a whole picture of sequential events transpiring in the target environment. The expert system provides the mechanism for making intelligent decisions based on sensors information in conjunction with a rule base to reconcile competing goals regarding cost, quality and productivity. In this paper an attempt is made on developing a generic approach to autonomous robotic arc welding thus offering a viable solution to the problems encountered in small batch size manufacturing industries.
predominant function is to identify the interrelationship between process input (welding path and speed, current, arc voltage and wire feed) and output parameters (geometric weld size and shape), and subsequently feed them back into the system to compensate for variations between the predicted and actual dynamic weld conditions. Although such additional parameters as solidification and cooling rates of weld pool material with respect to its composition are essential for the overall weldment quality evaluation and can be monitored no tangible adaptive control scheme has effectively emerged yet (2).
2.
APPLICA TIONS OF EXPERT SYSTEMS IN AUTOMATED WELDING
Electronic hardware interfacing and software implementation are the two vital components for providing communications network between the welding facilities. robotic controller and sensor devices. A great deal of process data is generated during the course of welding manufacturing. These data can be divided into three prime categories representing the production stages of designing, processing and inspection. A considerable effort is being devoted worldwide to implement artificial intelligence in welding engineering to manipulate these data. Expert systems which form the basis of interactive consultation systems are used to interpret welding data and to derive conclusions in a robust manner.
a
1. INTRODUCTION The move towards automation was to extend beyond the capabilities of human welder in terms of efficiency. consistency and resistance to fatigue. With the continuous developments in automated systems for applications into an increasingly demanding welding production environment the urge for robotisation evolved. An important factor contributing to the development of robotic welding systems is the high degree of flexibility which enables them to be adapted for diversified welding applications. Early industrial robots, however, suffered from a serious drawback; lack of accountability of in-process joint variations and workpiece misalignments encountered mainly due to material imperfections and heat distortion.
Early versions of such expert systems operated in an interactive mode by asking the operator a series of questions and subsequently recommending a suitable answer. Since no reasoning mechanism was built in. the operator was asked to provide answers to a string of questions, without any feedback. Recently developed expert systems. however, are re-structured and an immediate feedback on the effect of each answer is given thus acting as advisory rather than instructing systems. This friendlier updated approach has had a dramatic impact on user acceptability
The most promising solutions emerged when adaptive control strategies are applied using sensors to bring the process into conformance with the workpiece (I). The variations commercially available sensors employed for joint tracking can be broadly classified as tactile and remOle. whereas the sensing techniques as direct or indirect. Their 93
leaving them with the impression that they are in control of the system. rather than vice-versa (3).
Commercially available systems are now available for assisting in storage and retrieval of welding procedures. Their capabilities extend beyond searching and identifying suitable procedures from the documented ones to new areas of more diffuse knowledge, deducing new welding parameters and qualifying the resulting specification to comply with relevant standards and approved codes of practice (5). The derivation of welding parameters using knowledge-based expert systems is carried out utilising mathematical models extracted from empirical data. Their implementation allows the user to compromise between process tolerance and productivity issues (Figure 1).
Although the interpretation of welding manufacturing design is considered to contain elements of ambiguity. the ultimate goal state is to join structural components from prescribed conditions and constraints. Factors involved in the welding design stage include choice of welding technique. workpiece preparation method. consumable material type and welding procedures to produce welds of sound quality. Many expert systems have been developed covering the wide spectrum of all attributes involved in welding design . These procedure generator expert systems aid the decision making process rather than being decision makers themselves. Expert systems assisting in the choice of welding process are enriched with static knowledge on estimated costs and quality with the final decision made by the user. while choice of weld preparation expert systems are enhanced with graphic capabilities used as a design aid in calculating the volume of proposed weld preparations. Additionally. they are capable of deciding how many weld beads are required for each pass and the number of passes needed to fill the entire joint geometry. Expert systems have also been devised to accommodate techniques for filler material selection according to workpiece material. The closest match to the required properties is indicated from an exhaustive search of material characteristics stored in a knowledge-base.
,. EXPERT SYSrn.1
.................................. MICROPRCCESSOR CONTRalER
... PO'NER saJRCE
----- - -_ ... -- .. ---- ... WIRE FEEDER
j
t ,,~--...... MANIPU LATORARM
.. ................ SENSOR f-O...DER
At runtime. expert systems cycle through matching. conflict resolution and action operational phases and try to select and execute appropriate actions. While Fig. 1 _~l:Itomated welding process control using an Expert System the focus of this in-process approach centres on weld execution, its impact is distributed over selected aspects of all stages of welding. Thus. during weld 3. INTELLIGENT WELDING planning the expert system receives data about the SYSTEMS FOR ROBOTIC weld set-up from the welding design knowledge base. ASSEMBL Y OPERATIONS Information includes part geometry and weld input parameters such as current. voltage. travel speed. wire diameter and feed. number of passes and bead Essential ingredients of an intelligent welding system sequence. During weld execution. the expert system are: sensors for measuring process performance. a receives information about the weld operation from knowledge base filled with information on process various sensors. Based on its evaluation of this operation and a strategy for welding manufacturing information. expert systems can identify conditions sequences. The implementation of direct and indirect varying from the pre-set weld procedure. analyse sensing methodologies is of an extreme importance. these conditions and modify the procedures Placing the transducers in the weld's close proximity accordingly. They can subsequently advise the gives rise to problems associated with the harsh welding control subsystem as to how to handle these environment around the vicinity of the arc. Indirect anomalies when occurring during welding . As part approaches utilise the sensors in either the prepass or of its analysis of weld conditions. the system may preview mode. While the first technique avoids the provide in-process inspection and fault detection and hostile welding zone it is insensitive to variations diagnosis. Although such control systems are highly during welding, the latter is able to compensate for autonomous and require little operator intervention using near real-time corrective actions. With the their decision can be depicted in a visible and direct sensing approach. however. results are obtained transparent manner. After welding, the data can be by monitoring the joint at the point of welding thus. further analysed to identify and extract any errors in alleviating any time delays exhibited using the upstream operations. system performance. scheduling indirect approach. information and statistics about the weld operation. such as total arc-on time, cycle time, deposition rate Although sensor hardware is still in a period of per hour, and maintenance time. development and flux the following is an outline of current technologies available. In response to the
94
needs of the welding industry early developments concentrated on tactile sensing devices making use of the seam configuration as a mechanical guide. Although their low cost has attracted many users their implementation was limited due to their incompatibility to interrogate complex weld centreline geometries and to keep constant contact with rough workpiece surfaces.
4.
Welding quality requirements are demanding and increasing levels of quality are achieved only if both product and process are inherently capable of attaining them. Post-welding non-destructive evaluation of components is usually carried out to assess product integrity. The interpretation of data, however, requires intensive use of human expertise and this labour intensive work is the principal reason for high inspection costs. Even the most sophisticated and automated test systems are hampered by human ability to interpret test results. On-line quality evaluation in welding may lead to errors and reduction in reliability, while during offline monitoring the vast amount of data generated by the test require sometimes several man hours to extract relevant information and to make interpretation possible. Expert systems can adequately cope with this dilemma, either by rapidly sentencing repetitive but simple date on-line, or by filtering the large quantities of data produced off-line. A sophisiticated non-destructive testing application in welding manufacturing is the one of ultrasonic sensing for welding process monitoring and adaptive control. The technique makes use of the ultrasonic time of flight principles in which high frequency electronic pulses are transformed into mechanical vibrations through piezoelectric crystals and in turn into elastic acoustic waves (8). Returned signals having amplitude and analogues to size, shape and orientation of reflectors, and time of flight separation proportional to distance between transducer and reflector are collected and analysed in real-time. Both these parameters are digitised of all three consecutive
The remote sensing method encompasses a plethora of techniques such as vision, electromagnetic, thermal and others utilised to control joint tracking and well fill adaptively (6). Within this group one successful contribution has been the development of the through·the-arc sensing. Due to its ease of use, the relatively low cost and the ability to compensate for the heat distortion this technology has been established into a wide variety of welding applications. It makes use of an oscillating torch to track across the weld seam with the electrode tip acting as a transducer. Weld filling is accomplished after both sidewalls have been interrogated and the torch is positioned over the weld centreline. Sensors inabilities, however, to follow complex seam contours and to weld non-ferrous materials are the only major drawbacks accompanying this technology. The most advanced remote sensing technology that has emerged is machine vision and is capable of generating three-dimensional profiles of weld joints. Projected planes of laser light produce illuminated stripes on the weld joints which are viewed by a solid state camera. Digitised images portray such essential ingredients for adaptive control as seam centreline, surface convexity, wetting points and undercut. Latest developments in laser/vision sensors include adaptive control using expert systems to optimise the welding process with respect to weld quality levels by correlating between the manufacturing conditions and the severity of the technical specifications (7). I.P
A/IAl..OQUE INPUT SICIIAl. FROII ULTRASOHASCOPE
ROBOTIC WELDING PROCESS MANAGEMENT WITH EXPERT SYSTEMS AND ULTRASONIC SENSORS
p
SIQlAL COHDITIONEltS
SIQW.
Fig. 2 Schemata of welding process sensing systems 95
OEF"Ecr AHPtIruDE REF"ERElICE
echoes that appear on a single trace. This phenomenon of variable echoes is caused due to beam spread effects and the characteristic control fashion that acoustic waves propogate in media. Data from scanned weld pool sidewalls are simultaneously collected and deconvoluted during the process of welding. Newly formed weld pools are interrogated from both sides and data are utilised for accomplishing the adaptive control (Figure 2). The expert system implemented is prompting for process parameter settings if results are different from the expected and awaits for instructions to commence scanning following the weld centreline. During the interrogation procedure ultrasonic echoes from both sensors placed alongside the seam are compared, and contrasted with the predetermined set values in a continuous sampling fashion . The inference engine comprises of rules containing information about the welding process in concern such as input parameters and their inter-relation with weld pool size. If the corresponding echoes from each multiplexed transducer comply with the preset signals no action is taken. If they don't, then action is taken to restore process harmony by acting upon both the weld pool placement and shape adjusting the electrode tip position relative to weld centreline and/or the melting rate respectively so that modelled requirements are fully met (Figure 3). This in-process equilibrium maintenance with respect to desired matching of reference quality tolerances is a distinct characteristic of preventative quality designed into the product leaving any further post weld acceptance criteria relegated to supplementary roles. Some more attractive features of the intelligent ultrasonic sensing welding system include capabilities such as welders and inspectors training since it can display three dimensional images of weld pool silhouettes (9) .
5.
REFERENCES. I.
Boughton, 0 ., et ai, "Towards the automation of arc welding" , Central Electricity Generating Board, Research Report, Vol. 9, June, 1979. Cook, G.E., "Feedback and adaptive control in automated arc welding systems", Metal Construction, Vol. 13, No. 9, 1981. Lucas, W. and Brightmore, A., "Expert systems in welding", First Int. Conf. on Computer Technology in Welding, Paper 47, London, UK, June 1986. Yapp , D., et aI, "The potential of intelligent computer systems in welding", First Int. Conf. on Computer Technology in Welding, Paper 44 , London, UK, June 1986. The Welding Institute, "Annual research report - product literature", Abington, UK, 1986. Hanright, S. , "Robotic arc welding under adaptive control - A survey of current technology". Welding Journal, Vol. 65, No. 11 , November 1986. Servo-Robot, "Advancing the vision of intelligent adaptive welding" , Product Literature, Quebec, Canada, 1989. Siores, E., et aI, "Adaptive control in arc welding utilising ultrasonic sensors", Second Int. Conf. on Developments in Automated and Robotic Welding. Paper 8, London, UK, November 1987. Siores, E. , "An autonomous welding and inspection system", Thirty Seventh Int. Conf. on Welding Technology for Profit, Paper 6, Sydney, Australia, November 1989.
2. 3.
4.
5. 6.
7. 8.
9.
l
DYNAMIC) DATA
.
I I
CONCLUSIONS
The simultaneous management of several goals is one of the major challenges in such a system. The goals of maximised productivity. quality, cost and management policy can often conflict when variations occur in the workpieces to be welded. During welding, these goals are traded-off in light of the facts of the particular circumstances, and decisions regarding changes in welding parameters are made in real-time.
MODaL",) DATA
I II
WELDING ?RCXESS
Il
,
( "OIN T I ifCSITICN,
_ _ _ _ _Y _e_s
'I
s,z~ )
(> I
• E.X?~~T
Intelligent welding management systems comprising expert systems and sophisticated sensory devices will increasingly continue to provide solutions to the welding manufacturing industry. A great deal of research and development has been devoted on implementing such advanced technology and undoubtedly the hesitant enhancements in both software and hardware taken today will lay the foundations for the welding manufacturing systems of the future .
'1,'8.!l
~
SYSiE\1
( I Nr~:;ENC"i (KN:)M.EGE! =''-G:NE SAS" I
!L
l
(
~ EAL "7"IM E CA -;-A
r ig. 3 K.1owledge based adaptive control flow chart
96