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Resistance based iterative learning control of additive manufacturing with wire
Mechatronics 31 (2015) 116–123
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Mechatronics journal homepage: www.elsevier.com/locate/mechatronics
Resistance based iterative learning control of additive manufacturing with wire Petter Hagqvist a,⇑, Almir Heralic´ b, Anna-Karin Christiansson a, Bengt Lennartson a,c a
University West, Department of Engineering Science, SE-46186 Trollhättan, Sweden GKN Aerospace Engine Systems, SE-46181 Trollhättan, Sweden c Chalmers University of Technology, Department of Signals and Systems, SE-41296 Göteborg, Sweden b
a r t i c l e
i n f o
Article history: Received 30 September 2014 Revised 12 March 2015 Accepted 22 March 2015 Available online 10 April 2015 Keywords: Additive manufacturing Metal deposition Automatic control Resistance Process measurement Iterative learning control
a b s t r a c t This paper presents successful feed forward control of additive manufacturing of fully dense metallic components. The study is a refinement of former control solutions of the process, providing more robust and industrially acceptable measurement techniques. The system uses a solid state laser that melts metal wire, which in turn is deposited and solidified to build the desired solid feature on a substrate. The process is inherently subjected to disturbances that might hinder consecutive layers to be deposited appropriately. The control action is a modified wire feed rate depending on the surface of the deposited former layer, in this case measured as a resistance. The resistance of the wire stick-out and the weld pool has shown to give an accurate measure of the process stability, and a solution is proposed on how to measure it. By controlling the wire feed rate based on the resistance measure, the next layer surface can be made more even. A second order iterative learning control algorithm is used for determining the wire feed rate, and the solution is implemented and validated in an industrial setting for building a single bead wall in titanium alloy. A comparison is made between a controlled and an uncontrolled situation when a relevant disturbance is introduced throughout all layers. The controller proves to successfully mitigate these disturbances and maintain stable deposition while the uncontrolled deposition fails. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Additive manufacturing (AM) is advantageous in both replacing and complementing conventional material processing as well as opening up for entirely new ways of constructing and fabricating components [1–3]. Within metal additive manufacturing, a number of technologies exist, most of which are based on using metal powder as feedstock material, either in a powder-bed or blown powder configuration [2]. Metal wire has also been used as feedstock material for deposition, using either an electrical arc [4,5], an electron beam [6], or a laser [5,3] as energy source. Many solutions for monitoring and controlling these processes have been developed for powder based processes [2,7–10]. However, for wire based metal AM processes, which are beneficial in terms of high material deposition rates, less effort has been spent in controlling such systems [3,11]. In this study, the control system of a metal AM process, Laser Metal Deposition with wire (LMD-w), which utilizes a solid state laser together with wire feedstock is progressed. This is achieved ⇑ Corresponding author. E-mail address: [email protected] (P. Hagqvist). http://dx.doi.org/10.1016/j.mechatronics.2015.03.008 0957-4158/Ó 2015 Elsevier Ltd. All rights reserved.
through replacing a relatively advanced laser scanning system described in [12], ill-suited in industrial production, with a simpler system based on resistance measurements. 1.1. The LMD-w process The overall principle of the LMD-w process is illustrated in Fig. 1. A solid state laser, not shown in the picture, creates a pool of molten metal, into which the wire is fed. The high temperature of the weld pool and the laser radiation heats up the wire and causes it to melt. The wire feeder is together with the laser optics attached to a processing tool which is moved along a deposition path by an industrial robot. This causes the molten wire to solidify along this deposition path, forming beads. These beads are placed side-by-side and layer-upon-layer, forming 3D structures upon the substrate using suitable robot movements. Although similar to powder based deposition techniques, for which several process models and control solutions have been proposed [2,7–10,13,14], the LMD-w process may not directly benefit from these concepts developed for blown powder deposition due to the processes’ differences. For example, when increasing the material feed rate for the powder processes, excess powder will
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Fig. 1. Illustration of the LMD-w process. The laser beam (not shown) establishes the weld pool into which the wire is fed. Current is led from the nozzle through the wire into the weld pool and the substrate. Resistance, Rx is measured over the nozzle and the substrate.
remain unmolten but not significantly disturb the process. On the other hand, if too much wire is fed into the LMD-w process, the wire will eventually leave the weld pool to form a coil, disrupting the process. The LMD-w process is highly sensitive to disturbances and exhibits non-linear behaviour in terms of deposit geometries and input parameters [3]. Positioning of the tool relative to the substrate and the wire feed rate is essential in maintaining a stable deposition process. If the distance between the tool and the workpiece, denoted d, is correct, as shown in the leftmost part of Fig. 2, there is a continuous transition of material from the solid wire into the weld pool. If d is too small, the wire will protrude through the weld pool into the solid substrate. This phenomenon, called stubbing [3], causes the wire to rapidly oscillate from side to side, scraping onto the unmolten part of the substrate causing lack-of-fusion defects in the deposited material. This deposition mode is indicated in the middle section of Fig. 2. If instead d is too large, a throat will form in the material transfer as indicated in the rightmost part of Fig. 2. This throat may become disrupted due to some further increase of d, in which case the wire will be molten independently of the substrate. A droplet will build up at the wire tip, before it eventually grows too large to be sustained by surface tension and falls down onto the substrate. This droplet transfer will give an uneven surface, poorly suited for further deposition. Disturbances in d may be caused by process variations due to deposit geometry, temperature buildup or poor planning of deposition paths [3]. Based on the above, it is concluded that control of d is essential for creating high quality deposits. Control of d can be actuated in two ways. Either the tool position is adjusted through the robot position, or the wire feed rate, v wire , is adjusted to form a higher or lower deposit, effectively altering d. One way of measuring d is to measure the resistance over the process as indicated by the placement of Rx in Fig. 1. In this application Rx is the combined resistance over the nozzle, wire stick-out,
weld pool, deposit, fixture and the wires used. Out of these, only the combined resistance of the wire stick out and the weld pool is assumed to vary. An increase in Rx will indicate an increase in d, since the resistance will increase as the weld pool geometry changes into a throat as shown in Fig. 2 or if the wire stick-out increases. The resistance of the deposit itself will also increase with the number of deposited layers, but this effect is negligible for the relatively large, low resistance, deposits created by LMD-w.
1.2. Previous work In a previous study [15], the application of resistance measurements to control of the LMD-w process was investigated. A proof of concept was presented. Although promising results were obtained in [15] using on-line control of robot height position, the concept was only evaluated for a single deposited bead, not for a layerupon-layer geometry. For a single bead, the initial conditions are well defined and known beforehand. When depositing a multilayer structure, the outcome of the deposition of one layer serves as the setting for depositing the next, giving rise to accumulation and amplification of disturbances. In an industrial context, it is required that the control system is robust and is able to reject many kinds of disturbances also for multilayer deposits. One problem which was identified was that the high frequency disturbance contents of the acquired resistance signal may cause either unwanted high-frequency actuation, or require a low-pass filter with such low bandwidth that desired actuation is performed too slowly for feedback control. Due to these limitations for closed loop feedback control, based on resistance, the PI-controller previously employed will not suffice when the process is to be implemented in industry [15]. By instead basing control action during deposition on the height profile of previous layers, such as in the work by Heralic´ et al. [12],
Fig. 2. Material transfer modes, dependent on the tool-to-workpiece distance d.
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measurement inputs and actuation outputs can be filtered and thus limited in bandwidth. In this study, the focus is shifted from the measurement principle itself, which was presented in [15], to the integration of resistance based measurements into an industrially viable control system. Control action is based on the measurement signal produced while depositing earlier layers, exploiting the benefits of feed forward control for the LMD-w process, further described in Section 2.4. 1.3. Motivation
2.1. Resistance to distance model Similarly to the model previously devised by the authors [15], a static regression model is used for translating resistance into distance
^ dðkÞ ¼ uT ðkÞh
ð1Þ
^ denotes the estimated diswhere k is the discrete time instant, d tance d, see Fig. 2. The constant h is a column vector of regression coefficients, and the row vector T
0:5 2 u ¼ ½1; R0:5 ðkÞ; v 0:5 wire ðkÞ; Rx ðkÞ; Rx ðkÞ : x ðkÞ; Rx
This paper presents a refinement of the previous resistance measurement system, along with its implementation into a feed forward Iterative Learning Control (ILC) system. The benefits of this is illustrated by depositing not only a single bead as in [15], but a multi layer wall through control of the wire feed rate v wire and the robot z-position. The ILC control system has previously been successfully utilized for control of LMD-w based on height input provided by a laser scanning system [12]. The laser scanner is replaced by a resistance measurement system in this study, a replacement which is beneficial in several ways. First, no additional scanning time is required during processing. All required measurements are made during deposition while using resistance measurements instead of the laser scanner. All measurement data can be directly related to robot path coordinates when measuring resistance rather than requiring extraction from the point-cloud generated by the laser scanner [3]. Also, no scanner hardware has to be fitted to the processing tool. This saves both money and precious space when desiring to use small and nimble processing tools in order to facilitate access. Resistance measurement hardware can easily be fitted outside of the processing tool at a relatively low cost. It may also be added to existing LMD-w tools without any modifications to the tool itself. Another benefit of using resistance measurements instead of laser scanning is that resistance based measurement system is simpler from a calibration and maintenance point of view, and thus more suitable for production environment than one based on laser scanning. 2. Materials and methods
All constituents of u were found to be significant for the model with a confidence level of 95%. This model enables estimations of d based on the measurement of electrical resistance, Rx discussed in Section 2.2 and indicated in Fig. 1. The root mean square errors of the obtained model is less than 0:15 mm, which is in accordance to the value of 0:16 mm obtained for the model created in [15]. The scanner, which the proposed method is supposed to replace, is mounted to the robot which has a positional accuracy that limits the scanners resolution to 0:10 mm [3]. 2.2. Resistance measurement system In the previous study [15], the resistance was measured through readings of voltage over the process and measuring the current which was supplied by a weld source. This current acted as a ‘‘hot-wire’’ energy source for the process. If such an energy addition is unwanted, it is required that the current through the weld is significantly reduced. This is made possible by the improved measurement circuit presented below. By using a wheatstone bridge for measuring the unknown resistance Rx , as shown in Fig. 3, accurate measurements of Rx can be made for an unknown current passing through the circuit. The bridge is supplied with a DC source voltage, V 1 , with limited current output. In Fig. 3, Rref is chosen to be close to Rx , and R is selected to be in the same order of magnitude, in this case less than 1 X. The voltages V 1 and V 2 , as introduced in Fig. 3, can be measured and their ratio
K ¼ V 2 =V 1
In this section, an overview of the materials and methods utilized are presented.
ð2Þ
ð3Þ
can be calculated. Using the notation for resistances as indicated in Fig. 3, Rx can be calculated based on K and the known resistances
Fig. 3. Wheatstone bridge for measuring Rx .
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Rx ¼
RðRref þ KRref þ KRÞ : R KðRref þ RÞ
ð4Þ
The use of the wheatstone bridge allows reduction of current through the weld pool while still maintaining accuracy. Also, only one physical quantity, namely voltage, has to be measured instead of measuring both current and voltage. This means that the measurement hardware can be simplified from two digital acquisition modules to one. 2.3. Filtering of resistance signal The two sampled voltage signals V 1 ðkÞ and V 2 ðkÞ contain noise and are because of this filtered before their quotient KðkÞ, as introduced in (3) above, is formed. This is in order to avoid noise amplification through division. For this purpose, a discrete median downsampling filter with a window size of 0:4 s is used due to its abilities to suppress and reject noise, but not remove all high frequency components in the signal such as a linear low-pass filter would [16]. After filtering of V 1 ðkÞ and V 2 ðkÞ; KðkÞ and subsequently Rx ðkÞ are calculated according to (3) and (4) respectively. The signal Rx ðkÞ is then fed to the control system for translation ^ in (1). into d 2.4. Control system The main purpose for controlling the LMD-w process is to maintain a stable process in order to avoid the problems outlined in Section 1.1. The most successful control system so far was based on feed forward control of deposit height [12]. The height profile of the deposit served as input for control of the subsequent layer. Since no height profile information could be gathered during deposition but was collected after deposit of each layer, feed forward control was a natural choice. Even though it might seem tempting to try to devise a closed loop feedback control system based on resistance measurements, this approach has several drawbacks. Due to the process dynamics [12], control actuation should ideally be initiated before a height disturbance is reached and detected. This is not possible for feedback control. Also, since data from the previous layer would indicate the presence of such a disturbance in the current layer, it is wise to utilize this knowledge. Yet another benefit of feed forward control in this case, is the ability to use non-causal, i.e. zero-phase, filtering [17], of the height profile data along the deposition path. This avoids any lag in the control signal which would be detrimental to the stability of the process. Since the bandwidth of the actuation is much lower than that of the measurement signal, low-pass filtering of the control signal is required. The comparably low cut-off frequency of the low-pass filter would give a significant time lag in any feedback control system with causal filters. Hence, the use of feed forward control even when height measurements are available in situ, brings many benefits compared to a feedback, closed-loop control system. However, on-line measurements based on resistance can be used as a monitoring watchdog for setting an alarm if serious problems, such as droplet formation is detected. This is true even if a feed forward system is not implemented. Also, the resistance measure could prove to be valuable information to convey to an operator running the process manually. Provided a control system which successfully controls deposition of a part, the output of said system could be used as a recipe for producing identical parts on a larger scale. This requires that all disturbances can be assumed to be consistent for every new part. However, LMD-w is mainly expected to be used for low volume, high value products. With the low cost of the resistance based
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system and short production series, the benefit of using recipes instead of automatic control is limited. 2.5. Actuators As previously mentioned in Section 1.1, either robot position or wire feed rate can be adjusted in order to maintain a nominal value of d. In this paper, in contrast to the previous resistance based study [15], wire feed rate, v wire , is adjusted in order to perform control action during deposition. While the robot is limited to adjustments of at least 0:1 mm, the wire feeder’s feed rate does not exhibit any such practically limiting discretization. Also actuation of changes in v wire is faster and more exact than robot position changes. Because of this, the tool z-position is only adjusted, using the robot, before deposition of a new layer in order to ensure a suitable nominal distance between the deposition tool and the workpiece, and not during deposition. 2.6. Iterative learning controller During deposition any height variations within a layer are generated either due to disturbances (e.g. unpredicted variation in wire feed rate) or as a consequence of the chosen process parameters (e.g. dimensional variations of the molten pool due to temperature build-up). Experience has shown that the latter is the dominating factor and hence, any observed variations in layer topography are mainly deterministic. However, the dependency is complex and hard to predict. For each new setting of process parameters the a priori knowledge of the resulting system’s dynamics is as such limited. There is because of this a need for a controller that adapts to local process variations on-line. Since the deposition process is repeated, layer after layer, it can be seen as an iterative process with deposition layers as the iteration domain. Implementing an Iterative Learning Controller (ILC) seems therefore as a natural choice. Usually, invariant starting conditions are demanded for application of ILC, however as shown in [3,12], ILC proves to be a suitable control scheme also for LMD-w anyway, due to the strong repetitive behaviour of the process. The concept of learning repetitive tasks through iteration was mathematically formalized three decades ago, see e.g. the seminal works in [18,19] or a more recent survey paper [20]. Today, the ILC approach is mainly implemented for improving reference following in repetitive tasks which involve industrial robots. An important part of an ILC algorithm is its performance measure. Here, the goal is to maintain the distance d as close to dnom as possible, where dnom is defined as the desired distance between the nozzle and the last deposited layer, see Fig. 2. The way of achieving this is by both controlling the wire feed rate (e.g. to fill the cavities) and the robot offset (to compensate for wrong estimates of the layer height) as previously shown by Heralic´ et al. [3,12]. Included in these studies, Heralic´ investigates and proves that the ILC provides stability and height convergence to the LMD-w process. Defining the control structure in order to provide low deviations from a mean build height, the ILC is limited to only smooth out height variations, i.e. to control the wire feed rate in order to achieve a flat surface. This will minimize the control action and thereby also the process induced disturbances. Assume that the ILC algorithm operates on a set of N discrete points k (sampled with the sampling time T s ), which are uniformly distributed along the deposition path (see Fig. 4). Define the mean j as layer height h N X ¼ dnom 1 ^ ðkÞ þ h ^z h d j j N k¼1
ð5Þ
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Fig. 4. Illustration of time index k and layer (iteration) index j. Note that k is reset for each new layer.
Fig. 5. ILC architecture as implemented in this work. The delays are in the iteration domain and
^ ðkÞ is the estimated distance (obtained through resistance where d j measurements) between the nozzle and the underlying layer in a single point, i.e. at time index k, after layer j has been deposited, ^ z is the estimated layer height. and h The step-height compensator is then defined in the following , is way: After each deposited layer, the mean layer height, h j obtained and the robot’s tool position is offset in the z-direction , according to: by h j z T jþ1 ¼ T jz þ h j
ð6Þ
z j
where T is the robot tool’s z-position at layer j. Further, define the performance measure, at each discrete point k, as the error
^j ðkÞ ej ðkÞ ¼ d
N 1X ^j ðkÞ d N k¼1
ð7Þ
The error ej ðkÞ thus measures the deposited layer’s height variations from a mean value, at each discrete point k, which should be minimized. Using (7) the ILC algorithm can now be defined. Here, a second order ILC is chosen such that it incorporates the error measurements from the previous two iterations, i.e. ej ðkÞ and ej1 ðkÞ.
ujþ1 ðkÞ ¼ Q ðkÞ½uj ðkÞ þ qlk ðc1 ej ðkÞ þ c2 ej1 ðkÞÞ
ð8Þ
where uj is the control input that controls the wire feed rate on layer j; ci are the learning gains. The Q-filter, Q ðkÞ, is a zero phase low pass filter in k-domain, i.e. not in the layer-iteration domain j [21]. The Q-filter is based on a second order Butterworth filter.
v wire
is the nominal wire feed rate.
Furthermore, a time shift operator, qlk (q1 k uj ðkÞ ¼ uj ðk 1Þ) acts on the two error signals, ej ðkÞ and ej1 ðkÞ, where l denotes the number of samples the corresponding signal is shifted in time. For each new iteration, the time is reset as illustrated in Fig. 4. Fig. 5 shows the ILC architecture as implemented in this work. The higher order ILC is needed since each new layer is deposited on top of its preceding layer, and not on a new flat substrate. Hence, the error is inherited, and thus accumulated, from previous iterations. Due to this feature, as previously shown by Heralic´ in previous studies [3,12], the ILC controlled deposition system becomes unstable if a first order ILC is used. For clarity, the deposition process’ different steps are here summarized: 1. Time index k is reset when a new layer is to be built. 2. Layer j is deposited using the control action uj . 3. Distance d is estimated through resistance measurements using (1). is calculated using (5). 4. Mean layer height h j 5. Error ej is obtained using (7). 6. New control action ujþ1 is calculated using (8). z 7. Robot’s new height position T jþ1 is updated using (6). 8. Index j is incremented and the process is repeated. 2.7. Implementation 2.7.1. Hardware A six axis robot manipulator, ABB IRB 4400, is used for holding the deposition tool. The tool integrates a push–pull Fronius
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2.7.2. Software The core of the measurement and control system is implemented in National Instruments’ LabVIEW software. Data acquisition, communication with the robot and wire feeder as well as resistance signal filtering is performed using this software. Time synchronized process data, including calculated resistance values are saved to a MySQL database. From this database, a programme written in MATLAB extracts measurement data after each layer and calculates the control signal using the ILC algorithm. 2.8. Experimental procedure In this section, the experiment specific details are presented. 2.8.1. Control parameters and control signal filtering During deposition, the voltage quotient K, tool position and all process parameters were sampled at 25 Hz. The control parameters used essentially reflects those parameters previously used when employing a laser scanner as input to the ILC system [12]. The only difference is that median filtering with a window size of 50 samples, followed by low-pass, zero-phase, Butterworth low-pass filtering with a suitable cutoff frequency was applied to the control signal. This filtering is to ensure that all control signals match the actuator’s bandwidth. The use of a non-linear downsampling filter has proved to give good results, but comes with the drawback that the two filters may not be combined due to the non-linearity of the median filter. The other parameters used were
identical to the ones previously shown to give stability for the process [12]: static gain of the plant, K h ¼ 4:95 104 , the system’s time constant T h ¼ 0:37 s, learning gain parameters, c1 ¼ 1=K h and c2 ¼ 0:75c1 . The controller tuning considerations in [12] are summarised in the next paragraph. 2.8.2. Controller tuning considerations There are four controller parameters to consider in (8), the learning gains (c1 ; c2 ), the cut-off frequency of the Q-filter and the time shift qlk . Another important consideration is on the rate of change of the wire feed rate signal, which needs to be limited to mitigate the risk of droplets being formed. In [12] the Q-filter was initially set to Q ¼ 1 and l ¼ 0, i.e. qlk ¼ 1. Then simulations with the static gain K h (here K h 4:95 104 ) were performed and a suitable (c1 ; c2 )-pair was chosen to give a marginally stable system with c1 ¼ 1=K h and c2 ¼ 3=4c1 as a compromise between a reasonable response time and moderate control activity. The cutoff frequency of the Q-filter is set to 1 Hz as a trade-off between robustness and response time. The time-shift qlk compensates for the time delay of the plant, here T h ¼ 0:37 s. 2.8.3. Provoked disturbance A disturbance in deposition is caused by gradually decreasing the wire feed rate v wire from nominal (0%) to 25% and then back to nominal again as seen in Fig. 7. This disturbance is applied to all 10 5
Wire speed rate change [%]
TransSynergic 4000 wire feeder/weld source with the laser optics. An IPG, 6 kW fibre laser is used as laser power source. The laser light is conveyed to the optics through a 0:6 mm diameter optical fibre to eventually form a 3 mm diameter out of focus laser spot on the workpiece. All processing of material (Ti6Al4V), takes place in inert argon atmosphere contained by a processing chamber, in which the tool operates. The voltage source, limited to 1 A and 5 V, used for supplying the wheatstone bridge is developed in-house. The only necessary modification to the tool is shown in Fig. 6. A steel hose clamp is used for attaching a copper wire to the nozzle. Another copper wire is attached to the fixture, serving as the second contact point for Rx . The two copper wires are attached to the wheatstone bridge, illustrated in Fig. 3. The resistors with known resistances needed for the other positions (R and Rref ) were created from the same wire which was deposited. This results not only in resistors with resistance comparable to Rx but also resistors capable of conducting high currents. A National Instruments cDAQ-9172, rack with an NI 9215 voltage measurement module is used for measuring the voltages V 1 and V 2 .
0 −5 −10 −15 −20 −25 −30
0
20
40
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Deposit length [mm] Fig. 7. Wire feed rate in percent as a function of deposition distance provoking a disturbance.
Fig. 6. Fastening of copper wire to nozzle using a hose clamp.
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deposited layers, with the goal of the control system to counter these disturbances and stabilize the process. 2.8.4. Deposition ^ z , was During uncontrolled deposition, a nominal build height, h assumed and the tool z-position was increased, using the robot manipulator, according to this at the beginning of each layer. ^ z is With the controller activated, the nominal build height h ^ in order to ensure a beneficial tool z-position adjusted based on d as described in Section 2.6. 3. Results and discussion For comparison, two walls were deposited. One was deposited with and the other without the control system activated. Without the control system, seven beads could successfully be placed on top of each other before droplets formed during deposition of the eighth layer. For the controlled wall, eleven beads were deposited without any problems. This can be seen as a quantitative measure of the deposition quality, since any control system for LMD-w should be judged on its merits regarding maintaining stable deposition and preventing failure. When depositing metal wire, the start and stop sequences are critical and require dedicated control, see e.g. [11] or [22]. Due to the focus of the paper
being control of the nominal process and not start or stop sequences, 15 mm at the beginning and at the end of the 200 mm deposit were not controlled. The buildup of material at the start and the lack of material towards the end of the wall, prevented deposition of any more layers, because these regions were not controlled. Future work will include an investigation into the suitability of using ILC also for the start and stop sequences. Such adoption will however most probably require substantial adaptation and tuning of the iterative learning controller or setting up separate controllers for the start and stop sequences. In Fig. 8, the resulting height profiles of the controlled and uncontrolled deposits, as measured by an ATOS Core 80 3D measurement system are presented. The deposits are shown in Fig. 9. Note that eleven layers were successfully deposited for the controlled case, whereas the uncontrolled case failed at the eighth layer. Hence the generally higher profile for the controlled case. The droplets formed during the last layer for the uncontrolled deposit can be seen as undulations between roughly 90 mm and 120 mm in Fig. 8 and as a wavy portion on the uncontrolled bead in Fig. 9. As shown in Fig. 10, the control signal clearly reflects the introduced disturbance. The ideal response to counter the disturbance is indicated with dashed lines in the figure. A control signal based on feedback control with the same bandwidth would, due to the lack of non-causal zero-phase filtering, be shifted to the right and
7.5
7
Deposit height [mm]
6.5
6
5.5
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Controlled Uncontrolled
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Deposit length [mm] Fig. 8. Height profiles of controlled and uncontrolled tests as measured by a GOM ATOS Core 80 3D measurement system. The test pieces used for producing these profiles can be seen in Fig. 9.
Fig. 9. Uncontrolled wall in foreground, controlled wall in background. The surface profiles of these test walls can be seen in Fig. 8.
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bridge implemented enabled enhanced geometric control. This improvement proved enough for extending the use of resistance based control to multi-layer structures. The contributions of this work amounts to a significant progression of LMD-w measurement and control. An expensive laser scanning solution, poorly suited for industrial adoption, is successfully replaced with a simpler and cheaper system based on resistance measurements. The proposed solution basically requires no changes to the deposition equipment or the deposition sequence. The concept of ILC, previously proven to be successful for controlling the process [12], is adopted for use with the input data provided by the measurement system. Application of the developed measurement and control solution is demonstrated and found to exhibit desired properties.
Layer 3 50 0 −50
Wire speed rate change [%]
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Layer 5 50 0 −50
Layer 7 50 0 −50
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Acknowledgements
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Deposit length [mm] Fig. 10. Solid line, control signal depending on layer number. Dashed line, ideal control signal, i.e. negative disturbance.
actuated too late. This would give a mismatch in time between the disturbance and the compensation resulting in an unstable process. The plate onto which the deposit was added deformed during processing. Due to the fact that the plate was firmly clamped at the short-side of the plate, close to the start of the deposited bead, deformation close to the start was less than for the rest of the plate. This means that as the deposit generally ‘‘sunk’’ down due to deformation, the effect was less significant near the start. This led to a comparably higher measured height in the first part of the deposit, before the introduced deformation. The control action for this portion subsequently becomes to lower the wire feed rate in order to compensate for this comparably higher portion as seen in Fig. 10. The 3D profile measurement presented in Fig. 8, is compensated for the deformation. It too, reflects the aforementioned in that the part of the controlled bead which is near the start, and did not deform as much, has lower effective build height due to the high height measurement and subsequently lowered v wire . 4. Conclusion In this paper, the use of resistance measurements, instead of laser scanner information as used in previously devised systems [3,12], serves well as input to a second order ILC system, which has proven stability for the application [3,12]. Note that no tuning of controller parameters was performed after replacing the laser scanner with the resistance measurement system. It is found that the resistance based measurements result in accuracy similar to that of the laser scanner which is replaced by the resistance measurements. The resistance based measurements are of comparable accuracy to the scanner and it is within this study proven successful to replace the scanner data with resistance based data. Based on this, and taking the benefits of a resistance based solution, such as cost and in situ measurements, into account. It is recommended that it is used instead of scanner based solutions for LMD-w control. Future efforts should include such tuning, based on studies of capability for the resistance measurement system with regard to e.g. tool orientation and effective build height. Compared to an earlier study where resistance measurements for control of LMD-w was first introduced [15], the ILC system and the wheatstone
The authors wish to acknowledge the contributions from Kjell Hurtig, Hans Dahlin and Fredrik Sikström at University West. The research leading to these results has received funding from the European Community’s Seventh framework Programme FP7 2007-2013 under grant agreement 266271 – Merlin Project. References [1] Wohler T. Additive manufacturing 101: Part IV; 2010. [2] Toyserkani E, Khajepour A, Corbin S. Laser cladding. CRC Press; 2005. [3] Heralic´ A. Monitoring and control of robotized laser metal-wire deposition [doctoral thesis]. Chalmers University of Technology; 2012. [4] Almeida PS, Williams S. Innovative process model of Ti–6Al–4V additive layer manufacturing using cold metal transfer (CMT). In: Proceedings of the twentyfirst annual international solid freeform fabrication symposium. Austin, TX, USA; 2010. [5] Brandl E, Baufeld B, Leyens C, Gault R. Additive manufactured Ti–6Al–4V using welding wire: comparison of laser and arc beam deposition and evaluation with respect to aerospace material specifications. Phys Procedia 2010;5(Part 2):595–606. [6] Stecker S. inventor; Sciaky Inc, assignee. Electron Beam Layer Manufacturing. US8546717 (B2); 2013. CIB: B23K15/00. [7] Toyserkani E, Khajepour A. A mechatronics approach to laser powder deposition process. Mechatronics 2006;16(10):631–41. [8] Mazumder J, Dutta D, Kikuchi N, Ghosh A. Closed loop direct metal deposition: art to part. Optics Lasers Eng 2000;34(4):397–414. [9] Bi G, Schürmann B, Gasser A, Wissenbach K, Poprawe R. Development and qualification of a novel laser-cladding head with integrated sensors. Int J Mach Tools Manuf 2007;47(3–4):555–61. [10] Song L, Mazumder J. Feedback control of melt pool temperature during laser cladding process. IEEE Trans Control Syst Technol 2011;19(6):1349–56. [11] Zhang YM, Li PJ, Chen YW, Male AT. Automated system for welding-based rapid prototyping. Mechatronics 2002;12(1):37–53. [12] Heralic´ A, Christiansson AK, Lennartson B. Height control of laser metal-wire deposition based on iterative learning control and 3D scanning. Opt Lasers Eng 2012;50(9):1230–41. [13] Sammons PM, Bristow DA, Landers RG. Height dependent laser metal deposition process modeling. J Manuf Sci Eng 2013;135(5):054501. [14] Sammons PM, Bristow DA, Landers RG. Control-oriented modeling of Laser Metal Deposition as a repetitive process. In: American Control Conference (ACC), 2014; 2014. p. 1817–20. [15] Hagqvist P, Heralic´ A, Christiansson AK, Lennartson B. Resistance measurements for control of laser metal wire deposition. Opt Lasers Eng 2014;54:62–7. [16] Runkler TA. Data analytics – models and algorithms for intelligent data analysis. Vieweg + Teubner Verlag; 2012. [17] Smith SW. Digital signal processing: a practical guide for engineers and scientists. Elsevier; 2003. [18] Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning. J Rob Syst 1984;1(2):123–40. [19] Craig JJ. Adaptive control of manipulators through repeated trials. In: Proceedings of the american control conference, vol. 3. 1984. p. 1566–73. [20] Bristow DA, Tharayil M, Alleyne AG. A survey of iterative learning control. IEEE Control Syst 2006;26(3):96–114. [21] Mitra SK, Kuo Y. Digital signal processing: a computer-based approach, vol. 2. New York: McGraw-Hill; 2006. [22] Heralic´ A, Christiansson AK, Ottosson M, Lennartson B. Increased stability in laser metal wire deposition through feedback from optical measurements. Opt Lasers Eng 2009;48(4):478–85.
Contents lists available at ScienceDirect
Mechatronics journal homepage: www.elsevier.com/locate/mechatronics
Resistance based iterative learning control of additive manufacturing with wire Petter Hagqvist a,⇑, Almir Heralic´ b, Anna-Karin Christiansson a, Bengt Lennartson a,c a
University West, Department of Engineering Science, SE-46186 Trollhättan, Sweden GKN Aerospace Engine Systems, SE-46181 Trollhättan, Sweden c Chalmers University of Technology, Department of Signals and Systems, SE-41296 Göteborg, Sweden b
a r t i c l e
i n f o
Article history: Received 30 September 2014 Revised 12 March 2015 Accepted 22 March 2015 Available online 10 April 2015 Keywords: Additive manufacturing Metal deposition Automatic control Resistance Process measurement Iterative learning control
a b s t r a c t This paper presents successful feed forward control of additive manufacturing of fully dense metallic components. The study is a refinement of former control solutions of the process, providing more robust and industrially acceptable measurement techniques. The system uses a solid state laser that melts metal wire, which in turn is deposited and solidified to build the desired solid feature on a substrate. The process is inherently subjected to disturbances that might hinder consecutive layers to be deposited appropriately. The control action is a modified wire feed rate depending on the surface of the deposited former layer, in this case measured as a resistance. The resistance of the wire stick-out and the weld pool has shown to give an accurate measure of the process stability, and a solution is proposed on how to measure it. By controlling the wire feed rate based on the resistance measure, the next layer surface can be made more even. A second order iterative learning control algorithm is used for determining the wire feed rate, and the solution is implemented and validated in an industrial setting for building a single bead wall in titanium alloy. A comparison is made between a controlled and an uncontrolled situation when a relevant disturbance is introduced throughout all layers. The controller proves to successfully mitigate these disturbances and maintain stable deposition while the uncontrolled deposition fails. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Additive manufacturing (AM) is advantageous in both replacing and complementing conventional material processing as well as opening up for entirely new ways of constructing and fabricating components [1–3]. Within metal additive manufacturing, a number of technologies exist, most of which are based on using metal powder as feedstock material, either in a powder-bed or blown powder configuration [2]. Metal wire has also been used as feedstock material for deposition, using either an electrical arc [4,5], an electron beam [6], or a laser [5,3] as energy source. Many solutions for monitoring and controlling these processes have been developed for powder based processes [2,7–10]. However, for wire based metal AM processes, which are beneficial in terms of high material deposition rates, less effort has been spent in controlling such systems [3,11]. In this study, the control system of a metal AM process, Laser Metal Deposition with wire (LMD-w), which utilizes a solid state laser together with wire feedstock is progressed. This is achieved ⇑ Corresponding author. E-mail address: [email protected] (P. Hagqvist). http://dx.doi.org/10.1016/j.mechatronics.2015.03.008 0957-4158/Ó 2015 Elsevier Ltd. All rights reserved.
through replacing a relatively advanced laser scanning system described in [12], ill-suited in industrial production, with a simpler system based on resistance measurements. 1.1. The LMD-w process The overall principle of the LMD-w process is illustrated in Fig. 1. A solid state laser, not shown in the picture, creates a pool of molten metal, into which the wire is fed. The high temperature of the weld pool and the laser radiation heats up the wire and causes it to melt. The wire feeder is together with the laser optics attached to a processing tool which is moved along a deposition path by an industrial robot. This causes the molten wire to solidify along this deposition path, forming beads. These beads are placed side-by-side and layer-upon-layer, forming 3D structures upon the substrate using suitable robot movements. Although similar to powder based deposition techniques, for which several process models and control solutions have been proposed [2,7–10,13,14], the LMD-w process may not directly benefit from these concepts developed for blown powder deposition due to the processes’ differences. For example, when increasing the material feed rate for the powder processes, excess powder will
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Fig. 1. Illustration of the LMD-w process. The laser beam (not shown) establishes the weld pool into which the wire is fed. Current is led from the nozzle through the wire into the weld pool and the substrate. Resistance, Rx is measured over the nozzle and the substrate.
remain unmolten but not significantly disturb the process. On the other hand, if too much wire is fed into the LMD-w process, the wire will eventually leave the weld pool to form a coil, disrupting the process. The LMD-w process is highly sensitive to disturbances and exhibits non-linear behaviour in terms of deposit geometries and input parameters [3]. Positioning of the tool relative to the substrate and the wire feed rate is essential in maintaining a stable deposition process. If the distance between the tool and the workpiece, denoted d, is correct, as shown in the leftmost part of Fig. 2, there is a continuous transition of material from the solid wire into the weld pool. If d is too small, the wire will protrude through the weld pool into the solid substrate. This phenomenon, called stubbing [3], causes the wire to rapidly oscillate from side to side, scraping onto the unmolten part of the substrate causing lack-of-fusion defects in the deposited material. This deposition mode is indicated in the middle section of Fig. 2. If instead d is too large, a throat will form in the material transfer as indicated in the rightmost part of Fig. 2. This throat may become disrupted due to some further increase of d, in which case the wire will be molten independently of the substrate. A droplet will build up at the wire tip, before it eventually grows too large to be sustained by surface tension and falls down onto the substrate. This droplet transfer will give an uneven surface, poorly suited for further deposition. Disturbances in d may be caused by process variations due to deposit geometry, temperature buildup or poor planning of deposition paths [3]. Based on the above, it is concluded that control of d is essential for creating high quality deposits. Control of d can be actuated in two ways. Either the tool position is adjusted through the robot position, or the wire feed rate, v wire , is adjusted to form a higher or lower deposit, effectively altering d. One way of measuring d is to measure the resistance over the process as indicated by the placement of Rx in Fig. 1. In this application Rx is the combined resistance over the nozzle, wire stick-out,
weld pool, deposit, fixture and the wires used. Out of these, only the combined resistance of the wire stick out and the weld pool is assumed to vary. An increase in Rx will indicate an increase in d, since the resistance will increase as the weld pool geometry changes into a throat as shown in Fig. 2 or if the wire stick-out increases. The resistance of the deposit itself will also increase with the number of deposited layers, but this effect is negligible for the relatively large, low resistance, deposits created by LMD-w.
1.2. Previous work In a previous study [15], the application of resistance measurements to control of the LMD-w process was investigated. A proof of concept was presented. Although promising results were obtained in [15] using on-line control of robot height position, the concept was only evaluated for a single deposited bead, not for a layerupon-layer geometry. For a single bead, the initial conditions are well defined and known beforehand. When depositing a multilayer structure, the outcome of the deposition of one layer serves as the setting for depositing the next, giving rise to accumulation and amplification of disturbances. In an industrial context, it is required that the control system is robust and is able to reject many kinds of disturbances also for multilayer deposits. One problem which was identified was that the high frequency disturbance contents of the acquired resistance signal may cause either unwanted high-frequency actuation, or require a low-pass filter with such low bandwidth that desired actuation is performed too slowly for feedback control. Due to these limitations for closed loop feedback control, based on resistance, the PI-controller previously employed will not suffice when the process is to be implemented in industry [15]. By instead basing control action during deposition on the height profile of previous layers, such as in the work by Heralic´ et al. [12],
Fig. 2. Material transfer modes, dependent on the tool-to-workpiece distance d.
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measurement inputs and actuation outputs can be filtered and thus limited in bandwidth. In this study, the focus is shifted from the measurement principle itself, which was presented in [15], to the integration of resistance based measurements into an industrially viable control system. Control action is based on the measurement signal produced while depositing earlier layers, exploiting the benefits of feed forward control for the LMD-w process, further described in Section 2.4. 1.3. Motivation
2.1. Resistance to distance model Similarly to the model previously devised by the authors [15], a static regression model is used for translating resistance into distance
^ dðkÞ ¼ uT ðkÞh
ð1Þ
^ denotes the estimated diswhere k is the discrete time instant, d tance d, see Fig. 2. The constant h is a column vector of regression coefficients, and the row vector T
0:5 2 u ¼ ½1; R0:5 ðkÞ; v 0:5 wire ðkÞ; Rx ðkÞ; Rx ðkÞ : x ðkÞ; Rx
This paper presents a refinement of the previous resistance measurement system, along with its implementation into a feed forward Iterative Learning Control (ILC) system. The benefits of this is illustrated by depositing not only a single bead as in [15], but a multi layer wall through control of the wire feed rate v wire and the robot z-position. The ILC control system has previously been successfully utilized for control of LMD-w based on height input provided by a laser scanning system [12]. The laser scanner is replaced by a resistance measurement system in this study, a replacement which is beneficial in several ways. First, no additional scanning time is required during processing. All required measurements are made during deposition while using resistance measurements instead of the laser scanner. All measurement data can be directly related to robot path coordinates when measuring resistance rather than requiring extraction from the point-cloud generated by the laser scanner [3]. Also, no scanner hardware has to be fitted to the processing tool. This saves both money and precious space when desiring to use small and nimble processing tools in order to facilitate access. Resistance measurement hardware can easily be fitted outside of the processing tool at a relatively low cost. It may also be added to existing LMD-w tools without any modifications to the tool itself. Another benefit of using resistance measurements instead of laser scanning is that resistance based measurement system is simpler from a calibration and maintenance point of view, and thus more suitable for production environment than one based on laser scanning. 2. Materials and methods
All constituents of u were found to be significant for the model with a confidence level of 95%. This model enables estimations of d based on the measurement of electrical resistance, Rx discussed in Section 2.2 and indicated in Fig. 1. The root mean square errors of the obtained model is less than 0:15 mm, which is in accordance to the value of 0:16 mm obtained for the model created in [15]. The scanner, which the proposed method is supposed to replace, is mounted to the robot which has a positional accuracy that limits the scanners resolution to 0:10 mm [3]. 2.2. Resistance measurement system In the previous study [15], the resistance was measured through readings of voltage over the process and measuring the current which was supplied by a weld source. This current acted as a ‘‘hot-wire’’ energy source for the process. If such an energy addition is unwanted, it is required that the current through the weld is significantly reduced. This is made possible by the improved measurement circuit presented below. By using a wheatstone bridge for measuring the unknown resistance Rx , as shown in Fig. 3, accurate measurements of Rx can be made for an unknown current passing through the circuit. The bridge is supplied with a DC source voltage, V 1 , with limited current output. In Fig. 3, Rref is chosen to be close to Rx , and R is selected to be in the same order of magnitude, in this case less than 1 X. The voltages V 1 and V 2 , as introduced in Fig. 3, can be measured and their ratio
K ¼ V 2 =V 1
In this section, an overview of the materials and methods utilized are presented.
ð2Þ
ð3Þ
can be calculated. Using the notation for resistances as indicated in Fig. 3, Rx can be calculated based on K and the known resistances
Fig. 3. Wheatstone bridge for measuring Rx .
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Rx ¼
RðRref þ KRref þ KRÞ : R KðRref þ RÞ
ð4Þ
The use of the wheatstone bridge allows reduction of current through the weld pool while still maintaining accuracy. Also, only one physical quantity, namely voltage, has to be measured instead of measuring both current and voltage. This means that the measurement hardware can be simplified from two digital acquisition modules to one. 2.3. Filtering of resistance signal The two sampled voltage signals V 1 ðkÞ and V 2 ðkÞ contain noise and are because of this filtered before their quotient KðkÞ, as introduced in (3) above, is formed. This is in order to avoid noise amplification through division. For this purpose, a discrete median downsampling filter with a window size of 0:4 s is used due to its abilities to suppress and reject noise, but not remove all high frequency components in the signal such as a linear low-pass filter would [16]. After filtering of V 1 ðkÞ and V 2 ðkÞ; KðkÞ and subsequently Rx ðkÞ are calculated according to (3) and (4) respectively. The signal Rx ðkÞ is then fed to the control system for translation ^ in (1). into d 2.4. Control system The main purpose for controlling the LMD-w process is to maintain a stable process in order to avoid the problems outlined in Section 1.1. The most successful control system so far was based on feed forward control of deposit height [12]. The height profile of the deposit served as input for control of the subsequent layer. Since no height profile information could be gathered during deposition but was collected after deposit of each layer, feed forward control was a natural choice. Even though it might seem tempting to try to devise a closed loop feedback control system based on resistance measurements, this approach has several drawbacks. Due to the process dynamics [12], control actuation should ideally be initiated before a height disturbance is reached and detected. This is not possible for feedback control. Also, since data from the previous layer would indicate the presence of such a disturbance in the current layer, it is wise to utilize this knowledge. Yet another benefit of feed forward control in this case, is the ability to use non-causal, i.e. zero-phase, filtering [17], of the height profile data along the deposition path. This avoids any lag in the control signal which would be detrimental to the stability of the process. Since the bandwidth of the actuation is much lower than that of the measurement signal, low-pass filtering of the control signal is required. The comparably low cut-off frequency of the low-pass filter would give a significant time lag in any feedback control system with causal filters. Hence, the use of feed forward control even when height measurements are available in situ, brings many benefits compared to a feedback, closed-loop control system. However, on-line measurements based on resistance can be used as a monitoring watchdog for setting an alarm if serious problems, such as droplet formation is detected. This is true even if a feed forward system is not implemented. Also, the resistance measure could prove to be valuable information to convey to an operator running the process manually. Provided a control system which successfully controls deposition of a part, the output of said system could be used as a recipe for producing identical parts on a larger scale. This requires that all disturbances can be assumed to be consistent for every new part. However, LMD-w is mainly expected to be used for low volume, high value products. With the low cost of the resistance based
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system and short production series, the benefit of using recipes instead of automatic control is limited. 2.5. Actuators As previously mentioned in Section 1.1, either robot position or wire feed rate can be adjusted in order to maintain a nominal value of d. In this paper, in contrast to the previous resistance based study [15], wire feed rate, v wire , is adjusted in order to perform control action during deposition. While the robot is limited to adjustments of at least 0:1 mm, the wire feeder’s feed rate does not exhibit any such practically limiting discretization. Also actuation of changes in v wire is faster and more exact than robot position changes. Because of this, the tool z-position is only adjusted, using the robot, before deposition of a new layer in order to ensure a suitable nominal distance between the deposition tool and the workpiece, and not during deposition. 2.6. Iterative learning controller During deposition any height variations within a layer are generated either due to disturbances (e.g. unpredicted variation in wire feed rate) or as a consequence of the chosen process parameters (e.g. dimensional variations of the molten pool due to temperature build-up). Experience has shown that the latter is the dominating factor and hence, any observed variations in layer topography are mainly deterministic. However, the dependency is complex and hard to predict. For each new setting of process parameters the a priori knowledge of the resulting system’s dynamics is as such limited. There is because of this a need for a controller that adapts to local process variations on-line. Since the deposition process is repeated, layer after layer, it can be seen as an iterative process with deposition layers as the iteration domain. Implementing an Iterative Learning Controller (ILC) seems therefore as a natural choice. Usually, invariant starting conditions are demanded for application of ILC, however as shown in [3,12], ILC proves to be a suitable control scheme also for LMD-w anyway, due to the strong repetitive behaviour of the process. The concept of learning repetitive tasks through iteration was mathematically formalized three decades ago, see e.g. the seminal works in [18,19] or a more recent survey paper [20]. Today, the ILC approach is mainly implemented for improving reference following in repetitive tasks which involve industrial robots. An important part of an ILC algorithm is its performance measure. Here, the goal is to maintain the distance d as close to dnom as possible, where dnom is defined as the desired distance between the nozzle and the last deposited layer, see Fig. 2. The way of achieving this is by both controlling the wire feed rate (e.g. to fill the cavities) and the robot offset (to compensate for wrong estimates of the layer height) as previously shown by Heralic´ et al. [3,12]. Included in these studies, Heralic´ investigates and proves that the ILC provides stability and height convergence to the LMD-w process. Defining the control structure in order to provide low deviations from a mean build height, the ILC is limited to only smooth out height variations, i.e. to control the wire feed rate in order to achieve a flat surface. This will minimize the control action and thereby also the process induced disturbances. Assume that the ILC algorithm operates on a set of N discrete points k (sampled with the sampling time T s ), which are uniformly distributed along the deposition path (see Fig. 4). Define the mean j as layer height h N X ¼ dnom 1 ^ ðkÞ þ h ^z h d j j N k¼1
ð5Þ
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Fig. 4. Illustration of time index k and layer (iteration) index j. Note that k is reset for each new layer.
Fig. 5. ILC architecture as implemented in this work. The delays are in the iteration domain and
^ ðkÞ is the estimated distance (obtained through resistance where d j measurements) between the nozzle and the underlying layer in a single point, i.e. at time index k, after layer j has been deposited, ^ z is the estimated layer height. and h The step-height compensator is then defined in the following , is way: After each deposited layer, the mean layer height, h j obtained and the robot’s tool position is offset in the z-direction , according to: by h j z T jþ1 ¼ T jz þ h j
ð6Þ
z j
where T is the robot tool’s z-position at layer j. Further, define the performance measure, at each discrete point k, as the error
^j ðkÞ ej ðkÞ ¼ d
N 1X ^j ðkÞ d N k¼1
ð7Þ
The error ej ðkÞ thus measures the deposited layer’s height variations from a mean value, at each discrete point k, which should be minimized. Using (7) the ILC algorithm can now be defined. Here, a second order ILC is chosen such that it incorporates the error measurements from the previous two iterations, i.e. ej ðkÞ and ej1 ðkÞ.
ujþ1 ðkÞ ¼ Q ðkÞ½uj ðkÞ þ qlk ðc1 ej ðkÞ þ c2 ej1 ðkÞÞ
ð8Þ
where uj is the control input that controls the wire feed rate on layer j; ci are the learning gains. The Q-filter, Q ðkÞ, is a zero phase low pass filter in k-domain, i.e. not in the layer-iteration domain j [21]. The Q-filter is based on a second order Butterworth filter.
v wire
is the nominal wire feed rate.
Furthermore, a time shift operator, qlk (q1 k uj ðkÞ ¼ uj ðk 1Þ) acts on the two error signals, ej ðkÞ and ej1 ðkÞ, where l denotes the number of samples the corresponding signal is shifted in time. For each new iteration, the time is reset as illustrated in Fig. 4. Fig. 5 shows the ILC architecture as implemented in this work. The higher order ILC is needed since each new layer is deposited on top of its preceding layer, and not on a new flat substrate. Hence, the error is inherited, and thus accumulated, from previous iterations. Due to this feature, as previously shown by Heralic´ in previous studies [3,12], the ILC controlled deposition system becomes unstable if a first order ILC is used. For clarity, the deposition process’ different steps are here summarized: 1. Time index k is reset when a new layer is to be built. 2. Layer j is deposited using the control action uj . 3. Distance d is estimated through resistance measurements using (1). is calculated using (5). 4. Mean layer height h j 5. Error ej is obtained using (7). 6. New control action ujþ1 is calculated using (8). z 7. Robot’s new height position T jþ1 is updated using (6). 8. Index j is incremented and the process is repeated. 2.7. Implementation 2.7.1. Hardware A six axis robot manipulator, ABB IRB 4400, is used for holding the deposition tool. The tool integrates a push–pull Fronius
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2.7.2. Software The core of the measurement and control system is implemented in National Instruments’ LabVIEW software. Data acquisition, communication with the robot and wire feeder as well as resistance signal filtering is performed using this software. Time synchronized process data, including calculated resistance values are saved to a MySQL database. From this database, a programme written in MATLAB extracts measurement data after each layer and calculates the control signal using the ILC algorithm. 2.8. Experimental procedure In this section, the experiment specific details are presented. 2.8.1. Control parameters and control signal filtering During deposition, the voltage quotient K, tool position and all process parameters were sampled at 25 Hz. The control parameters used essentially reflects those parameters previously used when employing a laser scanner as input to the ILC system [12]. The only difference is that median filtering with a window size of 50 samples, followed by low-pass, zero-phase, Butterworth low-pass filtering with a suitable cutoff frequency was applied to the control signal. This filtering is to ensure that all control signals match the actuator’s bandwidth. The use of a non-linear downsampling filter has proved to give good results, but comes with the drawback that the two filters may not be combined due to the non-linearity of the median filter. The other parameters used were
identical to the ones previously shown to give stability for the process [12]: static gain of the plant, K h ¼ 4:95 104 , the system’s time constant T h ¼ 0:37 s, learning gain parameters, c1 ¼ 1=K h and c2 ¼ 0:75c1 . The controller tuning considerations in [12] are summarised in the next paragraph. 2.8.2. Controller tuning considerations There are four controller parameters to consider in (8), the learning gains (c1 ; c2 ), the cut-off frequency of the Q-filter and the time shift qlk . Another important consideration is on the rate of change of the wire feed rate signal, which needs to be limited to mitigate the risk of droplets being formed. In [12] the Q-filter was initially set to Q ¼ 1 and l ¼ 0, i.e. qlk ¼ 1. Then simulations with the static gain K h (here K h 4:95 104 ) were performed and a suitable (c1 ; c2 )-pair was chosen to give a marginally stable system with c1 ¼ 1=K h and c2 ¼ 3=4c1 as a compromise between a reasonable response time and moderate control activity. The cutoff frequency of the Q-filter is set to 1 Hz as a trade-off between robustness and response time. The time-shift qlk compensates for the time delay of the plant, here T h ¼ 0:37 s. 2.8.3. Provoked disturbance A disturbance in deposition is caused by gradually decreasing the wire feed rate v wire from nominal (0%) to 25% and then back to nominal again as seen in Fig. 7. This disturbance is applied to all 10 5
Wire speed rate change [%]
TransSynergic 4000 wire feeder/weld source with the laser optics. An IPG, 6 kW fibre laser is used as laser power source. The laser light is conveyed to the optics through a 0:6 mm diameter optical fibre to eventually form a 3 mm diameter out of focus laser spot on the workpiece. All processing of material (Ti6Al4V), takes place in inert argon atmosphere contained by a processing chamber, in which the tool operates. The voltage source, limited to 1 A and 5 V, used for supplying the wheatstone bridge is developed in-house. The only necessary modification to the tool is shown in Fig. 6. A steel hose clamp is used for attaching a copper wire to the nozzle. Another copper wire is attached to the fixture, serving as the second contact point for Rx . The two copper wires are attached to the wheatstone bridge, illustrated in Fig. 3. The resistors with known resistances needed for the other positions (R and Rref ) were created from the same wire which was deposited. This results not only in resistors with resistance comparable to Rx but also resistors capable of conducting high currents. A National Instruments cDAQ-9172, rack with an NI 9215 voltage measurement module is used for measuring the voltages V 1 and V 2 .
0 −5 −10 −15 −20 −25 −30
0
20
40
60
80
100
120
140
160
180
200
Deposit length [mm] Fig. 7. Wire feed rate in percent as a function of deposition distance provoking a disturbance.
Fig. 6. Fastening of copper wire to nozzle using a hose clamp.
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deposited layers, with the goal of the control system to counter these disturbances and stabilize the process. 2.8.4. Deposition ^ z , was During uncontrolled deposition, a nominal build height, h assumed and the tool z-position was increased, using the robot manipulator, according to this at the beginning of each layer. ^ z is With the controller activated, the nominal build height h ^ in order to ensure a beneficial tool z-position adjusted based on d as described in Section 2.6. 3. Results and discussion For comparison, two walls were deposited. One was deposited with and the other without the control system activated. Without the control system, seven beads could successfully be placed on top of each other before droplets formed during deposition of the eighth layer. For the controlled wall, eleven beads were deposited without any problems. This can be seen as a quantitative measure of the deposition quality, since any control system for LMD-w should be judged on its merits regarding maintaining stable deposition and preventing failure. When depositing metal wire, the start and stop sequences are critical and require dedicated control, see e.g. [11] or [22]. Due to the focus of the paper
being control of the nominal process and not start or stop sequences, 15 mm at the beginning and at the end of the 200 mm deposit were not controlled. The buildup of material at the start and the lack of material towards the end of the wall, prevented deposition of any more layers, because these regions were not controlled. Future work will include an investigation into the suitability of using ILC also for the start and stop sequences. Such adoption will however most probably require substantial adaptation and tuning of the iterative learning controller or setting up separate controllers for the start and stop sequences. In Fig. 8, the resulting height profiles of the controlled and uncontrolled deposits, as measured by an ATOS Core 80 3D measurement system are presented. The deposits are shown in Fig. 9. Note that eleven layers were successfully deposited for the controlled case, whereas the uncontrolled case failed at the eighth layer. Hence the generally higher profile for the controlled case. The droplets formed during the last layer for the uncontrolled deposit can be seen as undulations between roughly 90 mm and 120 mm in Fig. 8 and as a wavy portion on the uncontrolled bead in Fig. 9. As shown in Fig. 10, the control signal clearly reflects the introduced disturbance. The ideal response to counter the disturbance is indicated with dashed lines in the figure. A control signal based on feedback control with the same bandwidth would, due to the lack of non-causal zero-phase filtering, be shifted to the right and
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Deposit length [mm] Fig. 8. Height profiles of controlled and uncontrolled tests as measured by a GOM ATOS Core 80 3D measurement system. The test pieces used for producing these profiles can be seen in Fig. 9.
Fig. 9. Uncontrolled wall in foreground, controlled wall in background. The surface profiles of these test walls can be seen in Fig. 8.
P. Hagqvist et al. / Mechatronics 31 (2015) 116–123
bridge implemented enabled enhanced geometric control. This improvement proved enough for extending the use of resistance based control to multi-layer structures. The contributions of this work amounts to a significant progression of LMD-w measurement and control. An expensive laser scanning solution, poorly suited for industrial adoption, is successfully replaced with a simpler and cheaper system based on resistance measurements. The proposed solution basically requires no changes to the deposition equipment or the deposition sequence. The concept of ILC, previously proven to be successful for controlling the process [12], is adopted for use with the input data provided by the measurement system. Application of the developed measurement and control solution is demonstrated and found to exhibit desired properties.
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actuated too late. This would give a mismatch in time between the disturbance and the compensation resulting in an unstable process. The plate onto which the deposit was added deformed during processing. Due to the fact that the plate was firmly clamped at the short-side of the plate, close to the start of the deposited bead, deformation close to the start was less than for the rest of the plate. This means that as the deposit generally ‘‘sunk’’ down due to deformation, the effect was less significant near the start. This led to a comparably higher measured height in the first part of the deposit, before the introduced deformation. The control action for this portion subsequently becomes to lower the wire feed rate in order to compensate for this comparably higher portion as seen in Fig. 10. The 3D profile measurement presented in Fig. 8, is compensated for the deformation. It too, reflects the aforementioned in that the part of the controlled bead which is near the start, and did not deform as much, has lower effective build height due to the high height measurement and subsequently lowered v wire . 4. Conclusion In this paper, the use of resistance measurements, instead of laser scanner information as used in previously devised systems [3,12], serves well as input to a second order ILC system, which has proven stability for the application [3,12]. Note that no tuning of controller parameters was performed after replacing the laser scanner with the resistance measurement system. It is found that the resistance based measurements result in accuracy similar to that of the laser scanner which is replaced by the resistance measurements. The resistance based measurements are of comparable accuracy to the scanner and it is within this study proven successful to replace the scanner data with resistance based data. Based on this, and taking the benefits of a resistance based solution, such as cost and in situ measurements, into account. It is recommended that it is used instead of scanner based solutions for LMD-w control. Future efforts should include such tuning, based on studies of capability for the resistance measurement system with regard to e.g. tool orientation and effective build height. Compared to an earlier study where resistance measurements for control of LMD-w was first introduced [15], the ILC system and the wheatstone
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