# Robotized grinding experiments of construction materials for asbestos removal operation

Quentin Trebot, Harrisson da Rosa Aguirre, Philippe Vaslin, Jean-Christophe

Fauroux, Frédéric Chapelle, Laurent Sabourin

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Quentin Trebot, Harrisson da Rosa Aguirre, Philippe Vaslin, Jean-Christophe Fauroux, Frédéric Chapelle, et al.. Robotized grinding experiments of construction materials for asbestos removal operation. IFToMM World Congress on Mechanism and Machine Science, Jun 2019, Krakow, Poland. 10.1007/978-3-030-20131-9 . hal-04877782

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# Robotized grinding experiments of construction materials for asbestos removal operation

Trebot Quentin¹, Da Rosa Aguirre Harrisson¹, Vaslin Philippe², Fauroux Jean-Christophe¹, Chapelle Frédéric¹, Sabourin Laurent¹

¹ Institut Pascal, SIGMA Clermont, CNRS, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.

² LIMOS, SIGMA Clermont, CNRS, MINES Saint-Etienne, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.

Quentin.Trebot@sigma-clermont.fr

**Abstract.** Asbestos is a carcinogen prohibited material that has to be removed from houses and buildings. The European project Bots2ReC aims at constructing cooperative mobile manipulators capable to replace human operators in contaminated areas, in order to remove construction materials that contain asbestos by robotized grinding. The main objective of this work is to measure the grinding forces generated by a rotating tool used in construction materials, as these data are lacking in the literature. Two materials and different grinding parameters are tested, and recommendations are also deduced for robotizing grinding.

**Keywords:** Construction robotics, Construction materials, Asbestos removal, Robotized grinding, Experimental tests.

## 1 Bots2ReC Project

**Context.** Health hazards due to asbestos inhalation have been known since the beginning of the 19th century. After inhalation, it may take several decades to develop painful and fatal lung diseases. Despite this, the use of asbestos in France lasted until its recent prohibition in 1997. As a result, large quantities of construction materials present inside buildings contain asbestos fibers [1]. The disintegration of these materials causes dispersion of fibers in the environment, contaminating living beings. When removing contaminated materials, numerous precautions have to be taken. Workers need to be extensively trained for the exhausting removal task performed with sealed suits in uncomfortable positions. In addition, Asbestos Removal-Construction Sites (ARCS) need to be artificially isolated from the outside atmosphere and the working time is restricted to two hours due to health regulations. These factors explain the high cost and long delays for ARCS.

**State of the art: Asbestos removal mobile robot.** The Bots2ReC project [1] aims to robotize the task of asbestos removal. The main goal is to reduce workers'exposure to dangerous asbestos fibers while maintaining the same task productivity [2]. Mobile Manipulators (MMs) that have to be designed will remove asbestos by grinding. They also have to comply with requirements specific to ARCS located in flats: less than 400 kg, passing in elevators, narrower than 600 mm to pass through doors and reaching ceiling at 3 m high. Two of the existing commercial systems [3], [5] also use grinding operation to remove asbestos contamination (see Fig. 1a & 1c). Material removal with water at ultra-high pressure (UHP) is used by "AS Protek" robot (see Fig. 1b) [4].
However, in this case, management of the contaminated water is complex. Additionally, a cumbersome cartesian frame has to be mounted and positioned manually on the contaminated surface. “Brokk” system [5] is also penalized by its end-effector using a long slider, too wide to pass through doors. “Eco-Amiante” robot [3] is heavy and limited to floor grinding.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Asbestos removal mobile grinding robots: a) Grinding mobile robot “Eco-Amiante” [3]; b) “AS Protek” robot [4]; c) Hydraulic grinder gliding end-effector by “Brokk” French subsidiary [5], d) MM for asbestos removal on beams [6].</figcaption></figure>

**Synthesis.** From the state of the art, two important conclusions can be drawn. Firstly, the dry grinding operation (with large grinding wheel, 250 mm of diameter [5]) is adapted to remove asbestos layer permanently glued into materials. Secondly, a difficulty exists in terms of maintaining an appropriate position and orientation of the removal tool with respect to the geometry of contaminated layers. Position and orientation errors of grinding robots associated with the asbestos layer geometrical variability lead to a poor quality of the contaminated layer removal operation. In order to compensate this problem “Eco-Amiante” [3] and “Brokk” robots [5] are equipped with compliant mechanisms, which permit correcting passively position and orientation errors experienced by their material removal tool. “AS Protek” robot needs a physical set up made by operators between its frame, fixed along the contaminated layer, and its tool to correctly position and orient the latter with respect to the asbestos material. To control the quality of the contaminated material removal operation, made by the asbestos removal on beams MM [6], a camera system and a force sensor are used. However, concerning the grinding process of construction materials, neither experimental results nor grinding parameters could be found. Only patents about grinding parts are available. Finally, links between the grinding process of the construction material and the resulting requirements of the robotic system are unknown. In order to determine the optimal grinding parameters for several construction materials, a comprehensive study has been performed based on grinding state of art. Then, an experimental protocol for grinding construction material has been created. Finally, an analysis of experimental test results has been produced.

## 2 Grinding operation for removal of construction materials

**Abrasion mechanism.** An abrasion tool involves 'cutting-grains' that are physically linked to each other, thanks to a bonding material. Each grain acts as a little tool at a millimetre scale, removing a little chip when touching the material to be ground.
Three main physical phenomena are present in the grinding process: sliding, ploughing, and cutting [7]. They cannot be separated from each other. An efficient grinding process occurs when the cutting mechanism is dominant over the two others. In a reverse situation, energy sent in the process is mainly dissipated in thermal losses and can lead to the damage to either the grinding tool or the workpiece.

**Grinding- state of art.** A large scientific documentation exists about the industrial grinding process [8] [9]. However, it remains a relatively complex machining operation that serves to shape metals, ceramics, and glass materials. The grinding objective considered by the project, the least valued in industry, can be defined as the maximum material removal rate on a raw workpiece. It is relevant for the Bots2ReC project since the need is to operate on large surface areas with a contaminated layer depth of several millimeters. This grinding process is precisely named as Creep Feed Grinding (CFG). Its industrial version [9] corresponds to a material removal process with a low feed speed (v) combined with important removal-material thickness (i.e. depth of cut, a). The Material Removal Rate (MRR) Q in mm³/s for CFG is not the best one but the required machine characteristics are less restrictive: our mobile robotized grinding system will be less stiff, less precise, and less powerful compared to the massive industrial grinding machine. This problem appears for the Scompi robot [10][11] (see Fig. 3b). That is why our robot system will have to use a grinding task only with low MRR in order to correctly perform the task. The notion of industrial grinding incorporates numerous grinding processes using a grinding wheel. It is characterized by several criteria: surface shape obtained after the process (contour grinding, form grinding, surface grinding, etc.); height of removed material (deep or shallow grinding); nature of the grinding wheel motion along the workpiece (plunge or transverse grinding, etc.); localization of the machined surface along the workpiece (internal or external grinding); active grinding wheel face (face or peripheral grinding) and relative motion existing between the grinding wheel and the workpiece (up-grinding, down-grinding, plunge grinding, etc.). Finally, for this paper, the grinding process shaped a flat surface with a feed motion tangential to the external face of the workpiece. Moreover, two faces of the grinding wheel are used (its periphery and its normal face to the rotation axis) to remove an important depth of the material. It is an external longitudinal surface grinding process combining simple face and peripheral grinding operations.

**Application of construction-material grinding process.** Hand-held grinder and floor grinder are the most common machines present in the market for construction material grinding.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Typical teeth geometry on grinding wheels (http://www.diamag.eu): a) Blue grinding wheels for soft construction materials, b) Black grinding wheels for hard construction materials.</figcaption></figure>
The hand-held grinder could be mounted on MMs with minimal design changes. The grinding tool that we used differs from industrial tools. It is smaller and constituted of a stiff cup wheel on which abrasive segments (i.e. teeth) are attached along concentrically circles (see Fig. 2a & 2b). The teeth have a metallic matrix of unknown composition inside which are included abrasive diamond cutting grains. Each tooth has two working surfaces named primary and secondary working zones (see Fig. 3a). Segmented grinding wheel structure [8] reduces its weight, provides efficient cooling, and provides efficient chip evacuation. It is an efficient tool (9 teeth by cylindrical row, teeth eight of 5 mm and height of 18 mm) able to be handled by an operator.

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Grinding mechanism: a) Material removal in vertical spindle face grinding with horizontal feed [8]. b) Scompi robot in grinding configuration [10] [11].</figcaption></figure>

Two basic cutting operations are classified in grinding process: the Transverse grinding operation (TGO) and the plunge grinding operation [13]. The TGO is the simplest to study for first experiments. The conclusion of the grinding Scompi robot project [10] shows the impossibility of application of advanced industrial grinding theories on robotized grinding system. Then, only simple tests are made to understand different mechanisms of robotized grinding while ensuring the desired quality of results. From the experimental proposition of Shi et al, in [12] and the industrial TGO knowledge presented in [8] and [9], a different grinding cutting study is proposed. It imposes cutting parameters (feed speed *v* and depth of cut *a*) to the grinding wheel and measures the force generated in the process.

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Transient cut-in and cut-out stages for vertical spindle surfacing during a Transverse Grinding Operation (TGO).</figcaption></figure>
The main goal of an efficient robotized TGO for construction material is to have a maximum MRR with generation of minimum grinding force and absorbed power, without creating damage. TGO is divided in three phases (see Fig. 4): cut-in phase (A-C), transition cutting phase (C-D) and cut-out phase (D-F). They are corresponding to time limits 1 to 6 (first and last contact between the wheel and the material) (see Fig. 4). During, the cut-in and cut-out phases, a non-constant force evolution (as well as a non-constant power evolution) is expected because of the non-linear MRR due to the evolution of wheel/material contact surface with time.

## 3 Experimental construction material grinding process

**Goals and description.** The project needs cutting reaction force data related to the depth of cut *a*, feed speed *v* and {grinding wheel, grinder} characteristics used to improve the MM architecture dimensioning and its control laws. No data are currently available. Hardware availability is also limited. Thus, simple robotized grinding operation is studied for a single layer of construction material fixed on a supporting frame (see Fig. 5-6). The main construction materials selected for the study are plaster (considered soft) and resurfacing concrete (RC, considered hard, see Fig. 2). Neither of these materials include asbestos nor are available to purchase due to safety reasons. According to "Bouygues Bâtiment", we assume that mechanical properties of materials with and without asbestos fibers are similar. For initial tests, an industrial robotic arm with a fixed base is used (see Fig. 5) to avoid stability issues typically experienced by MMs.

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Experimental test hardware organization: a) Top test cell organization view; b) Robot/test plate at their rest configuration in the test cell.</figcaption></figure>

**Material protocol.** For the test, cutting parameters describing the grinding operation (see section 2) are theoretically managed by the speed and the position control loops. The grinding feed speed *v* is imposed as a theoretical constant. The same assumption for the depth of cut *a* is chosen for each test. The grinder mounted on the RTS (robot terminal segment) is a hand held Blastrac grinder (BGV 125-AV with $P_{nominal} = 1,3$ W and $N_{nominal} = 11$k trs/min) coupled with a disk of *d* = 125 mm diameter (see Fig. 2 and 6b). Grinding reaction forces are measured by a 6-axis TSR force table on which the test plate is clamped (see fig. 5b). The force table of width *l* = 600 mm is clamped to the ground. Each sensor device is integrated in a measurement chain (see Fig 5a). The power absorbed by the grinder is measured by a power-meter (PEL103).
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Full material layer TGO configuration: a) TGO scheme; b) The grinder and the grinding wheel mounted on the robot.</figcaption></figure>

**Configuration of the robot and the force table.** Gravitational acceleration vector should be kept colinear with the rotation axis of the grinder spindle. Gravity pushed the grinding wheel against the machined surface. Thus, robot configuration during each test is in favor of the grinding process. Zero offset position between the grinding tool and the test plate is made, thanks to a preparatory surfacing operation. Acceleration and deceleration of the STR are performed during cut-in and cut-out phases while constant feeding speed is ensured during the TGO phases. The resulting feed trajectory is parallel to the smaller side of the force table (see Fig 6a). With the available hardware, force table has been calibrated with a definitional uncertainty (u) of 5 N for X, Y and Z axes. A high acquisition time for each test limited the frequency measurement of force table signals at 100 Hz. Hence, mechanical shocks due to the input of each tooth on material may not be taken into account.

**Force table use.** The test plates used in the experiments had an important weight (600 N maximum), which shifts all the measurements of 600 N. Cutting reaction force ranges were unknown. From preliminary tests, a possible maximum force of 200 N produced by a TGO was observed. Thus, an active measurement interval [0, 200] N was established. With the test plate, the nominal indication interval (NII) on the Z-axis becomes [0,800] N. X- and Y-axes NII [-800 800] N are chosen to have a force table calibrated with a same maximum value on all its measurement axes. Preliminary experiments showed that the measurement uncertainty *u* of the force table was very close to the cutting reaction forces measurements. Thus, true quantity value evolution of force reactions cannot be characterized. However, experiments did not intend to precisely characterize one grinding process for one material. Its main objective was to observe for the first time a new type of material grinding in order to help MM dimensioning and control. Each force table measurement (*M*<sub>i</sub>) is the center of a constant uncertainty interval [*M*<sub>i</sub>-*u*, *M*<sub>i</sub>+*u*]. True quantity force value is in this uncertainty interval. So *M*<sub>i</sub> is the middle of the uncertainty interval. The difference between one measurement uncertainty interval middle *M*<sub>i</sub> and the force table zero is characterized by *F*̅ in each test done instead to characterize true quantity force measurements with a too high uncertainty (*F*).

Moreover, the reaction forces have an offset error (*E*<sub>0</sub>) obtained by using the force table sensors despite the calibration. This could be due to the force table age. An offset error of measurement appears when a sensor gives a false reading when the true reading is zero after the sensor solicitation. At each time step, the real measure (*m*<sub>i</sub>) is affected by a growing offset error *E*<sub>0</sub> proportional to time. *E*<sub>0</sub> is computed for each of our tests with equation 1. Thus, we assumed that the error is linear in time for each
test with *n* measurements. The raw force curves have been treated this way with equation 2 and presented in Fig. 7, 8, 9 and 10. For each case presented, a shift of zero point is observed for force values recorded post grinding operation. Mechanical self adjusting clearance inside the force table assembly are supposed to be the source of this phenomenon.

# 4 Results

**Test plan.** Table.1 synthesizes TGO tests performed for extreme values of the whole test plan made with the controlled cutting parameters. Several preliminary tests have been made with gradually increased depth of cut *a* and feed speed *v* between each test until the force table could measure the process reaction force (determination of minimum values) and until the grinder nominal power (*P*<sub>nominal</sub>) was reached (determination of maximum values).

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Force reaction components for the full resurfacing grinding layer Test_01 with *a* = 1 mm and *v* = 31,25 mm/s as cutting parameters.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** Force reaction components for the full resurfacing grinding layer Test02 with *a* = 2 mm and *v* = 46,9 mm/s as cutting parameters.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 9 Force reaction components for the full plaster grinding layer Test03 with a = 1,5 mm and v = 31,25 mm/s as cutting parameters (material machined length = 537.5 mm instead of 600 mm). Note: Limit 1 position shows the shallow volume of ground material due to real a which is smaller than the one expected because of the low amplitude of the reaction forces at the beginning of the TGO. The actual limit were defined manually (4*, 5* and 6*). Global plaster layer orientation was not parallel to the trajectory of the robot so the tool goes out of the material before the RTS reaches the estimated limits 4, 5 and 6.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 10. Force reaction components for the full plaster grinding layer Test04 with a = 2 mm and v = 46,9 mm/s as cutting parameters.</figcaption></figure>

$$E_0 = \sum_{i=\text{Last measure}}^{i=\text{Limit 6}} \sqrt{m_i^2} - \sum_{i=\text{First measure}}^{i=\text{Limit 1}} \sqrt{m_i^2} \quad (1)$$

$$M_i = m_i + \frac{E_0}{n} * i \quad (2)$$

$$Q = (\text{Removed material volume}) \div (\text{Machining time}) = (a \cdot d \cdot l) \div \frac{(d+l)}{v} \quad (3)$$

$$e = \frac{P}{Q} \quad (4)$$
<table><caption>**Table 1.** Test data with input variables depth of cut *a*, feed speed *v*, grinder absorbed power *P*.</caption><thead><tr><th>Test name</th><th>Machined layer</th><th>a</th><th>v</th><th>P</th><th>F&#773;<sub>x</sub></th><th>F&#773;<sub>y</sub></th><th>F&#773;<sub>z</sub></th><th>Q</th><th>e</th></tr><tr><td></td><td></td><td>mm</td><td>mm/s</td><td>W</td><td>N</td><td>N</td><td>N</td><td>mm<sup>3</sup>/s</td><td>W.s/mm<sup>3</sup></td></tr></thead><tbody><tr><td>Test01</td><td>RC</td><td>1</td><td>31.5</td><td>335</td><td>-5.7</td><td>-4.3</td><td>-17.3</td><td>3125</td><td>0.11</td></tr><tr><td>Test02</td><td>RC</td><td>2</td><td>46.9</td><td>1279</td><td>-16.3</td><td>-42</td><td>-62.2</td><td>9380</td><td>0.14</td></tr><tr><td>Test03</td><td>Plaster</td><td>1.5</td><td>31.5</td><td>394</td><td>-5.3</td><td>8.5</td><td>-6.8</td><td>4783</td><td>0.09</td></tr><tr><td>Test04</td><td>Plaster</td><td>2</td><td>46.9</td><td>471</td><td>-17.5</td><td>-30.6</td><td>-8</td><td>9380</td><td>0.05</td></tr></tbody></table>

**Analysis of results.** The beginning of the TGO test (Time limit 1: t_1, (see Fig. 4)) is defined manually thanks to the data taken from test videos recording. The other time limits (2, 3, 4, 5 and 6) (from t_2 to t_6) are computed by estimating TGO parameters, “l” and “v”. A net change in $\bar{F}$ happens at the limits t_1-t_2 for all the tests except for the test 3 because of the low amplitude of the $\bar{F}$ at the beginning of the TGO. Furthermore, due to lack of sufficient material length, about 95% of the primary working zone (see Fig. 3a) loses contact with the material a little earlier than t_5 showing a change in reaction force. We assumed that the volume of the material machined between t_5 and t_6 is considered negligible to generate significant reaction force. Thus, we also assumed that the wheel primary working zone is dominant in the generation of reaction force for grinding RC and plaster material. Outside of the cut-in and cut-out phases, reaction force curves are not as constant as expected (see Fig.4). It might be explained by several phenomena: depth of cut evolution may be linear but not constant throughout the trajectory. Lack of stiffness in the robotic arm, non-linear arm configurations, chip clogging phenomenon and material relaxation contribute to the effect as well. Moreover, Test 03 shows the tool orientation problem.

**Data analysis.** $\bar{F}_z$ is exceeds the other tangential components $\bar{F}_x$ and $\bar{F}_y$ in RC tests. The contrary happens in the case of plaster tests. Furthermore, if the grinding parameters “v” and “a” are considered as constant outside cut-in and cut-out phases, a comparison between tests efficiency can be made. The theoretical MRR (Q) shows the productivity for each test (equation 3). Grinding energy per volume unit “e” shows the efficiency in the grinding process (equation 4). Plaster grinding seems to consume twice less energy for a similar productivity than RC grinding. However, individually, RC TGO generated higher $\bar{F}$ and absorbed more power than plaster TGO. Plaster and RC have not at all the same grinding behavior in term of generated forces and consumed energy.

**Conclusions and perspectives.** This experimental study observed globally the robotized TGO behavior for construction materials. Our main result was the analysis of the middle of uncertainty interval grinding forces $\bar{F}$ with several cutting parameters and two materials (resurfacing concrete and plaster). This work presents for the first time experimental data of this grinding process, that cannot be found in literature up to now. The upper bond of $\bar{F}$ will be used for dimension the MM designed in the Bots2ReC H2020 project. However, the result of this experimental characterization showed the need to use a measurement hardware with less uncertainty in order to precisely quantify the grinding phenomena for modeling construction materials. Another conclusion was to take care of the difficulty to maintain constant the
controlled depth of cut *a* and the different TGO cutting variables responses. A protocol similar to the one used in the Scompi project [11] should be used to reduce this issue. The considered surface was scanned with respect to a reference frame and the layer to be removed was defined. Then the robot's grinder with a hybrid force/position controller machined the surface. Finally, the machined surface was scanned to qualify the grinding operation quality and determine if other passes were necessary. Moreover, these experiments showed a high dependency of TGO behavior on the particular material nature. Future work will aim to build an analytical model of the grinding force depending on the material, the feed speed *v* and depth of cut *a*, for a given tool and robot. Reaction torques on the tool and more accurate data on volume of material removed have also been measured during each test. They must be interpolated to be integrated in our multibody models of the MM robot during grinding in order to evaluate its stability.

**Acknowledgments.** This project has received funding from the European Union's Horizon 2020 research and innovation program under Grant agreement no.687593. The authors extend their gratitude to the CTT SIGMA team for their technical support. We also particularly thank V. Gagnol and A. Maamar for the measurement hardware lending and their precious recommendations.

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