1 Introduction

The surface miner was developed in the mid-70s improving the design concept of the road milling machine, which were popularly used to cut the old road surface during road construction (Dey 1999). The surface miner essentially comprises a cutting unit, disposing unit (windrowing/conveyor loading) and propelling unit (Fig. 1a). The surface miner is a track mounted machine, it is mounted either on three or four tracks depending on the type of model. Each set of crawler unit has a separate steering cylinder to ensure sharp turns. It has a powerful diesel engine and hydraulic pumps for transmitting the power for different operations. The heart of the surface miner is the milling drum, which cuts and loads the material onto the primary conveyor. The milling drum is fitted with specially manufactured tungsten carbide tipped steel cutting tools mounted in tool holders. There are 70–120 cutting picks depending on the machine application and type of model. The tool holders are arranged on the milling drum in a spiral fashion. The milling drum rotates in an up milling direction and a layer of predetermined thickness of the deposit is cut and crushed to the optimal size by the cutting tools. Depth of cut is controlled by the “electronic depth control system” which enables the milling drum to achieve the preset cutting depth. The rock is excavated by the milling drum utilizing the combination of normal force and drag force. Angle of attack of the pick is important as shown in Fig. 1b. As per the rule of thumb, the clearance angle is kept less around 5° and rake angle is kept around 25°. Depth of cut is important for determining the theoretical output (depth of cut × drum width × velocity of movement) of surface miner. However, depth of cut depends on the drum diameter, machine power, and type of material. With the increase in the depth of cut, load on the machine increases, which in turn, decreases the speed of the machine.

Fig. 1
figure 1

Schematic diagram of surface miner and theory of rock cutting (a) Schematic diagram of surface miner (courtesy manual Wirtgen GmbH) (b) Rock-cutting principle using point attack tool

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Since the 1990s, surface miners have gained in popularity with improved design of the cutting drum and higher machine power. This has enabled the users to excavate rock from in situ with competitive cost and eco-friendliness. Surface miners can be classified in three categories based on the principle of rock cutting, namely, (1) cutting drum positioned in front of the machine, (2) cutting drum positioned below the machine and rotate in up-cutting direction and (3) cutting drum positioned below the machine and rotate in a complementary direction. Among these, the second model is the most popular and its cutting system is given in Fig. 1a. Keeping the cutting drum below the machine has the added advantage that it utilizes machine weight in cutting of the rock.

For economical rock excavation, therefore, using surface miners, two basic elements should be considered––the machine and the rockmass. A machine is the product of human ingenuity and can be modified to suit a specific requirement (Eskikaya and Tuncdemir 2007). However, a rockmass is a natural component in the earth’s crust and is thus immutable. Therefore, it is imperative to understand the rock to be excavated prior to selecting the machine. This contribution seeks to present a rational basis for evaluating the applicability of surface miners using a rockmass classification system.

2 Cuttability Indices–A Review

“Cuttability” of a surface miner depends on a host of influencing parameters. These parameters can be categorized as rock/rockmass parameters, machine parameters and the type of application. The properties relevant to the classifications are detailed in Table 1.

Table 1 Parameters influencing cuttability of surface miner
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Rockmass classification forms the backbone of the empirical design approach. It is necessary to recognize the caveats implicit in its application (Ghose 1996). The existing rockmass classification systems available for prediction of suitability of mechanical excavation are reviewed here. The pioneering system of this type of classification was the Discontinuity Strength Classification (Franklin et al. 1971). This was followed by a Rippability Rating Chart (Weaver 1975), Excavation Index (Kirsten 1982), Geological Factors Rating Scale (Minty and Kearns 1983), Engineering Classification of Coal Measures (Scoble and Muftuoglu 1984), Rippability Chart (Singh et al. 1986), Excavatability Index Rating Scheme (Hadjigeorgiou and Scoble 1990), Diggability Index (Karpuz 1990), Revised Excavatability Graph (Pettifer and Fookes 1994) and many more. The manufacturers of the surface miners compute performance curves mostly based on the Unconfined Compressive Strength (UCS) or the ratio of compressive and tensile strengths. These indices have been used either directly or indirectly to select appropriate excavation systems, the equipment used in mining and assessment of the excavatability (Kramdibrata 1998).

Table 2 Rock strength classification (Atkinson et al. 1986)
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2.1 Cuttability Indices for Cutting with Drag Pick

Barendsen (1970) developed a relationship (Fig. 2) between specific energy and uniaxial compressive strength for the machines working with cutting (drag bit) and crushing (rotary bit) principles, respectively.

Fig. 2
figure 2

Prediction of specific energy required for cutting (Barendsen 1970)

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Atkinson (1971) was the first to classify the excavability of the rockmass based on the field seismic velocity measurement as in Fig. 3. However, this only gives an idea of the “GO–NO GO” situation.

Fig. 3
figure 3

Determination of excavation possibilities (Atkinson 1971)

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Rasper (1975) proposed the following formula to calculate the cutting power required for bucket wheel excavators

$$ N_{\text{c}} \, = \, {\frac{0.0054}{\eta }} \, F_{\text{L}} \, (L^{*} \, n_{\text{s}} \, R)^{0.5} $$
(1)

where,

N c = cutting drive power (kW)

η = efficiency

F L = linear specific cutting resistance (kN/m)

L * = production (bcm/h)

n s = number of bucket discharge per minute

R = radius of the wheel (m).

Singh et al. (1986) used the Toughness Index (TI), which is believed to be a measure of elastic strain energy requirements for deforming using a cutting tool and is derived from UCS and Young’s modulus as given below:

$$ {\text{TI}} = \, {\frac{{\sigma_{\text{c}}^{2} }}{2 \, E}} \, \times \, 100 $$
(2)

where,

TI = toughness index (MPa)

E = Young’s modulus (GPa)

σ c = unconfined compressive strength (MPa).

Similarly, Farmer (1986) proposed the fracture index or rock toughness, which is defined as the strain energy required to fracture the rock and is estimated as:

$$ {\text{EI}} = \, {\frac{{\sigma_{\text{c}}^{2} }}{E}} \, = \, {\frac{N \, \eta }{{L^{*} }}} $$
(3)

where,

EI = energy input

N = power (kW)

η = efficiency

L * = production (bcm/h)

E = Young’s modulus (GPa)

σ c = unconfined compressive strength (MPa).

Farmer also developed a performance prediction curve for DOSCO-IIIA machine in coal measure rocks as shown in Fig. 4.

Fig. 4
figure 4

Relationship between rock toughness and volume extraction (Farmer 1986)

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Atkinson et al. (1986) used the toughness index, UCS and Young’s modulus to classify the rock strength as shown in Table 2. He proposed that a toughness index of more than 27 represents un-economical cutting condition.

Roxborough (1987) utilized the specific energy derived from an instrumental cutting test (core cuttability test) to relate performance of medium/heavy weight road headers (Table 3).

Table 3 Selection of road header based on laboratory specific energy test
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Bilgin et al. (1988) estimated the advance rate of a road header using UCS and rock quality designation (RQD). The proposed rockmass cuttability index (RMCI) was defined as:

$$ {\text{RMCI}} = \, \sigma_{\text{c}} \, \left( {{\frac{\text{RQD}}{ 1 0 0}}} \right)^{2/3} $$
(4)

where,

RMCI = rockmass cuttability index (kg/cm2)

RQD = rock quality designation (%)

σ c = unconfined compressive strength (kg/cm2).

A relationship between the advance rate of a road header of cutting power less than 100HP against the rockmass cuttability index was derived and is shown in Fig. 5.

Fig. 5
figure 5

Relationship between advance rate and RMCI (Bilgin et al. 1988)

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Gehring (1989) proposed that the performance of a road header (rock-cutting machine) could be defined as:

$$ L^{*} = \, {\frac{k}{{\sigma_{\text{c}} }}}N $$
(5)

where, L * = production or cutting performance (bcm/h)

N = cutter head power (kW)

k = a factor for consideration of relative cuttability or tuning effect between road header and rock

σ c = unconfined compressive strength (MPa).

In 1992, Gehring modified his earlier formula for use of Voest-Alpine Surface Miner (VASM-2D) as follows:

$$ L^{*} = \, {\frac{{k_{1} \, \times \, k_{2} \times \, k_{3} }}{{\sigma_{\text{c}} }}}N $$
(6)

where, k 1  = relative cuttability of intact rock and,

= 6 for very tough and plastic rock

= 7 for tough and plastic rock

= 8 for average rock

= 9 for brittle rock

= 10 for very brittle rock

= 10–15 for coal

k 2  = influence of discontinuity such as joint, bedding plane etc. and,

= 1 for massive and discontinuity distance >25 cm

= 1.5–2 for layered/fissured, thinner bed rock, discontinuity 10–25 cm

= 2.5 for layered/fissured/interbedded rock discontinuity <5 cm

k 3  = influence of specific cutting condition and is a function of dry/wet cutting, cutting height, cuter head flexibility, pick array, pick shape. However, for road header its value ranged from 3.5–4.5.

Gehring classified rock for application of TBMs and road headers based on the Voest-Alpine Rock Cuttability Index (VARCI) as given in Table 4.

Table 4 Selection of TBM and road header based on VARCI (Gehring 1980)
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Hadjigeorgiou and Scoble (1990) developed an excavation index classification scheme and correlated it with other excavation indices. The index was developed using some rock and geologic parameters as rated below:

$$ {\text{EI}} = \, (I_{\text{s}} + B_{\text{s}} ) \, W \, J_{\text{s}} $$
(7)

where,

EI = Excavation Index

I s = point load strength index

B s = block size index

W = weathering index

J s = relative ground structure index.

The above-mentioned excavating index rating scheme is detailed in Table 5.

Table 5 Excavating index rating scheme
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Kolleth (1990) proposed an application range for excavators, scrapers, surface miners and bucket wheel excavators based on the compressive strength of rock samples (Fig. 6).

Fig. 6
figure 6

Application range of equipment based on rock strength (Kolleth 1990)

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Natau et al. (1991) developed a correlation between the road header performance and UCS for brittle and non-plastic rocks as shown in Fig. 7.

Fig. 7
figure 7

Change in the cutting rate of road header with UCS (Natau et al. 1991)

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Fowell and Johnson (1991) used a core cuttability test to determine the specific energy. A relationship, between the obtained specific energy and actual cutting rate in the field, has been developed for medium and heavy weight road headers (Fig. 8).

Fig. 8
figure 8

Cutting rate of road header from specific energy (Fowell and Johnson 1991)

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Bolukbasi et al. (1991) have also used laboratory-specific energy to describe diggability (Table 6).

Table 6 Diggability assessment from laboratory specific energy (Bolukbasi et al. 1991)
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Thuro (2003) utilized ‘specific destruction work’ (W z) to predict the cutting performance of a road header. This ‘specific destruction work’ is the work done required to fragment the rock (or energy required to create new surface area) and is expressed in kJ/m3.

2.2 Cuttability Indices for Ripping

Franklin et al. (1971) developed a rockmass classification using the fracture index and the point load index. Different zones of ripping, digging, and blasting were shown in a plot of fracture index and point load index (Fig. 9).

Fig. 9
figure 9

Proposed zones for digging, ripping and blasting (Franklin et al. 1971)

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Kirsten (1982) identified the parameters influencing the excavatability of the rock, namely, strength of rock, in situ rock density, degree of weathering, seismic velocity, block size, shape of excavation relative to excavating equipment, block shape, block orientation, joint roughness, joint gouge, joint separation. Kirsten formulated an Excavatability Index, a similar system like NGI ‘Q’ Index, as follows:

$$ N \, = \, M_{\text{s}} \, {\frac{\text{RQD}}{{J_{\text{n}} }}} \, J_{\text{s}} \, {\frac{{J_{\text{r}} }}{{J_{\text{a}} }}} $$
(8)

where,

N = excavatability index

M s = mass strength number

RQD = rock quality designation

J n = joint set number of Q-system

J s = relative ground structure number

J r = joint roughness number of Q-system

J a = joint alteration number of Q-system

According to Kirsten, the rippability can be assessed as given in Table 7.

Table 7 Assessment of rippability based on excavatability index (Kirsten 1982)
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Pettifer and Fookes (1994) proposed a classification to assess the rippability of the rockmass. Similar to Franklin et al. in this classification, the point load index and the discontinuity spacing index have also been utilized (Fig. 10).

Fig. 10
figure 10

Rippability assessment by Pettifer and Fookes (1994)

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2.3 Cuttability Indices for Cutting with disc cutter

Graham (1976) developed an empirical equation to predict the penetration rate of the tunnel-boring machine (TBM) for known UCS values utilizing the experience of the TBM manufacturer:

$$ P = \, {\frac{{3940 \, F_{\text{c}} }}{{\sigma_{\text{c}} }}} $$
(9)

where,

P = penetration rate (mm/rev)

F c = average cutter force (kN)

σ c = unconfined compressive strength (kN/m2).

McFeat-Smith and Tarkoy (1979) correlated the point load index with the advance rate of tunnel-boring machines (TBM) for two types of models, a Robbins 123-133 and a Demag 34-38 TBM models as shown in Fig. 11.

Fig. 11
figure 11

Relationship between point load index and penetration rate (McFeat-Smith and Tarkoy 1979)

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Farmer and Glossop (1980) modified Graham’s formula by replacing the compressive strength with the tensile strength value in arriving at the penetration rate of TBM as given below:

$$ P = \, {\frac{{624 \, F_{\text{c}} }}{{\sigma_{\text{t}} }}} $$
(10)

where,

P = penetration rate (mm/rev)

F c = average cutter force (kN)

σ t = indirect tensile strength (kN/m2).

Hughes (1986) established that the advance rate of full-face tunnelling machines with disc cutters is a function of thrust per disc, speed of cutting, average number of discs per kerf, average radius of disc and unconfined compressive strength. The relationship proposed is given as:

$$ P = \, {\frac{{6 \, F_{\text{t}}^{1.2} \varpi \, n}}{{\sigma_{\text{c}}^{1.2} \, r^{0.6} }}} $$
(11)

where,

P = penetration rate (m/h)

F t = thrust per disc periphery (kN)

ω = speed of cutting head (rev/s)

n = average number of disc per kerf

r = average radius of cutter disc (m)

σ t = unconfined compressive strength (MPa).

Einstein et al. (1979) provided a neat critique of expectations of classification system as follows:

“(1) they should promote economical and yet safe design,

(2) they must be correctly calibrated against test cases and those test cases must be representative of the field application for future use,

(3) they should be complete in that all relevant factors are included, yet they must be practical in that parameters can be determined and with acceptable certainty,

(4) they should have general applicability and robustness to the vagaries of use, yet they must be recognized as fundamentally subjective.”

In addition to the above, Ghose (1996) postulated “there has been no dearth of efforts in adding sophistry to the essential simplistic elegance of classification systems and removing its subjectivity”.

3 Cuttability Index for Surface Miners–a Proposal

A new rockmass classification system is simplistically developed considering the key influencing parameters, namely, point load strength index and volumetric joint count. Influence of rock abrasivity and direction of machine operation with respect to joint direction are also considered. Considering that a high-powered machine can cut a relatively stronger rock, the engine power of the cutting machine is also rated in this classification. The ratings of these parameters are tabulated in Table 8.

Table 8 Rating of the parameters of new rockmass cuttability classification
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Thus, the new cuttability index is the sum of the rating of above five parameters:

$$ {\text{CI }} = \, I_{\text{s}} + J_{\text{v}} + A_{\text{w}} + J_{\text{s}} + M $$
(12)

The objective of this model is to select a suitable surface miner for an application. So that the requirement of trial run can be avoided. Trial runs may be required for the difficult excavation category. The point load index is used instead of uniaxial compressive strength to reduce the testing difficulties. Abrasivity is an important rock property, which significantly affects the performance and pick cost considerably. Apart from this, an abrasive rock frequently stops the cutting operation for changing of the blunt picks. Volumetric joint count helps to incorporate probability of finding a weakness plane by the surface miner, which eventually decreases the rockmass strength. Similarly, the movement of surface miner with respect to plane of weakness is also important and thus incorporated here. Based on this new cuttability classification, the ease of excavation of rockmass using a surface miner can be classified as given in Table 9.

Table 9 Assessment of excavatability of surface miner based on cuttability index
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The proposed cuttability index is easy to derive and gives a first hand idea about the “GO–NO GO” criterion on applicability of surface miner. The main advantage of this cuttability index is that it considers the cutting power of the machine. With heavy machines of higher cutting power, surface miner could be applied for higher rock strengths also. While considering the economics of the application, abrasivity of the rock or in turn the wear to the point attack cutting tools, has significant influence. This aspect is also incorporated in this cuttability index.

Production rate of a surface miner can be estimated as follows:

$$ L^{*} = \, \left( {1 - {\frac{\text{CI}}{100}}} \right)kM_{\text{c}} $$
(13)

where,

L * = production or cutting performance (bcm/h)

M c = Rated capacity of the machine (bcm/h)

CI = cuttability index

k = a factor for taking into consideration the influence of specific cutting conditions and is a function of pick lacing (array), pick shape, atmospheric conditions etc. and varies from 0.5–1.0.

4 Case Studies

The above model was tested with 20 cases from limestone and coal deposits. Considering the variability to show the range of the model testing three cases are reproduced here–two in limestone and one in coal. The details of the investigations are given in Table 10. The coal mine is situated in eastern India. In this coal mine, the surface miner was used on an experimental basis. The limestone mines are situated in southern India. One of the limestone mines is having hard limestone. Both the limestone mines are sticky and bed moisture is also high.

Table 10 Performance analysis of surface miner for three mines
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The expected performance of different surface miners for a particular rock type can also be computed. For example, in a limestone mine (Pit-1) expected production is computed for a few surface miner models and is presented in Table 11.

Table 11 Expected performance of different surface miners
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5 Conclusions

A brief review of the existing cuttability indices has been carried out. A new index has been proposed considering three rock/rockmass parameters, a machine parameter and an application parameter, which are easy to determine. The new cuttability index should provide a handy tool for decision making on the applicability of surface miners. The extension of Gehring’s formula for prediction of production rate of surface miners is acceptable with reasonable accuracy. Determination of the new cuttability index is simple and gives reasonable accuracy.