Quantifying lithic microwear with load variation on ...

Surf. Topogr.: Metrol. Prop. 3 (2015) 034006

doi:10.1088/2051-672X/3/3/034006

PAPER

RECEIVED

19 June 2015 REVISED

4 August 2015

Quantifying lithic microwear with load variation on experimental basalt flakes using LSCM and area-scale fractal complexity (Asfc)

ACCEPTED FOR PUBLICATION

W James Stemp1, Mikhail Morozov2 and Alastair J M Key3

10 August 2015

1

PUBLISHED

4 September 2015

2 3

Department of Sociology, Anthropology and Criminology, Keene State College, Keene, NH, 03435-3400, USA Surface Metrology Lab, Mechanical Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609-2280, USA School of Anthropology and Conservation, University of Kent, Canterbury, Kent, CT2 7NR, UK

E-mail: [email protected] Keywords: working load, lithic microwear, LSCM, basalt, relative area, area-scale surface complexity, mean square ratio

Abstract Working load is one factor that affects wear on stone tools. Despite the recognition of the importance of the relationship between working load and the development of microwear on stone tools, there have been few attempts to quantify differences in wear due to changes in load. In a controlled experiment, we used 30 basalt flakes knapped from raw material collected in Olduvai Gorge, Tanzania, Africa, to cut oak branches for the same number of strokes. For each flake, a different loading level was applied starting at 150 g and increasing by increments of 150 g to a maximum load of 4.5 kg. A laser scanning confocal microscope was used to mathematically document the surface texture of the flakes. The worn surface data were compared using area-scale fractal complexity (Asfc), calculated from relative areas, to determine the degree to which variation in loading significantly affected the amount of wear on the flake surfaces. Our results indicate that working load does play a role in the development of lithic microwear on these flakes and that discrimination of two worn flake surfaces, using mean square ratios of Asfc, based on variable load is consistently possible with load differences between ∼100 g and 4.5 kg. However, discrimination of microwear on flake surfaces was not consistent for all load level differences and discrimination became less consistent when working load differences were below ∼100 g.

1. Introduction The first significant lithic microwear analysis method, as a means to understand and reconstruct stone tool function in the past, was introduced to archaeologists around the world with the publication of Prehistoric Technology by Sergei Semenov (1964). Decades after this pioneering work, many other methods and microscopic techniques were developed by archaeologists (e.g., Tringham et al 1974, Odell 1977, 1981, Keeley 1980, Kajiwara and Akoshima 1981, Serizawa et al 1982, Vaughan 1985, Grace 1989, Hurcombe 1992, Aoyama 1999), including those attempting to identify the wear produced on tools based on quantification of surface microstructure or texture using engineering technologies and principles (e.g., Dumont 1982, Grace et al 1985, Tomenchuk 1985, 1988, Beyries et al 1988). Although early attempts to quantify the wear produced on stone tools using metrology and tribology were not overly © 2015 IOP Publishing Ltd

successful, archaeologists did not abandon this approach. As superior measurement systems were developed and archaeologists gained a better understanding of the complex nature of wear formation on stone tools (e.g., Diamond 1979, Del Bene 1979, Kamminga 1979, Anderson 1980, Masson et al 1981, Meeks et al 1982, Unger-Hamilton 1984, Fullagar 1991, Christensen et al 1992, 1993, Yamada 1993, Hurcombe 1997, Mansur 1997, Christenson 1998, Šmit et al 1998, 1999, Ollé and Vergès 2008), there was renewed interest in quantification of wear using interferometry (Vargiolu et al 2003, Anderson et al 2006, Astruc et al 2011, Bofill et al 2013), laser profilometry (Stemp and Stemp 2001, 2003, Stemp et al 2009, 2010, Stemp 2014), atomic force microscopy (Kimball et al 1995, 1998, Faulks et al 2011), laser scanning confocal microscopy (Evans and Donahue 2008, Evans and Macdonald 2011, Stevens et al 2010, Stemp and Chung 2011, Stemp et al 2013, Evans 2014, Evans et al 2014, Key et al in press, Stemp

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et al in press), and focus variation microscopy (Evans and Macdonald 2011, Macdonald 2014). These technologies, borrowed from materials science, provide opportunities to study surface roughness or microtopography on scales ranging from micrometers to nanometers. Typically, archaeologists using optical microscopes to magnify stone tool surfaces have used the terms ‘microwear’ and ‘microtopography’ to broadly define surface features they have observed; but specific scales of observation vary widely depending on the microscope employed, ranging from low magnification stereomicroscopes to scanning electron microscopes (SEM). Because light limits observations to less than ∼0.2 μm, any surface documentation below that requires some kind of atomic probe technology. It is worth noting that ‘there is a huge dynamic range of what can be considered as surface roughness and it is up to the specific application with available standards to determine what is smooth and what is rough’ (Mattsson 1997, p 82). For example, for the purposes of his general discussion of surface roughness and microtopography measurement, Mattsson (1997, p 82) restricted variation in height (z) from ∼0.1 nm to ∼100 μm and lateral dimensions (x, y) to atomic distances to a few millimeters. In our experiment described below, maximum height (z) of a measured flake surface was less than ∼375 μm, although the peak-tovalley distance for most surfaces was between less than 200 μm to ∼100 μm, and x and y were restricted to 643×643 μm2. Despite better technology and more knowledge about wear formation processes (although not necessarily agreement), archaeologists must still contend with many factors that influence microwear development, microwear features, and surface roughness or microtopography, such as the raw material from which the tools were made, the shape of the tool’s working edge/surface, the type of tool action or motion, the type and condition of the worked materials, and the duration of tool use, among others (e.g., Tringham et al 1974, Greiser and Sheets 1979, Keeley 1980, Beyries 1982, Vaughan 1985, Bradley and Clayton 1987, Bamforth 1988, Lerner 2007, 2014, Lerner et al 2007, 2010, Evans et al 2014). One important variable that has historically not received as much attention is the force or working load applied to the stone tool by the tool-user during use. The research discussed in this paper focuses on the role that applied force or working load plays in wear formation and the ability to discriminate worn stone tool surfaces mathematically as loading conditions change and the differences in working load on two stone tools decrease. In this experiment, 30 stone flakes chipped from basalt retrieved from Olduvai Gorge, Tanzania, were used to cut English oak branches for a set period of time. For each tool, the working load was different, ranging from 150 g for the first tool to 4.5 kg for the last. The working load was increased by 150 g increments for 2

each tool used in the experiment. Each tool’s used surface was measured using a laser scanning confocal microscope (LSCM) and the surface measurement data were calculated using area-scale fractal complexity (Asfc), derived from relative area (Srel), and compared using mean square ratios (MSR).

2. Working load as a variable in lithic microwear analysis Within the archaeological literature, there is comparatively little on the relationship between working load and microwear (see Key 2013, Olausson 1980). One of the earliest discussions of the effects of variable pressure on stone tool surface modification can be found in Warren’s (1914, pp 441–442) experimental study of chipping and scratches on flints from Britain (although also see Evans 1872). Essentially, he concluded that more pressure produced chips with larger areas and deeper scratches; however, his standard ratios of pressure to chip area became unreliable when applied pressure was less than 10 kg. Later, Semenov (1950) addressed the issue of pressure as a factor in wear formation and Nero (1957) commented on downward pressure in relation to graver tip breakage during experimentation. In general, microwear analysts appear to have been aware of applied pressure as a factor in wear development, yet there was little discussion of pressure or load in lithic microwear studies. In the 1970s, a number of archaeologists (e.g., Tringham et al 1974, Broadbent and Knutsson 1975, Keeley and Newcomer 1977, Odell 1977, Brink 1978, Walker 1978) referenced their attempts to keep this variable constant during experimentation or mentioned the pressure applied to stone tools while completing a task. The consensus was that increased pressure or load increased the amount of edge damage or polish; however, most archaeologists referencing applied pressure or working load during tool use provided qualitative assessments of the effects on wear formation. UngerHamilton (1983, 1988) experienced mixed results while testing the effects of pressure on tool polish formation. Experiments cutting reed seemed to demonstrate no correlation between pressure and polish formation, whereas experiments in which materials were rubbed using different pressures produced different amounts of tool polish. Vaughan (1985, p 17) observed that ‘the amount of pressure exerted during tool use is a factor in the production especially of microflaking along the working edge’; consequently, he attempted to minimize the effect of variable pressure during his experiments by only applying as much force as needed to ‘accomplish the task in an efficient, nonexerting manner’. Using a mano to grind feed corn, Adams (1988, p 311) noted that the greatest amount of wear formed on the edges of the tool where the most pressure was applied. Adams (2014,

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pp 132–133) later elaborated on factors influencing the mechanisms of adhesive wear, abrasive wear, and fatigue wear on ground stone tools, including the alternating stresses of movement, pressure, and impact. Hurcombe (1992, p 9) made reference to the application of force as it pertained to the contact between particles and a stone tool surface during the process of abrasive wear. Her comments did not directly address the pressure or load applied by the tool-user, but she summarizes that light abrasion created a fairly smooth surface with a low material removal rate, whereas more forceful abrasion will remove surface material more quickly. Shea (1992, p 149) enumerated the many factors that affected wear formation on stone tools and included ‘the rate at which the edge is loaded’. In terms of variation in raw material microstructure and the rate and mechanics of wear formation, specifically the homo/heterogeneity of stone tool surfaces, Ollé and Vergès (2008) observed that irregular surfaces experienced differential pressures at different points of contact such that initial wear was primarily the product of microflaking and abrasion of prominent points (brittle fracture phenomena) until the surface was more homogeneous, which is when the dominant wear mechanism became plastic deformation. They argued that tool surfaces that were already relatively even in terms of microtopography would not experience the first phase of wear involving microfracture and abrasion. An emphasis on load as a quantifiable variable in modern archaeological use-wear studies was first demonstrated in Walker and Long’s (1977) production of cut marks on bone using metal and stone tools. They noted that tools used with different pressures affected the depth of cutting grooves on bone in crosssection, but that other variables, including tool type and cutting edge length, rendered precise correlations impossible (also see Bello et al 2009). Walker and Long (1977, p 611) also commented on the efficiency of different tools in relation to maximum load levels, a relationship noted in a number of other functional experiments (e.g., Jones 1980, Jobson 1986, Dewbury and Russell 2007, Key and Lycett 2014a). Although Lawn and Marshall (1979) provided valuable information concerning the role of applied load in relation to indentation and microfracture in brittle solids (e.g., glass), their work did not focus on working load and microwear development. The quantification of wear, specifically edge chipping, in relation to amount of applied force was attempted by Tomenchuk (1988; see 1985 for parametric use-wear analysis) who documented the size of the edge damage flakes as a product of the rate of loading or the amount of force applied to the implement to complete its task. More recently, the demonstration of a quantifiable relationship between working load and wear formation has become a topic of interest to two archaeologists—Harry Lerner and Alastair Key. Lerner et al’s (2007) work focused on the effects of pressure in 3

relation to variable raw material hardness. In these experiments a diamond-tipped triboindenter was used on three different cherts and silicified wood with particular loading rates. Flakes made from all four raw material types were also used in dry hide-scraping experiments to document different wear invasiveness in relation to variation in raw material hardness and surface microtopography. Other work by Lerner (2007, 2014) focused on the same raw materials used in series of scraping and planing experiments to document wear development and invasiveness using SEM, GIS and image analysis, but change in applied pressure was not a variable under study. Key (2013) investigated how variable loads applied by the tool-user affected wear development on tools made from the same stone type when all other variables were held constant; however, loading variation and duration of the cutting activity (700 strokes) undertaken in this experimental analysis was limited. As such, Key was unable to demonstrate a significant relationship between loading variation and microwear development. As a result, he undertook another series of experiments (Key et al 2014) using basalt flakes used for a longer time period (1800 reciprocal strokes) with a greater variation in working loads (150 g–4.5 kg). The worn surface structures of the used tools were quantified using a LSCM and relative area (Srel). Based on the experimental results, it was determined that a minimum load difference of roughly 750 g was necessary for successful discrimination of the worn surfaces on any two tools at fine scales. The ability to reliably discriminate the tools’ surfaces using Srel decreased with load differences between 250 and 750 g, with discrimination at fine scales between 200 and 400 g varying with total amount of load (see below). In the past, some archaeologists argued that because the tools in an experiment were all used by the same person who attempted to keep the working loads the same then ‘(t)he role of applied load as a variable K (could) thus be considered to be negligible’ (Le Moine 1994, p 322 for bone an antler tools). However, work by Key et al (2014) demonstrates that despite the best efforts of a single tool-user, it is difficult to maintain a consistent pressure or working load in experiments. Moreover, even if the tool-user consciously attempts to control the working load exerted on a tool, Ackerly (1978) argued that differential loading rates necessary for initiating edge fracture varied with the tool edge width in contact with a worked item as is demonstrated by the difference between cutting and scraping motions.

3. Experimental method: tools and techniques The flakes used in this experiment were produced using hard-hammer percussion on basalt collected from eroded deposits in Olduvai Gorge, Tanzania, to

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Table 1. Descriptive statistics for the final 28 basalt flakes used in the experiment.

Mean (n=28) SD (n=28) Range (n=28)

Length (mm)

Width (mm)

Thickness (mm)

Mass (g)

Edge angle (°)

47.7 13.2 44.2

48.9 14.4 55.0

13.4 5.1 25.7

43.9 33.5 116.7

36.3 11.5 37.4

Figure 1. Annotated figure of the multi-platform structure used to record working load. Flakes were used with a cutting stroke across the width of the branch and parallel to the length of the ‘top board’. The tool user was seated in front of the structure (which was placed upon a table) in all instances so that the position and angle of tool application was replicable between flakes. It should be noted that the higher ranges of loading required considerable effort on the part of the tool user and flakes used at higher working loads were consequently used over multiple sessions.

replicate the lithic technology of the Lower Palaeolithic of Africa. Thirty flakes were chosen from an original group of 45 based on homogeneity of material, the straightness of cutting edges, and the absence of non-use-related damage features along their edges (see Key et al 2014). The original group of 45 flakes was restricted to those that met certain metric criteria, specifically greater than ∼30 mm in maximum length and width, a cutting edge length greater than 20 mm, and a cutting edge angle greater than ∼60°. A summary of metric data for the 30 flakes selected is provided in table 1. The method of data collection for tool length, width, thickness, mass, and edge angle can also be found in Key and Lycett (2014b). Each of the 30 basalt flakes was used by a single tool-user—Alistair Key—to complete the same task. Handedness, flake position, flake orientation, and cutting angle (90o to the surface of the oak branch) were all held constant. The only changing variable was the amount of applied force (working load) applied by the tool-user for each flake. Each flake was used to cut through fresh English oak (Quercus robur) branches, measuring ∼25–30 mm in diameter, for exactly 1800 reciprocal strokes. Each stroke (see, Shumaker et al 2011) ranged between ∼50 and 150 mm in length, depending on the length of a tool’s cutting edge. Flakes with longer cutting edges required a greater draw length to ensure that the majority of the edge contacted the branch. Consequently, the amount of contact on any location in the used area along the cutting surfaces of each flake, regardless of cutting edge length, was the same. Flakes were used for 1800 cutting strokes because this was the minimum number required to produce microscopically visible wear 4

under the lowest working load (150 g) in this experiment. Some minor variation in task completion was expected given how even a single individual uses a tool. Despite some variation, the human use of the flakes in an experiment in the present most closely approximates the process of tool use as it would likely have occurred in the past. To determine the influence that different loading levels would have upon microwear development, each of the 30 flakes was used with a variable working load. In this experiment, the range of working load began at 150 g and extended to a maximum of 4.5 kg. Each flake was given a target load from set intervals of 150 g (i.e. 150 g, 300 g, 450 g, 600 g, 750 g, etc). While the load applied during the use of any flake tools is highly dependent upon the size of the flake and is likely to be task specific (Jones 1980, Jobson 1986, Key and Lycett 2014a), the effective ranges identified by Key and Lycett (2014a) were used in this experiment as a proxy for those achievable with a simple flake cutting tool. The working load was recorded using a pressure sensitive pad (Tekscan Economical Load and Force System (ELF™)) attached to a multi-platform cutting structure. The segments of oak branches were secured to the cutting platform with cable ties. This cutting platform was then located above another platform upon which the pressure pad was fixed. There were only two contact points between the upper cutting platform and lower board with the pressure pad. One contact point was located directly beneath the oak branch and, through a rubber stud, was fixed directly upon the pressure pad and subsequently allowed any forces applied to the branch through the tool to be disseminated onto the pad below. The second point was

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Figure 2. Example of pressure pad visual display data from the Tekscan Economical Load and Force System [ELF™].

located at the opposite end of the cutting board and was attached with hinges, thus allowing the upper board to move freely on and off the pressure pad (figure 1). The target load for each flake over the course of its use could be monitored through a visual display of applied force provided by the pressure pad’s software (figure 2). Although the target load for each flake was adhered to as closely as possible by the tool user, it was not possible to maintain the exact applied pressure for all 1800 reciprocal cutting strokes. As such, the mean loading values for each flake are not precisely 150 g apart. The loading value for each basalt flake, as well as its target load and standard deviation, is presented in table 2.

4. Surface measurement using LSCM Prior to measuring the worn surfaces of the flakes using a LSCM, each one was washed in a warm water/ grit-free detergent solution and then rinsed. The flakes were then soaked in a 15% solution of HCl for 15 min before being rinsed in warm water. Following the acid bath, the flakes were soaked in a 15% solution of NaOH for 15 min and then rinsed again in warm water (see Keeley 1980). After the flakes had air-dried, their worn surfaces were examined under a metallurgical microscope (Unitron MS-2BD) at 200x to ascertain that there were no residues or particles still adhering to them. In this experiment, the Olympus LEXT OLS4000 LSCM was used to mathematically document the surface microtopography of the basalt flakes. Advantages of this measurement system are that it is equipped with a dual head such that it can also be used much like a light-reflecting metallographic microscope and has the ability to produce both two-dimensional and 5

Table 2. The target load, mean actual load, and load SD values for each flake. * Indicates two flakes lost ‘post-use’. + Specifies five problematic flakes that have not thus far been able to be accurately scanned.

Flake

Target load (g)

Mean actualload (g)

Load SD values (g)

9+ 5 23+ 11+ 28 20 4 14 15 19 27 18 12 26 17 2 6 21 1 30 10+ 3 24 7+ 22 13 8 25* 16 29*

150 300 450 600 750 900 1050 1200 1350 1500 1650 1800 1950 2100 2250 2400 2550 2700 2850 3000 3150 3300 3450 3600 3750 3900 4050 4200 4350 4500

169 230 287 420 436 697 867 901 958 1353 1412 1452 1767 2056 2226 2532 2711 2870 2903 2911 3130 3209 3318 3407 3645 3621 4144 4221 4494 4450

18.3 30.0 63.6 76.5 52.1 50.8 83.5 113.4 165.0 89.9 214.4 135.2 321.2 168.4 117.9 221.8 142.7 97.4 385.4 219.9 236.8 200.2 286.1 250.6 267.0 525.3 132.5 393.8 246.5 186.9

three-dimensional images (figure 3) of the measured surface, which allows for visual assessment of the surface in addition to the quantification of surface

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Figure 3. Three-dimensional image from the LSCM of area scan #1 of the used region of flake B24 with the 20x objective. The x- and y-axis dimensions are 643×643 μm2. Although the entire surface in the area scan is worn through contact with the oak branch, the regions of higher microtopography (red and orange/yellow) possess significant abrasion and striations.

microtopography or roughness. Below is a general description of the operating principle of the LSCM; a more detailed explanation can be found in Sheppard and Shotton (1997; also see Evans and Donahue 2008, Stemp and Chung 2011, Stemp et al 2013). The LSCM creates images using reflected laser light from a discrete focal plane. The laser light reflected back from the measured surface (i.e., focal plane) through a pinhole aperture located in front of a photomultiplier records topographical structure. The recording of elevation on the sample is based on both the diameter of the pinhole aperture and the wavelength of the incident light reflected back from the measured surface to determine the depth of each focal slice. The LSCM used in this experiment produces the incident laser light that is scanned across the measured surface using a microelectromechanical resonant galvano mirror. The focal slices of the measured surface are produced by the objective lens on a motorized head which focuses the laser light onto surface points of variable vertical distance. The focal slices are used to mathematically create a three-dimensional digitized map of the scanned surface. The number of focal slices the laser takes of the measured surface varies depending on the peak-to-valley distance on the surface, the objective used, and the pitch setting. The LEXT OLS4000 LSCM used in this experiment produces a 405 nm laser. It has a vertical scale (z-axis) resolution of 0.8 nm and a height display resolution of 1.0 nm. It includes a range of objectives from 5x to 100x, of which the 20x objective [0.60 NA] was used. The pitch was set at 0.06 (‘fine’ pitch). 4.1. Multi-scalar calculation of surface roughness: Asfc Fractal or multi-scalar analyses have been used by archaeologists and anthropologists in a number of different ways. They have been applied to different types of settlement, landscape, and resource usage (e.g., Brown and Witschey 2003, Brown et al 2005, 6

2007), economic models (e.g., Romero et al 2010, Brown et al 2012), stone tool reduction (e.g., Brown 2001, Brown et al 2005), lithic and bone tool microwear analysis (e.g., Rees et al 1991, Stemp and Stemp 2001, 2003, Evans and Donahue 2008, Stemp et al 2010, 2013, Stevens et al 2010, Stemp and Chung 2011, Lesnik 2011, Stemp 2014, Watson et al in press), and, most notably, tooth microwear analysis to primarily reconstruct diet (e.g., Ungar et al 2003, 2007, 2010, Ungar 2004, Scott et al 2005, 2006, Merceron et al 2006, 2010, Schulz et al 2010, El Zaatari 2010, El Zaatari et al 2011, Krueger and Ungar 2012, DeSantis et al 2013, Krueger 2014, Teaford and El Zaatari 2014). In a previous analysis of the worn surfaces of the basalt flakes discussed in this paper, Key et al (in press) discriminated surface roughness using the relative area (Srel) algorithm (ASME B46.1 2009, ISO 25178-2 2012). Srel is the ratio of the calculated area (CA) of the measured surface divided by the nominal area (NA) of that surface

Srel (s ) =

CA (s ) . NA (s )

This algorithm calculates the change in apparent, or calculated, area based on the scales of observation, or calculation, by a series of virtual tilings. The CA(s) is the number of tiles used at a particular scale, s, multiplied by the area of the tiles used in that tiling exercise (i.e., that particular area scale, s). The NA(s) is the projected, or nominal, area that is tiled in that particular scale (s). Srel is related to the inclinations of the tilings as a weighted average of the inverse of the cosine the tile makes with the nominal horizontal, or datum (see Brown 2013). Srel (s ) =

N

1

å cos q i=1

i

si . S

The slope of the log–log plot of relative area (Srel) versus scale is a measure of the complexity of the surface (see below), with greater negative slopes

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corresponding to greater complexities. The slope can also be used to calculate the fractal dimension (D), where D=2–2(slope) (Siegmann and Brown 1999); however, the fractal dimension is not used in the relative area method. For the current study, we analyzed the surface roughness data generated by the LSCM using Asfc (ASME B46.1 2009; ISO 25178-2 2012). Asfc is calculated from a series of relative area (Srel) measurements at different scales. It is equal to −1000 times the slope of a log–log plot of relative area (Srel) versus the scale of observation

Asfc = -1000 (slope) . The higher the Asfc values, the greater the complexity of the documented surface area as the scale of measurement changes (see Brown 2013, Leach 2014). Asfc is also related to the fractal dimension, where Asfc=1000(D–2). 4.2. Mean square ratio The area-scale fractal complexities calculated for the worn basalt flake surfaces were compared statistically at each scale using MSR to determine confidence levels for the discrimination of two flake surfaces based on the standard deviations of their data sets. The variance demonstrated by these data is the sum of the variances of each of the independent sources. MSR compares the variation within each data set to the variations between the data sets to test the significance of each of the variations. MSRs of Asfc were used to determine whether the observed variations were statistically significant at a 95% confidence level. Scale ranges at which MSRs were above this confidence level demonstrate the ability to discriminate the surfaces’ complexities.

5. Surface measurement of the basalt flakes using LSCM Seven different area scans measuring 643×643 μm2 were taken on the worn surface of each of the basalt flakes with the LSCM. The worn surface (dorsal or ventral) on each flake that was more level tended to be chosen for measurement due to restrictions associated with the working distance of the 20x objective lens on the microscope. Very few flakes possessed a surface whose potential for accurate measurement using the LSCM exceeded the 1 mm working distance of this lens. In cases in which one surface could not be accurately measured in terms of working distance, the other side was chosen. Bifacial wear development on each of these wood-cutting tools was generally equal. The need for comparable surface data for all flakes meant that the working distance restrictions on some flakes necessitated the use of the 20x objective lens. The 50x or 100x objective lenses could not be used due to 7

Table 3. The five basalt flakes with missing surface area measurement data over a mean of 1% using the LSCM. Flake

Measurement

% Area removed (filtered out)

B7 B7 B7 B7 B7 B7 B7 B9 B9 B9 B9 B9 B9 B9 B10 B10 B10 B10 B10 B10 B10 B11 B11 B11 B11 B11 B11 B11 B23 B23 B23 B23 B23 B23 B23

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

4.9 0.73 0.97 1.04 0.7 0.93 0.62 1.94 1.42 3.95 5.41 2.74 4.29 3.19 3.48 5.78 6.53 6.25 4.45 3.29 4.21 2.99 3.07 3.75 2.69 3.43 3.42 3.41 0.75 0.8 0.7 0.68 0.73 6.26 5.15

the 0.35 mm working distances of these lenses. Some edges on the rough surfaces of the basalt flakes could not be easily measured because the distance between the lowest and highest microtopography exceeded the 0.35 mm threshold for the 50x and 100x lenses. In order to quantify changes in surface roughness, each of the scan locations measured in the worn area of each basalt flake was first calculated in terms of Srel on a log scale and then Asfc was calculated from the slope of Srel (see above). For these data, Sfrax was used to process the data. Asfcs were statistically compared using MSR to determine whether discrimination of the roughness of the worn surfaces of the basalt flakes was possible and the scale at which significant differences in surface roughness based on the different working loads applied to each flake occurred. The scales at which the MSR data were above the 95% confidence level were those at which the two worn tool surfaces used with different working loads could be discriminated based on Asfc.

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Figure 4. The mean relative area (Srel) of 2 flakes—B12 (x) and B17 (Δ)—at decreasing measurement scales (from right to left). Srel increases as scale of measurement decreases. The slope for flake B12 is −0.314 and for flake B17 is −0.147. Asfc is calculated from the slope of Srel for each flake and is related to the fractal dimension (D). D for flake B12 is 2.628 and for flake B17 is 2.294. The smoothrough cross-over (SRC) occurs at ∼103 μm2. At scales above the SRC the Srel for the two flakes is close to 1; at scales below the SRC the Srels are much higher than 1.

Figure 5. The 7 relative area (Srel) curves calculated from the surface area scans of flake B5 at decreasing measurement scales (from right to left). The Srel were used to determine area-scale fractal complexity to calculate the mean square ratios (MSR) presented in figure 7.

6. Results Two of the basalt flakes used in the cutting experiment were lost in transit from England to the United States, so the sample available for measurement using the LSCM was reduced to 28 flakes. As these flakes were being measured with the LSCM, it was noted that the roughness of the natural unmodified surface 8

microtopography of basalt presented some challenges in terms of surface documentation. Sharp contrasts or changes in surface elevations resulted in the inability of the laser to accurately capture some data points, and therefore portions of some of the basalt flakes’ surfaces in some measurements could not be documented. Prior to calculating Srel, the surface scan data were processed through a modal filter developed at

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Figure 6. The 7 relative area (Srel) curves calculated from the surface area scans of flakes B8 at decreasing measurement scales (from right to left). The Srel were used to determine area-scale fractal complexity to calculate the mean square ratios (MSR) presented in figure 7.

Figure 7. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B5 (230 g load) and B8 (4144 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 3419 g.

Worcester Polytechnic Institute to compensate for the absence of missing surface data points. Five flakes (B7, B9, B10, B11, B23), that had at least one of the seven individual surface measurements that was missing more than 1% of the total surface area scanned, were omitted from the study. The problematic flakes were, in fact, scanned twice in the hope that a second set of measurements would provide better documentation of their worn surfaces; however, poor results for these 9

flakes were generated again (table 3). As a result, the number of flakes included in this study was reduced from 28 to 23. The calculation of Srel and Asfc for the 23 basalt flakes with different working loads, as represented by the graph of flakes B12 and B17 (figure 4), demonstrates the expected behavior of increasing mean Srel as the scale of measurement decreases for both flakes. The graph also shows the ‘smooth-rough cross-over’

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Figure 8. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B5 (230 g load) and B17 (2226 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 1996 g.

Figure 9. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B8 (4144 g load) and B16 (4494 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 350 g.

or SRC—the scale of the relative area becomes significantly greater than 1 (ISO 25178-2 2012)—for the two flakes, which is the point in the scale of observation/documentation above which the surface of the natural raw material (that can be characterized by Euclidean geometry) appears ‘smooth’ and below which the modified surface structure associated with tool wear (that is described by a complexity measure) is ‘rough’. For the basalt flakes, SRC begins around 10

103 μm2 with the deviation of the mean Srels from one another (ISO 25178-2 2012). As scale of measurement decreases the mean Srel of each flake increases as does the difference between them. The different slopes for the Srels for each flake generate different Asfcs for the two flakes and different fractal dimensions (D). B12 has a greater negative slope than B17, permitting mathematical discrimination of the two flakes’ surface roughness, and also demonstrating that greater

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Figure 10. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B8 (4144 g load) and B13 (3621 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 523 g.

Figure 11. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B2 (2532 g load) and B12 (1767 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 765 g.

negative slopes indicate greater complexity and are related to higher fractal dimensions. Graphing the results of the seven 643×643 μm2 area scans on a log–log scale, as demonstrated for flakes B5 (figure 5) and B8 (figure 6) used in the MSR calculation for figure 7, illustrates both the increases in Srel with decreasing scale of measurement for all scans for each flake and the range of Srel at each scale. Comparisons of the Asfc, based on the mean Srel, of the 11

flakes’ worn surfaces using MSR provided the ability to discriminate flakes with significant differences in working load (figure 7) at fine scales (between 10 μm2 and 10−1 μm2), as well as those with mid-range (figure 8) and relatively small (figure 9) differences in working load. Regardless of whether the specific working loads applied to the flakes was higher (figure 10), mid-range (figure 11) or lower (figure 12), discrimination of the worn surfaces of the flakes at fine scales

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Figure 12. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B5 (230 g load) and B28 (436 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 206 g.

Figure 13. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B3 (3209 g load) and B24 (3318 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 109 g.

based on the MSR of Asfc was possible. In fact, the worn surfaces of flakes with small and very small differences in working load could be discriminated at fine scales (figures 13, 14, and 15). Overall, two worn flakes’ surfaces could be discriminated at fine scales above the 95% confidence level based on MSR of Asfc a majority (85.7%) of the time, regardless of the each flake’s working load or the difference in the working loads between the two flakes. Although the inability to 12

discriminate two flakes’ surfaces at fine scales did occur and there is no distributional pattern associated with flakes with higher working loads versus those with lower working loads (figures 16 and 17), there is a working load difference threshold associated with these anomalies around ∼100 g, below which discrimination of two worn flakes’ surfaces at fine scales appears less likely (figure 18). Above the ∼100 g working load difference threshold, worn flakes’ surfaces

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Figure 14. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B4 (867 g load) and B15 (958 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 91 g.

Figure 15. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B13 (3621 g load) and B22 (3645 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 24 g.

could be discriminated at fine scales based on MSR of Afsc for 87.5% of the two-flake comparisons. Below this threshold, similar discrimination based on twoflake comparisons was possible 75% of the time. Despite the difference in these percentages, there is no statistically significant difference (z=−0.6262; p=0.264 35) for surface wear discrimination between the sub-assemblage of flakes with working load differences above ∼100 g versus the sub13

assemblage of flakes with working load differences below this threshold. It may be that a larger sample of experimental flakes used with different working loads might produce a statistically significant difference in terms of the sub-assemblages above and below the ∼100 g threshold. The inability to discriminate two worn flakes’ surfaces at fine scales based on the MSR of Asfc appears to be somewhat randomly distributed based on these experimental results.

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Figure 16. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B8 (4144 g load) and B17 (2226 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 1918 g.

Figure 17. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B15 (958 g load) and B18 (1452 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 494 g.

7. Discussion Based on the results of this experiment using 23 basalt flakes to cut English oak branches, the Asfc algorithm provides reliable data for the discrimination at fine scales (beginning around 102 μm2) of worn surface roughness based on variable working loads. The lack of an obvious documented threshold level below which discrimination of surface roughness is not 14

consistently demonstrated seems to suggest that Asfc can be used to distinguish the worn surfaces on the basalt flakes when working load differences are high (1000s of g), as well as low (10s of g). However, reasons for the inability to discriminate some worn flakes’ surfaces at fine scales based on MSR of Asfc are not currently understood. The difficulty in determining why some discriminations were not possible is complicated by the fact that all of the flakes involved in

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Figure 18. Mean square ratios (MSR) of area-scale fractal complexity (Asfc) for basalt flakes B14 (901 g load) and B15 (958 g load). Discrimination of Asfc for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 57 g.

Figure 19. Mean square ratios (MSR) of mean relative area (Srel) for basalt flakes B8 (4144 g load) and B16 (4944 g load). Discrimination of mean Srel for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 350 g.

two-flake comparisons using MSR whose surfaces could not be discriminated at fine scales (e.g., B8–B17; see figure 16) were all also involved in comparisons using MSR in which discrimination at fine scales was possible (e.g., B5–B8 and B5–B17; see figures 7 and 8). As such, it does not appear that the wear development itself or the documentation of the surface roughness in worn areas using LSCM are the sources of difficulty in the discrimination of the worn flakes’ surfaces when 15

working loads differ. It is believed that the variable roughness of the natural surfaces of the basalt flakes used in the experiments likely plays a role (see below). If the ability to discriminate the worn surfaces of flakes using MSR of mean Srel (Key et al in press) versus those using MSR of Asfc is considered, there are some interesting results. In a previous experiment using the same surface data acquired from the 23 basalt flakes using the LCSM, Key et al (in press)

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Figure 20. Mean square ratios (MSR) of mean relative area (Srel) for basalt flakes B5 (230 g load) and B28 (436 g load). Discrimination of mean Srel for the two flake surfaces is possible at scales above the 95% confidence level as represented by the solid horizontal line. Load difference between the flakes is 206 g.

demonstrated that the flakes with differences in working load above ∼750 g could be reliably and consistently discriminated at fine scales using the MSR of mean Srel; however, this became less consistent when loading level differences dropped below ∼750 g. They also determined that the minimal working load difference required for the discrimination of worn surfaces on two basalt flakes using MSRs of mean Srel at fine scales was generally between 250 and 750 g. Moreover, there appeared to be variation in the discrimination at fine scales of small working load differences dependent upon the overall loading levels of the flakes. Small load differences (200–400 g) between flakes with low working loads (4000 g) were difficult to discriminate at fine scales based on MSR of mean Srel, but small load differences (200–400 g) between flakes with medium range working loads (about 1500–3000 g) could be discriminated at fine scales (Key et al in press). Since the threshold for discrimination at fine scales of the worn surfaces of any two of 23 flakes with different working loads is ∼750 g based on MSR of mean Srel, whereas similar discrimination using MSR of Asfc for the same flakes is more consistently reliable above ∼100 g, it appears that Asfc may be better for documenting and discriminating differently worn surfaces, as a product of the variable working load applied to basalt flakes, than mean Srel. This is because Asfc relies on the slope of the mean Srel calculated at the fine scales (