The experiment involved a visual and tactile preference examination of the seven toolpath strategies for each of the four designs, examining how this may affect the perceptual experience of these objects. The experimentation was grounded within the philosophy and approaches of Kansei Engineering and HCD whereby experiential preferences are established based on reviewing design variations against known perceptual effects (Lévy, 2013). The stage was split into two key stages, one visual and one tactile, corresponding to two fundamental senses, sight and touch. Based on the comparison tests that are widely used in optometry, these tests used the datum plates mentioned in the previous paragraph against the “comparison plates” (the remaining six plates) as a means of establishing if the differences in toolpath strategy related to any preferences in the visual or tactile domains. If some preferences could clearly be established, this has implications for machining protocol and manufacturing ontologies more generally.
Experimental setup
Visual and tactile preferences were the focus of this experimental stage where the six design variations were each compare against a datum. Each set of six comparison plates were presented to the participants in a randomized order to minimise automatic responses. Each participant was presented with a worksheet that contained three response options for each test, for the visual preference phase; “visually prefer”, “visually don’t prefer”, “no visual preference”. Each of these responses could be suffixed with “when compared to the datum plate” to establish the direction of the preferences. This was made sufficiently clear by both the researcher and the provided information sheets. For the tactile preference test that followed, the terminology was aligned around tactile comfort; “more comfortable”, “less comfortable”, “no preference” (which again could be suffixed with “when compared to the datum plate”). The plates were presented one by one in a randomized order and arranged next to the datum plate for visual comparison. All the plates included, each participant was required to assess 28 plates in total in both the visual and tactile domains equalling and total of 56 preference comparisons. Tactile comparison involved a short, guided interaction with the two plates, first the datum then the comparison using the participants index and middle fingers. While a maximum of two minutes was allotted to each task, most people were able to decide on their preferences in 10 or 20 seconds, making the experiment very quick overall.
Machining strategies for visual preferences
Considering the visual preference results first, Table 1 shows the comparison plate preference results against the distinct designs and toolpaths with the datum plate preference results. These results exclude “no preference” choices, the colour coding indicating the relative differences in the values (green-high, red-low, white-mid);
Table 1 – Participant preferences for visual comparison of plates
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Kip7FvdkJKEqUJz9ZVjeiYOaIqJE30jZwcK1us7kh6n9onk32l37ES1MdtySm0mQwoXeZI3WN/HGPiEpxeDkoWnj5+WJ+xC2uS56EwPwfA5b5nvWxN8jwE68J5LK8A+2bnDh6vw/SIANEXpbl/EdHTUHuyAtMjAgAASSnr+CXRBfsRhL7S7zzMZrNZ7iD6qqovL8odAhERkaqF/PpGl8s4HEZERESqxCSIiIiIVIlJEBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxF+MvoY8PDzkDoGIiEjVOkpz+LfDrqGEspVyh6BYG0Ynyx2CYhWceUfuEPq0hd4Pyh2CYv2/8nK5Q1As88c1cofQJ3E4jIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSpV5MgDw8Pm1tv7E+v11/z/bi7A3MzkRO2ETlhG2EoO22zTGrPCduIv7Z9L1OE7mvQL28RtxVLUkT7h1UnbJZR5w4+txtLAx/F0sBHcfC53TbLNk5OEcs+KT4hU4TKNPTekdj3VqG4bzxrwvW3DhA3cm1Y4gLs15eL+xWfn8YNDz0obtSxyqZG9Ht2Hfo9uw7DX94udzg90mtJ0JAhQ7Bp0yaYzWaYzWZs2rTpmidCZrMZOp3umu7D3VVuLcbQB0YgoWwlHtj2CN7b8JZIdo4tz8ewB4OQULYSoxaEoThlr8zRupe4qbHIyn0NbZfOo+3SeezO3oXDBy0nmymR0ThxqgZtl87j0fnzEDc1VuZo3dsnxSdwvvkbbKndjS21u1Gxv0wkOwef242QaROwpXY3nnh9JfasyML55m9kjlgZht47EgaT0aYtaloMHvvfefjp2wvIey0HQ+8dKVN07m1Y4gIYzp2zaQt7ZgW+eHUnfnzrbTwW9Vs8sD5VpuiUISz3deyJmYYfVq+F/803Y9Efi+QOqdt6JQkyGAxoaGjA9OnTRdtTTz0FACgoKIBer4eHhwcMBoNoGzJkCAAgKSkJUVFR8PDwQFJSkrgNGTIEHh4eiIqKEtuU7ksVIOtKkNQubcc6tt6sTvW20CVRuGdWCADgjrt9AAB///avAICLLRcw8B5fy7K7vHGx9YI8Qbqp/MMHMHV6jLg/ISIMLU3NAIC2S+cxWOMHABijC4Gpweh0G2QxImoMHstcJu7fGXo3Lpw7DwCY/syjCIuPBgBoR/waAND+DftiR6Rqz/qVqxzaDSYjnlz4BABg5kOW/ltxgtU1ifGrc7jhoQexbs7DDst+fOttaO4YCADQ3fUbNLS19nZ4imG8YHmPjvL0AgBM8NPA+L3y3re9kgRptVpMmjQJ/v7+KCgoEO1msxmzZs3q9PHHjh2D2WxGZmYmACArKwu5ubkwm804c+YMNm/ebLMv+wqQtE+pCvXuu++K5Mjf3x/5+fkwm81ITEy0Sar6mq9ONQG4nAx53avFuU8bLcs+a8aNniydd8TUYISXj7dD+wl9Fe4Lv1+GiJTri8pT8Av4lUO74ZMvAVxOhsg5zWA//PTtBZHkdKaZJ3NBc8dA/PjW25ihG9fhevrP/oLIwBG9FJXyaAYMgHbAAJxsbQEAvG8yQnOT8s4hvTYcVlxcjPz8fMTFxYmqi3VC1JFJkyY53JeSnAULFqCsrEwsGz9+vNNtnDlzxub/Op0Oer0e/v7+IhFbtmwZjh07JipSfcmx5fl4Z/EeDHswSLSFLrEkfDlhG9H2iQmxeUmuHq560nwg68rQWaMJg355C3Zn70LMzGlyhaYoZblFWBr4KG71ud0h0dmRlI6X/ncjxs4Ikyk65dMM9oPWT4NDbx8BAOx7q9BhuIw6JlWKdhT/CTPH3yd3OG7t9MJFSH2vDP2eXYcJfhps/1203CF1W69OjJ41a5aoxpSXlyMuLu6KJy77+vraJDiu9rtgwQKH4bDm5mY0NDSIdn9//yuKxZ1N2hSHhLKV+GvbBVRuLQZgSX4G3uOLhLKVCJyr4+RoF17Zsh27s3eh6tRHNu2DNX5ou3QeJ07VYEpkND6s4pBDZ8Lio7Gl1jIHaOPkFJtlj2Uuw5ba3Tjf/I3DxGnquuJDhXh6XSquv3UAyisroPXTyB2SokiVoi9e3YmwZ1ag4vPTnT9IhaRJ0UVzHsYPq9cCgCInR/dKElRQUOAwzKTT6eDv74/m5mZ4enr2eNuNjY1i/lBHnnrqKYfhMG9vb/j7+4t26abVanscj7sbNMIPf227IJKd24ZZjv0dd/vgRs8B+OZzls2trViSgrxde9B26bzLdQZr/OCn0aC1hceuq/wCfoVvm752uuxXo4dxYvQVkIbKfvr2Al5OexEGkxGjR3JydHdp7hgI7cCBaDnv+r2vZs0XL0I7YAA0AyxDYDHDhsNw4YKYK6QUvZIEzZo1C8eOHbMZ/iooKEBDQwNGjx4t2lpbLSeR3NzcDrdnPWS1c+dOhIV1XD7fvHmzzWRoiU6nQ0NDg4iroKCgz02OPrY8H58WVIn7J3eWYdAIP/zPoJsAQCQ9X51qwsXWCyIpIksF6KzJ5FABkobBzhpNAIDDBwthMhoxIognGlfKcouwIyld3C957W3cGXo3AMswWFnu5atKjm7dj1+NHtbrMfYV1986QEyEXpjyJCaGhUMz2E/mqNyfNAxm/Mpyxdh+fTkM585h1K8c564R4H3jjTZJT+Hnp22SIqW4rrd2ZDab4eHhgbi4ONHW0NAgqi6bNm3CuHGWiWqJiYkdDnFJk6yldaUrzVx56qmnxNVk0r6kOUUNDQ3w9/cXcTU0NPTwGbqnSZvicGBuJk7utMybGvZgkLhabMabj2P/nFfw3oa3AAD3r3pIJEcEbFi9DgBsfgfo0fnz8MLWNBwpKcKYuy/PrzpSUiSuFiNHYfHR+K71WywNfBQAcKvP7Vh5NA2AZRhs4+QUHN26HwAwdkaYuFqMuu/9d/6ECQ/8FgCg9dOg/qOPZY5IGTR3DETZcy/gzj8sEG1lz70grhYjW6E+vtgTMw3DrIbAPl+4SMaIesbDbDab5Q6iO5KSkmAwGFBcXCx3KJ2a/96qzlcipzaMTpY7BMUqOPOO3CH0aQu9+SN6PfX/yss7X4mcMn9cI3cIinVDB7/3xD+bQURERKrUa8NhV4v0W0FEREREV0JxSZCSvDJundwhKNaFH9vlDkGxHv/aS+4Q+rQmv7/LHYJisWdegW+/lTuCPonDYURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKikiC9Ho9PDw85A5Dsfpdf524LVq40GF5ZUUF+l1/nQyRub9Bv7xF3FYsSRHtH1adsFlGXffES9vwxEvbnC5L378Pd8Y/0ssRKZP2Ji9xW/3k007XmTAiFDu2ZfZyZMoxLHEB9uvLxf2Kz0/jhoceFDfqXL+sV8Stsq1N7nC6TRFJEPXc5OjfYU/em/jhp3/hh5/+hZ07XsX+ffvE8sqKCoRNuE/GCN1X3NRYZOW+hrZL59F26Tx2Z+/C4YOFAIApkdE4caoGbZfO49H58xA3NVbmaJXhiZe2YcfRo06XGdva8Ez2a70ckTLFT5uDbTmZMHzfAsP3LXgzZw+OHjpis86ObZloNJ6VKUL3NyxxAQznztm0hT2zAl+8uhM/vvU2Hov6LR5YnypTdMow+Z2j2DgmBD8kPo6yqTGYX1Yqd0jdpsgkyGAwwMPDQ9ys2wwGg1hvyJAhNvfV6GjRHzFj5kxxP3LiRDQ1NQIAXkxPQ9iE+7An7025wnNr+YcPYOr0GHF/QkQYWpqaAQBtl85jsMYPADBGFwJTg1GWGJVEqvBMvPdep8ufeGmby2VkK/fQm5g8bYq4Pz58AlqbW8T9RtNZbFq7EePDJ8gRnlszfnUONzz0INbNedhh2Y9vvQ3NHQMBALq7foOGttbeDk8xjBcvoqG9HU+OGAkACB00CKfnzJU5qu5TZBI0ceJEbNq0CWazGYmJiYiKioJWq8WkSZPw4YcfArAMoQ0ZMgRarVbmaN1Lw5kG+Pj4AgCeXJaCH376l8wRKYepwQgvH2+H9hP6KtwXfr8MESnLF7l78NITi50u2//+ewCARyZO6s2Q+gyTwQhPby9xf82Tz2BbDofBnNHcMRA/vvU2ZujGdbie/rO/IDJwRC9FpTwnv/4K/v37Y/I7R8VwmPHiRbnD6jbFJUF6vR4NDQ146qmnAADLli3DsWPHYDAYEB8fj9zcXABAZWUl4uPj5QzV7UjzgawrQ9Q10nwg68rQWaMJg355C3Zn70LMzGlyhdYnzH1uo8sEiTomzQeSKkPSsJh1pYi6TqoU7Sj+E2aO51SBjpQ0NeLhO+/ED4mPY+OYEEQffVvukLpNcUlQc3Ozy2WzZs3CsWPHAAA7d+7E6NGjeysst/diehp27ngVp+vr5Q5FcV7Zsh27s3eh6tRHNu2DNX5ou3QeJ07VYEpkND6sOiFThMr2xEvb8Nz830MzaJDcoSjOjm2ZeDNnD97/pFK0LU5IwvoXn5MxKmWTKkVfvLoTYc+sQMXnp+UOyW1pb+yPGUN+BQCI8R8Cw8V2xVWD3PaSoKSkJPj5+YmKj8Tb23E4wlpiYiIKCgo4FGZl0cKFOF5SwqGvHlixJAV/Ln0PbZfOu1xnsMYPfhoNWls4f6AnSmpqYGhttZkUfWf8I/gid4+MUbm/1U8+jfKyP8Pw/eW5QB+dsEwHmBAYKto+KH0fAPDY4qTeDVDhNHcMhHbgQLScd/3eVzPv//4lDBfb5Q7jirltJcjPzw9lZWUALENb/v7+AACdTgd/f39s3rwZAJCeng5/f3+R8IwfPx5xcXEICwuTJ3A382J6GoxGAytAPfDKlu04azI5VICkYbCzRhMA4PDBQpiMRowIGilHmIr3Re4e/PPd4/jnu8eR98xKaD09mQB1Yse2TDSZGm0qQABw75jR4ooxw/ctGB8+AcvXrWQC1AXSMJjxK8sVY/v15TCcO4dRv/qVzJG5p9BBg6C9sT/2n/k/AMCW2k8Q6eMLzY03yhxZ97htJeipp57Czp07xdVfDQ0NYtm7774Lf39/LF++HABgNpvFslmzZiEuLg7Tp0/v3YDd1MqnLfMFrH8HaMFjf8D2l1+WKyTF2LB6HQDY/A7Qo/Pn4YWtaThSUoQxdweJ9iMlReJqMaJrbdPajQAsvxUkmZPwCJ598Xm5QlI8zR0DUfbcC7jzDwtEW9lzL4irxcjR6Tlz0S/rFTxS8i4A4IfEx2WOqPs8zNYZRB9QUFCA3NxcFBcXyx0KfvzXv+UOQbEu/Kj8Mqtcbqn8RO4Q+rTm0cPkDkGxvN6rkTsExTK/e0zuEBTrhkzXX/rdthLUE0lJScjKykJ5eXnnKxMREZGque2coJ7IzMyE2WyGTqeTOxQiIiJyc32qEuR20l+UOwLF+suc8XKHoFgT7uRwzbU04Ib+coegWOYvPpc7BMW6kLZe7hAUq6NZXX2qEkRERETUVUyCiIiISJWYBBEREZEqMQkiIiIiVWISRERERKrEJIiIiIhUiUkQERERqRKTICIiIlIlJkFERESkSkyCiIiISJWYBBEREZEqMQkiIiIiVWISRERERKrEJIiIiIhUiUkQERERqRKTICIiIlIlJkFERESkSm6XBHl4eDjcoqKi5A6rz9hfV4d+Ty9Hv6eXY/LrOXKH4/YifUPEbfvKNJfL2hpbZIpQeRaufBoLVz7t0Ha9rxeu9/VCelamTJEpy4Bf/FLcnly8RLQXHjhos+xEZZWMUbo/fiZ2z6Bf3sT3fg4AACAASURBVCJuK5akiPYPq07YLFMKt0uCAKC8vBxms1nciouL5Q6pTzB+9x0eKdiLPbNm44fnN0Fz881YdOQtucNyW08/vATPvLweJY1VKGmswtE3CvHe2yVi2eSHY1DSWIX5Tz+OFXMWyxytMixc+TR2vLHHpm3f20ew4409+KmxBV/qK/H08xtRcfJDmSJUhukPTkXOnlxc+MclXPjHJezamY3CAwdhMhqR8Ei8aM/Zk4vfhkfKHa7b4mdi98RNjUVW7mtou3QebZfOY3f2Lhw+WAgAmBIZjROnatB26TwenT8PcVNjZY62a9wyCerIkCFDRIVIr9eL9oKCApvqUUFBAQAgKSkJUVFR8PDwQFJSklxhu4WTTU3Q3nILZgQEAACWjrsPO0+ckDkq9/X8G1tx/4OXTyD33heMr1u+AgC0mppxd3AgAOCue+9G61lWgjozdFwoAGDifRNs2ptaWkSbxncwtIMHo7mtrdfjU5KDbx9GTOx0cT88MgJNTU3w02hw4R+XRPvIe4MAACajsddjVAJ+JnZP/uEDmDo9RtyfEBGGlqZmAEDbpfMYrPEDAIzRhcDUoIw+p6gkKCoqChMnToTZbEZ+fj7GjRsHADAYDIiLi0NDQwPMZjM2bdqEVatWiccdO3YMZrMZmZkssztj/O47uUNQhFZTM273ugMAEDQ+GKeqawEAn310Cp6DveQMTRHqyyvx8sbnHdpD7r0X7/75fQCAsfEsDGfPwnvQoN4OT9EMDQ3w8fFxaD/Xdg4A4KfR9HZIisbPxK4xNRjh5ePt0H5CX4X7wu+XIaLuc8skaNy4cTZVHYPBAMCSzCxbtgwAMGvWLPj7+6OgoABarRZmsxlarRYA4Ovra7O9SZMm9e4TcFOjfHxgOH8elSYTAGBL+Z9ljkg5pPlAUmVo0UbLWHikbwhqKz/C7vKDssWmdGNHjcb7h97C9b5e+LUuFF/qKzF21Gi5w1IMaT6QdWVI8tvwSKRufLa3Q1IMfib2nDQfyLoydNZowqBf3oLd2bsQM3OaXKF1i1smQfZzgrRarUiE/P39RXLU0NAgHpOUlCTa4+Li5ArdrWluvhl7Zs1G2I4s9Ht6OQYPuFnukBRhX1Yejr5RaJPoRPqG4O7gQJQ0VmHOov/l5OgrsHDl03hu+zb81NiCnxpbEDUnjpOju2hbxhbs2pmNjz875bBs5F13Y96C+VicvFSGyJSBn4k988qW7didvQtVpz6yaR+s8UPbpfM4caoGUyKj8WGV+w8tXid3AF0lVXkaGhrE/yV6vR5ZWVkwm80ALPODrIfD6LIZAQFi/LvSZIL2lluguZlvfFe2r0xDzQfVKGm8fIWNlOzcGTgcAPCbUQHwHOyFL2pPY5Avh8W6y9jYiPtDx4r7v589F+9VVmBZorrn8HXmycVL8N7xUps5QJIBv/glUjc+ywSoC/iZ2D0rlqTgz6Xvoe3SeZfrDNb4wU+jQWtLay9G1jNuWQlyZdKkSUhPTwdgmQckTY5ubm62WY8JkHOVJhP6Pb1c3J9/cD8SRgfLGJF725eVh7bGFoehLinR+aL2NADgLyfr0Hq2RSRF1D0aX1+8V1kh7r+2N88mKSJH2zK24KzR5LIClLMnlwlQF/AzsXte2bIdZ00mhwqQNAx21mgZVjx8sBAmoxEjgkbKEWa3KKYSBADFxcXi6jAAyM/Ph06nA2BJfKT28vJyjBs3TgyhkUWonx82/vZ34k2/YMwYPDn+Ppmjcl/Zz78CwDL0JZn8cAwWbUzBHv1BPKKbjucWrgEAPPPyelaBeujljc8j+uE5uP7n4zfxvgmsAnUideVqAJaKj2TegvkYq9PBaLBcJp/wSLxY9qfSEowJDXHYjtrxM7F7NqxeBwA2vwP06Px5eGFrGo6UFGHM3UGi/UhJkbhazJ15mKUxJLrqfnxhs9whKFb5nPFyh6BYE+Db+UrUY5du6y93CIr1i62vyB2CYl14Yr7cISjWwP92PbypqOEwIiIioquFSRARERGpEofDrqF//eOfcoegWP9+/jm5Q1Cs/7T6O1J09dX9m79m3VO59YfkDkGxfnFdP7lDUKzNIctcLmMliIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFTJLZIgg8EADw8Pp7ekpKSrso+CggIMGTLkqmxLaa7//24Qt4WLF9ksi37wAbHMaDTKFKEyVDY1ot+z69Dv2XUY/vJ2ucNRrIUpT2JhypM2bdEzpuP6Wwfg+lsHYN9bhTJFpiz33jFc3F5Yvt5m2ROzFohle17OkSlC9/VG3Ba8PH41Xh6/Gv9XegoA0PbpWdFmfZOWk8VrsS8gLTQFaaEp+OJ4rc2yg0uzxbIP896XKcLuuU7uAABAq9XCbDYDsCQrq1atwpkzZ2SOqm+IfvAB5O1+AzNjZwCwJETjdDrMjJ2B9IwXAQA//f1H7DuwH1GTf4f6v3wuZ7huLSz3deyJmYYZd/0Gk/fmYdEfi7D9d9Fyh6UoC1OexI7Xd+Gx/50n2tJf2oZ3y0rx07cXAADX3zoA3oM8MXbMGLnCdHtPzFqA53akY+LU3wGwJEQjQ+7FxKm/E0nPR1+dFsvCJ0+E12Af2eJ1J+9nHMVdk+/FyNnj0PbpWRxamI07hnlj0D2DsfCDZ8V6/1d6Cieyj+NX4XfLGK17KUkvRMCUMRg9dwKa64zIT8zEwOE+uMnzFpH0pFSmAQDSQlPw67C7cZPnLXKG3Cm3qAR1Rq/Xw8PDA1FRUfDw8BD3DQYDANsqj31VSa/XQ6/XIy4uDg0NDYiKipLzqfS6orffEQkQAEyMiERTUxMA4LVd2Xjk4UcAADNjZ8BgMKCiskKWON2d8YLlBD3K0wsAMMFPA+P3F+QMSXGG3jsSADAxLNym/b3ycjy/NlXcf35tKvIPHejV2JTmpYKdIgECgJD7dTjX3AYAKHxjP1ZsWi2WffTVaSZAViYkT8bI2eMAAIPuGQwA+Nu3Fx3WO7ZuPyKejunV2Nxd5LIYjJ47AQDgHaABAFz6xnLs6o6cQETKQ2LdlMo0t0+AAIUkQZL4+HhRMXIlKSkJ+fn5MJvNKC8vR3x8PHQ6HfLz8+Hv74/i4uJeitY9nTE0wMfH8oFoMBjg7eUllmm1WjS3tMgVmlvTDBgA7YABONlqOT7vm4zQ3DRA5qiUpf6jj/Fy2otdWtd49uw1jqZvaTKexUDvQWg524RmUxNKj74rhsPePfxHucNzW22fWvqZlAxJPt5bDt/RQxza6bLmOsv0Ce8ADb5vPY/vW87jy7JTLofK3JWikqDRo0d3ab3GxkYAgE6n47CaFWk+kHVliLru9MJFSH2vDP2eXYcJfhoOhV0l948bh9d27xb3n16X2sHaZE+aD2RdGWptasFHX51G9tt5eOaxZWg52yRXeG7r7WW7cWhhNn4z1fG88tnRjzDqkQkyRKUMB5dmIz8xE4ExITbt7ee+Q0plGuKyknB0zZv4vvW8TBF2naKSoK4oLi7Gzp07bYbDCEjPeBE7XtvBOT89JE2KLprzMH5YvRYAODn6Kln2xGIM0WrFxGjroTHq2J6Xc3AwtwCHq4/ZtD+c9L8AgMDgkfD288Fnn3Byr70H0x/Fwg+excXW7/B+xlHR7qo6RJdN3zIfKZVpaG/5DiXply9kGDX7PgCW6tBNXrfg3Gn3T74VmQR5enp2uPzMmTM2w2Fqt3DxIry2Kxs//f1Hm3b74S/74TG6rPniRWgHDIBmgGUILGbYcBguXBBzhejKFO0/iJ++vYCfvr0AU2Mj7h83Tu6Q3N4Ly9ej8I39YgI0ADH355tz38gVluJ4j9TiYut34n7bXxrhM0qdVxJ3l2/QELS3fCfm/kjzg5REkUmQpLW1FQCQm5sr2oYMGcLqj5X0jBdhNBqcVoB+P28+9ryxBwCw78B+aLVajA0d29shKoL3jTfaJD2Fn5+2SYqo56wvma84cQI7Xt+FaQ9OkTkq97bn5Ry0nG12qAABwPT4WSgufAcA8O7hP6LZ1GQzVKZ2by/bjY/3lov7la++C++RWnG/+WMDvAL85AjN7R1cmm1z6fufM4vgG2RJGANjQvB5yScAgC+O1+L7lvO4MyJQlji7wy0uke8urVaLTZs2YdzP3xYTExPF3J/c3FzRDgANDQ0ALPOJGhoaMGTIEFXNE3p61TMALJfGSx77/WN4edt2LEt+Eu+9/55Y9uVnX8gSoxKE+vhiT8w0DLMaAvt84aIOHkFd9XLaixh670hcf6sloXz/nT9BM5gnoY5sf9YywfzeO4aLtunxs7Bi0xqs2LRG/E4QAGS/nSdLjO7qwfRH8UbcFlS++i4A4DdTR4urxQCgveU7/PK2G+UKz61N3zIfr8W+gD9nFgGwJD7S1WKRy2LE7wQBQFzW1fmNv2vNw9zZ5VbUY//6xz/lDkGx/v38c3KHoFj/uXiJ3CH0aXX/bpM7BMXKrT8kdwiK9Yvr+skdgmJtDlnmcpmih8OIiIiIeopJEBEREakSh8OuIQ8PD7lDICIiUrXK+nbx/5Bf2873UuTEaKWQ8suqL5V32SAREVFfx+EwIiIiUiUmQURERKRKTIKIiIhIlZgEERERkSoxCSIiIiJVYhJEREREqsQkiIiIiFSJSRARERGpEpMgIiIiUiUmQURERKRKTIKIiIhIlZgEkWKlpyYjdGh/m1tsZGCXH5+XvRXHiw5dwwg7tjQhBqFD+3d53dYm0zWOyDXrY11XUyVbHO7I/nU8XnQIedlbu72dupoqm+3ERgaitcmE1iZTl/p1d/pTT1hv/3jRIYQO7X9F7x/p+fbkWNkLHdofSxNibGK91u8XKf7Qof2RnprcrcfmZW8V7yXr45qXvdXmedC1xySIFO/g8TpU1reLvxTclRPG8aJDyExbe61DuyrSU5NRrS+VNYbqijIE68JRWd+OgKAQWWNxZ3U1VViTPK9Hj92wIhExcQkALCfDKTPj4enjh7TUZKx6IavTx2/JKbT5a9lq4+XjB6D33i+nPqkGYPn8WZaa0aNt3Hb7IACAl68GABA2aSqq9aWyfjlTGyZB1Kc8tmQ1WhqNolohfeOyrmJYn6jWJM9DXvZWUemQvj06+9ZrXQ1JT00W67iqjkjfFK0f19G3vNjIQIeKVl72VhTm5wAApkcEiP3YPydX24uNDLSJU3p+UkzSMulD19l2YyMD0dJoRLW+1OGY2FffurPd1iZTp8fH+phYf9t2tX9nx1+KRdq2/esokeKRblKFwr5i4aoK0tpkQuLsKABAZtpasW3r499RrC2NRgSOGivaMtPWitdrw4pEcRytY16aECO2aV8JcrVfqV9InFVRrI9DZ6TjKbGuctizft1Kjh5wuS37mJy9B60rSOszdol9O3u/2MfmbDvS6y89f+kYOesvedlbxZeo6REBXern1tudO3+JSHy25BSK+Dx9/ODlq0FR4ZudHne6OpgEUZ8y/J4gAJZvaXU1VchMWysqRV6+GuRmpiEgKER8aK7P2IW585d0adstTSZU1rcjJi4Bhfk5CAoZL75552amuXxcdUUZKuvbkbW3GNX6Uqfl//TUZHj7asV6LY1GHC86hLnzl4jqwMHjdQgICkFsZCC8fDWorG9HUso6JM6Ocln6b2k0Ij4pRTz/xfOm2iyXnkNE9DSX2z1QUgsAohIkJZFJKevE87dPXrqyXfvjk5SyDtX6UnHyWJoQg5ZGozgmhfk5yMve2qX9W2tuNKCyvh1bcgrFSTJrbzEOHq8T2wSAtNRkEWdMXIJNEtIVnj5+yNpbDABISlmHZakZOF50CNX6UmTtLRavq7PXX6oqSP33XHOjTfwSqe+JY6cvRfDYMIftdXW/9vKyt4rjdfB4nWjriJS4Sa9bTdUH8PLVOK0YrkmeJ/qRvfTUZBTm54j3a7W+1OnrWlnfjmBduE0ld03yPCxLzXD6fnFF6nOZaWttkqVqfSkq69txoKTWZX+x30+X+rnddqVK39KEGJtKX/DYMNkrv2rCJIj6rICgEFTWt6Ps2GGEDu2PlkajzQmlu6Jj5tjcD5tkSSi8fDUdbnfKzHgRj5evBjVVHzissyw1A1tyChEbGSiqCefamhzWa20yoaXRKLYpxVB27LDTfVufjKbMjEdLo9Hmg/nuEcHd3q50wpbWmTIzHtX60h5vVzqJDxzkY7Ofan2pONFIr+Xc+Uu6tH9r1kmC9Qna08cPwbpwHNmXC8CSbLQ0GhE6tD+WpWagsr4dnj8PsfSU9Bomzo7CN+daxXNwWO/npEfan/X+D5TUikQ0cnIsAEvCIVUfpLae7Nfe3PlLcKCkFksTYjA9IsAmNlcioqcBgKjsVOtLxettTUo0pPeRfdyF+TmIiUsQx0BKiq1fVynh8rJ7Xbo7DCjtW+pDUp8CLMm+pKP+Yq1L/dxqu3PnLxGvx5acQptkbaC3r9gmXXtMgqjPkoYxjuzLFZUQd+AsYZJK7lNmxotqgjPffN0GwDJUEjq0vzhRdYe0DeDynITubFc6KU6PCEDo0P5O51b1ZLsivnOtHZ4AurJ/a9JJBbBNdEKH9rf5xv3YktXi/92dZO+KdeKxJnmey2Gili6e8KSTZcnRA6g9WeGy4tLV/dqThou8fPxEJagrYuISUF1RJvYhJcHWvjnX6vLxHb3e1v21O4/rDutEzzrB6qi/OIuxo35un7iRe7hO7gCIrqbTn9YAsHwIS99Mt+1yXiWxZn2ivNa8fbUObYX5OQjWhWPu/CUdnqyk5CIpZV2Xh/FcbaOn25WO1cHjdZ1WSjrabkcnsI62253925OOvVRZsRYRPU1UNaTKYV72VvGtvqekKoU03LNhRaLD/rtzgpQSjpZGI5JS1nV7v96+WpeVyyP7cuHlq8Gy1IxuJRiBo8aiMD8HuZlpLhOz2wZ6unx8R6/jbbcP6jCBuhpcvf876i/WetrPSX6sBFGfsmPrs+JDWPpg++brNjHxtDPffN2G1ibTVR2TlyoVUgxBIeMd1rEeUnM2YVQiTZyUSvLShFFXiZM0twi4fIJzdsLpznalb/lSqd96cu6VxmstWBcu5r9YT6Luzv7tBYWMt5k4bz3Z1XoyrlSNsx6ik6oF3Zm0aj3hfllqBrx8NU6T4O4MgUROjhV92VnFpSv7lR5vP7nb21crlrkaYnVGSh5dzVECLlexpONn38+l+U7SMchMW4tgXfgVD0k6I83hk56jq+PYUX+xdiX93J790ChdW6wEkeJZl569fDXiW9vc+UuQmbZWzLEJ1oWL5CYiehrWJM/DmuR5qD1ZgWWpGTiyLxeJs6Pg5auxWfdKxcQliKtnpGqPvceWrBbDFtLcAenDMHJyLArzczA9IgDrM3bhQEmtzZU76zN2uZwAKl1pIl0N19EQR1e3K00sX5M8TyR4Hc3JcLXdzk740hwp62MnXYrcnf1bmzt/Cc41N9r0CWmbB4/XiSE2wPK6SSd3aQKtNG/FFWneV2baWtRUfYAtOYVoaTLZ9FFnVYWwSVORmbYWpz+t6fTkJ+1D+r8zy1IzXO43JTVDPE/75xKflCKuAvTy1cDLV9PloTrpPeNsjpIka28xEmdHOd23fczBunCbK6e6yv79Ir2G1rx8/MTrnJSyzmV/7Ki/2OtpP7dXXVHWYR+jq8vDbDab5Q6ir6v68qLcIZAM6mqqkDg76oqGrq6EVB3prJRP7mFpQgy8fPx6/JszcluaEIPmRoNb9zfp0vasvcVu+XtXrT8nge4aX18Q8usbbe5zOIyIyA3EJ6XYXP6uFNIFCNX60i79qCO5VnbsMIJ14UyAehErQb2AlSAiIiL5sRJEREREBCZBREREpFJMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSIiIiIVIlJEBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSIiIiIVIlJEBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEqQirU0mhA7tL26upKcmIzYysBcjc3/SscvL3mrTbn08Q4f2x/GiQw6PraupQujQ/qirqeqtcN3K0oQYcXyWJsS4XObs2AE8ftbSU5MdjkVsZKBNH7Tvo8Dl/uvqGKvB8aJDLj//2A87JvU7+5tEyX2QSZCKLJ43FTFxCaisb0ewLtzhhCRpaTJhysx4Vb7ZnamrqcL0iACH9tYmEwBgfcYuVNa3o7K+HRHR0xzWKzl6AFl7i1Fy9MA1j9Xd5GVvRbW+FFl7i5G1txjV+lLxASktq6xvx/qMXViTPE8cU2tqPn7WWptMKMzPcWhvaTQiKWWd6INz5y9xWKfs2GFk7S1GUeGbvRGq22ltMmFN8jxk7S3GweN1ACwndoD9sCuWpWaI/pW1txgAEBOXIJYruQ8yCVKJupoqtDQaEThqLAAgKGQ8qvWlTt/sdNnxokNInB0FL1+Nw7LTn9YAAG4b6NnbYSnG3PlLUFnfjoCgEHxzrhUAMHCQDwDgyL5cBOvCAQDD7wkCYPmgJOfSfj5pW5O+qEjHlJyT+lVAUAg8ffxQWd+OZakZANgPuys3Mw0AxPFTeh9kEqQS0gnIof3rNoc2Lx8/HNmXi4CgkGsdliLExCVg1QtZDu21JysAAImzoxA6tL/4ZmkvcnIsEmdHIXJy7DWN050dLzokvolL1bKWRqPDeueaGx3aePwsx69aX2rz7RsATn1SDQBYkzzP6XCjJGzSVCTOjkJ0zJxrHqs7qqn6AF6+GqfDsuyHXVdXU4VqfSnWZ+wSbUrvg9fJHQC5HynDJyAiehoioqc5HRpsaTLBy1eDAyW1qKupQuLsKAz09nUoBQcEhaCyvr23QnZLEdHTMPyeIEyPCEBSyjqn5XJXePwsJ5iklHUO7dLJurK+Ha1NJkyPCEB6arLDe1iqfqhZS6MRWXuLcdvtg1wep46wH16uAlkP+yu9D7ISRNRDW3IKcaCkFgBE1aym6gM5Q3Jrnj5+8PLV4Mi+XLlDUZT01GR4+WqcJo7SXA3g8vGtrijr7RAVQxoO43HqGWfVSKX3QVaCVMLVvJXbbh/Uy5EQWTibZzXQ21eGSNxbdUUZWhqNNlfjJM6OQtbeYg5Zd5GXj5/rZeyHXSJVw6V5pX0FK0EqERAUAi9fjZjHUlP1AYJ14fDs4MOBOhYbGSh+SkD6gHC38W65SZfWtjaZ0NpkQkujEVNmxgMApsyMR7W+FMDlSeZhk6bKFqu7OlBSK666kYbEpARIurQbgMPxpcukeTzHiw6xH/aQNPfH/gu10vsgK0Eqsm3XYUyPCBCX2brj+KySSMdT+gBISlnn9BJ5NVuWmoHqijLxEwMxcQliWGfu/CWoqfpAHL/1GbuYlHfTlpxCm99ssT6+dFlAUIi4/B0AgnXh7Ic9ZD96oPQ+6GE2m81yB9HXVX15Ue4QiIiIVC/k1zfa3OdwGBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSIiIiIVIlJEBEREakSfzG6F3h4eMgdAhERkerZpzz822G94IfEx+UOQdE8JkXJHYLimWtr5Q5B8f5jKv+o5pX6+1Ct3CEoWvqn2XKH0OdwOIyIiIhUiUkQERERqRKTICIiIlIlJkFERESkSkyCiIiISJWYBBEREZEqMQkiIiIiVWISRERERKrEJIiIiIhUiUkQERERqRKTICIiIlIlJkFERESkSkyCiIiISJWYBBEREZEqMQkiIiIiVWISRERERKrEJIiIiIhUiUmQSvXLegWVbW3i/v4z/4d+Wa+gX9YrGP5mnoyRuTfjV+dww9TJ4vbiW4fEshffOmSzzPjVORkjdX/9UlejsvGsTdvw7VvQL3U1+qWuxv6/nJIpMvdmbG7GfwXeLW7pubtslj+Q9AeXy+iym37x3+J2orJStB86cEC0j7jrbhkjVIaqN0rxyrRnAQBNdQZsCF7scPus5GOZo3TtOrkDoN7XL+sVm/vGixfxSMm7+CHxcQDA5HeOYtEHf8b28ffJEZ5b+93aNSh7fhPGDhsO41fncOdjv8eYO4cBALKPHcOPh48CsCREv1u7Bp+/ulPOcN1Wv9TVDm2Lio4iwn8Iti+aDOOFCxi2LQMzfsOTkL3fJi7Ae6/vxtgRI2FsbsbQB36LkIBAjB0xUiQ9/6y1JJD/FXg3pkVMhMbbW86Q3c60B6cgZ08upsXG4tCBA4gKj8T3//gbTEYjEh6Jx/f/+JtY78nFS/Ditq0yR+yeLrScR+nLb2OA960AAJ8ALVZVbxPLPyv5GO+/WoS7IkfKFWKnWAlSkcq2NvTLegV7IifatGtuvFEkQAAwwcsLxosXezs8Rfj81Z0YO2w4AEBzx0BoBw5Cy7ffYuyw4TYJz5g7h8Fwrs3VZlSrsvEs+qWuxp7pMxyW7Tz5IbZHTwYAaAYMwA+pz/Z2eIrwxdE/YuwIy0lF4+0NrY8PWr76CgCQfeggXnpmlVj3n7WnmAA5cejtI5gWGwsACLr3XgCAyWiEn0YjEiAAGD9hAkxGoywxKkHx5v3wH3Ony+VvrdqNB9fM6cWIuo9JkIqEDhqEHxIfx6jb7+hwvfdbWjDBy6uXolI2w7k2eN16q0N7y7ffQjtwkAwRubdQ38H4IfVZjPKyPTFXNp6F9uabsajoqBgOsx8qI+cMTU3wuuMOGJubYWhqwqHj74rhsP3Ff5I7PLdX89FH0Gi18NNoHJZ98P77GD9hggxRuT9piOueB4KdLq96oxT+Y+6ET4C2N8PqNiZBJEiVopKmRsT4D5E7HLf3wLq1+aRXWAAAHtBJREFUiBwxUlSGJMavzuHhF9Owbs5cmSJTJsN332HwTTfhh9RnsWf6DITtypY7JLf3QNIfMDF0rKgMAcDZ1lb8s/YU3nt9N+aueArG5mYZI3RvI+66GwmPxCM+YZ5N+4nKStz0i/9GaclxTHloqkzRube3Vu1G1FOOFV3Jx4croZs3qRcj6hkmQSRIlaKyqTEY9uYbHBLrwKJXs9DQ1oZ31q5zWHbnY7/Hc4/GY8a48TJEpmxPjh0HAGIuEKtBrj3x3AacaWrEO5mv2rQnPxIPABg7YiS0Pj44yQnmLn3y2Sl8/4+/ITdnF7ZlbBHtY0JD8f0//obi0hIEDv8Nh8Ts/GnzAYQvfBADvG5xurypzgAAbl8FAjgxmpwIHWQZxmn729+gufFGmaNxPw+sWwsADpOepYnSbzyZwgSomwb9j6WfGS9cgGbAAJmjcX8PJP0BgGV+kESa+9P6zdecB9RNYRHhaDzrmHCPCQ0FAJxra3M6XKZWhuovcKH5W5S+/LZoe2Xas3j8kOWCh+ZPjdAGu54r5E56tRKk1+vh4eEhblFRUdd0fwaDAR4eHjAYDNd0P0onDYNJXvzEMtYrJUN02aJXs6C9Y6DLClDZ85uYAPWAZsAARA4ZgsLTfwEAvFhRDu3NNyPUd7DMkbmfJ57bAI23t0MFCAAemzETBX+yJEb7i/8EQ1MTZkT9trdDdHsj7robhw4cAGCZEJ2z8zWE6saKYTCJVB2SkiGyePzQaqyq3oZV1dvw0IZHMcD7VpEAAYDpoy/hO8Jfxgi7rtcqQQaDAePGjUN5eTl0Oh0AICoqClFRUSguLr4m+9RqtTCbzddk231J6KBB2BM50SYR+nzOwzJG5J6MX53DjmLLCUb6FwCeezRe/D/s6eU2j/lix2vQ3DGwdwJUuKNzH8Xw7VuwsuRdAMDni5Nljsj9GJubsWP/PgAQ/wLAc0uWYln8PLz0zCrxO0EA8N7ru2WJ09198tkp3PSL/0bCz0OH6zZuEFeL5ezJtUmEan9OzKnrvmv+FjfefpPcYXSJh7mXsoSCggKsWrUKZ86cEW16vR7jxo1DQ0MDDh48iJ07d4rlSUlJAIDMzExRMTp27Bg2bdqEsrIyhIWFYflyywknMTERmZmZMBgM8Pf3x6RJk3Ds2DG8//77mDBhAhoaGqDVauHh4SH2nZ+fj1mzZonY4uLiAACTJk266knZj0kLr+r21MZj0rWtGKqBubZW7hAU7z+mcoLslfr7UPefI+LO0j/lxQJX6tnRi2zu99pw2KxZs9DQ0GAzPKXT6WA2m6HVdv7GOHPmDMxmM5566ikAwPLly9HQ0ACz2YysrCwUFBSIdcPCwmA2m+Hj4yPakpKSsGnTJpjNZjQ0NIikR6/XIy4uTmzrzJkz2Lx589V86kREROSGenVOkNlsRmJiIvz9/cW8oK7O15k40fYH/hITE0XylJiYiA8++EAsC3UxfmsymQDYDpNVVlbabGvDhg3YuZO/8ktERNTX9fol8pmZmTCbzTCbzdi0aRP8/a988pSfn1+nyZQ0XCYlX1LlyGQyISsrS7RLFSIiIiLq23otCUpKSnIYZpo+fToAy6RpX1/fHm/bZDJ1aUituLjYYTjMz88PiYmJIjGThsSIiIiob+u1JGj27NlYvny5TcUmPT0d/v7+IoFpaGgQy7KysjrcnvXyrKwsjB/f8WXJUVFRNvOGJKGhocjKyhJxJSUlXfNL94mIiEh+vXaJvE6nQ3l5uc3wl7+/v6i6zJo1C7m5ueIKrsTExA63N2nSJLHupk2bMGvWrA6HxDIzM+Hv7y8qQOXl5SKu/Px8EZd1TERERNR39eovRktXg7ni6tJ0Z+3OfgPIvs3+vqt9z5o1S1wuT0REROrAvx1GREREqqTIvx12rX5hmoiIiNSDlSAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSI3N5/TXlA7hBI5W5Yu0ruEBTv+oDfyB0CkQMPc0c/4UxXxY//+rfcISjaf/7tH3KHoHgtHpfkDkHxvG64Ve4QFK/8m0/kDkHR3vy/I3KHoHjZ92+wuc9KEBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSIiIiIVIlJEBEREakSkyAiIiJSJSZBREREpEpMgoiIiEiVmAQRERGRKjEJIiIiIlViEkRERESqxCSIiIiIVIlJEBEREamSYpKgzZs3IyoqSu4wFK/f9deJW2VFhWh/MT3NZpnRaJAxSvd2/U3/I24VJ6pslg0dcY9YRs759R8kbierPhTtRw8dtll2X2CIjFG6t+v7XS9uFZUVDssrKitwfb/rZYhMOcJ8RovbqZO1ov3UyVrRPlcXI2OE7u3A3EzkhG0UN0PZabHs2PJ80f7Xtu9ljLJzikmC6MpNjv4d9uS9iR9++hf25L2JsAn3AQAqKyqQ81o2fvjpX/jhp39h4/PPIzrqtzJH656ipz2EvJzX8dP3f0VezuuYEDVRLFv45FJEhoXjp+//iufXPYvoaQ/JGKl7eiQmDi/tyoKpvQ0v7cpCbNQUsay64gTmJjwKU3sbTO1t+HNtVQdbUq/oydHI25OHn374CXl78jAhbILN8orKCoc2srV87iKsemUDypo+xKpXNmBxzAKxbHHMAix45gmUNX2IB+Y8hOVzF8kYqXv6a9v3uNh6ATPefBwJZSuRULYS2rDhAIBPCyzv24Sylbh/1UMoTtkrZ6idUmwSVFBQAA8PD3h4eIgKkV6vh4eHh8169vfV7GjRHzFj5kwAwKjRowAARqMBoWPH4nR9vVgvJCQUBkODLDG6u6JDb2HmtOkAgNFB9wIAjCYTAGBHTjbiYmcAAKZNmYp3S4+LZWSxpzAfk6dNBQAEBI0AADSazop/g8eOkS02pSg6WoSZMyzv49GjRgMAjEYjACD9xXRMCJuAvD15ssWnBJvytiPsQcsXmGGBdwEA2hpbREVoVuLD4t+Tfz6BtsYWeQJ1U3//9q+40XMA/mfQTQ7L6t/5BL+aFAAA0IYNx8XWC/jqVFNvh9hlikyCDAYD4uLiUF5eDrPZjDNnzmDz5s3Q6XTw9/eHXq8HYEmUEhMTZY7WPZ388CS0Wn9oNFqHZc3NzdBq/WWISlk+rPkIWo0GGj8/kex4DhwEAND4+QEAWs+1yRafu6ur+QSDNX7w9RsM4P9v7+7joioTPYD/Zm9udtu7adkbijIzKIqpkKK8mqG8bJptSQpKxApqoC4UE2ZhitIas5D2JkHqdQ0dUMlXNsCVvMmLoeZLhqgwMzCAtmja7nWzZe/O/WPkwDiA1Lacczy/7+dzPnqec6hffBj48TzPmYB6oxmL58YJy2Ft5Yi6VnWkChqNBmq1GgCgS9Kh9XqryKnk5cyJr+A0ZBAeHjywy3sufd3Si4mk71rLX/GX5ivCklfF2iLh2l+ar+Du+9u3A/zSqT+utfxVjJg9IssStGPHDoSEhMDf3x8AkJaWhpycHADA/PnzsXWrbfrts88+w+zZs0XLKVXubm6IipyDmHmxDtdMJiOiIudgxcqVIiSTDzfP0YiM+Q3mRc8VO4osPebhg8Vz4xARHQnANgtUbzJje9FumL+9gFdSUzBxDGeFuuPm7obIqEjMi5kndhTZivR/BmkLUzBtjm3pepSXBwCgdE8JACAv6yPRsknZxVMNGOSlEZbCzuw5JiyDyc0dYgf4MczdLDGEhYUhONg2zZmVlYV169b1VizZaFv6cndzAwAk6V4Wro0YNgxvrF4tLJtR584ePwXAVoYA2/IX9Vzbfp+2zc8vJC6E+dv2WbMXEhfizeVpOFJZBS+f8aJklLqz1bbXsZu77XWsS9KJGUeWcss+BgBhA3R43HN4++McJDwzH2kLUzD/1cVixpMs30T7h5S85gfiwnEzRofL72EGSc8Eubq6CktbHbncWGrojEajgaurK5fCemBKUBDqbyw5mExG9O1zBzbnbrErRdS9oMDJMDfUOyx/3bw8Rp0LCHwMjQ3S3S8gB0FTgmCu596zf8W4iRNw0dIMwDYbVGqpQqmlCo9NDRTGqGduXv66eXlMaiRfgioqKgAApaWl0Ghs+1fCwsJQXFwsFKSUlBRh9gcAAgMDERERgYkTJ/Z+aAlzd3PDtvx8ALbSk5P9AfwDAgDYZoBKD/4PZ4Buwc1zNPILdgCwFZ3sDesR4OsHAFgQEwvD9m0AgILduxA8eYpQjsjmMQ8f7C3YBcC2BJa74Q+Y4OeNI5VVcLmnvTB+sPZ9DFG7cBaoE27ubsjf1vY6NiE7JxsB/gEip5KXSP9nhCWvCw1N2PNRAUZ7e+JCQxMCnccLG6HXvpqO6c/NEDOqJBUvMdgtfx3JKRU2Q7tN88T54pMAAGNpNX7p1B8PjnIWJWdPqKxWq1XsEF0xGo3Qam0bdLVaLWpra4VreXl5iIiIAACEhISgqKjI4eOk8p/2/T/+T+wIgr592ldA31i9Gkm6l5GZ8Xu8tnSpw71nzp3rdON0b/uPa9+JHcFOx/cAWp26CrqEROHczXM0jDee1Gm9Kp3NgE2q/xU7gqBj2XklNQUvJC4EYHufoMVz22dvOy6PScHAOweIHUHQ8T2AVr+x2mEpLH9bPiKjIiW3SfpQy3GxIwgCndsL9vxXFwtPhJXuKUHawhQAgNdj3kjPfUeUfJ3Zcn632BEE2yPX4S/NVwDYlsM6LoUVLzGg8YjtveZmblnY6VNkYln/eJrduaRL0I+l1+thNpslsx9ISiVIjqRWguRISiVIrqRUguRKSiVIjqRUguTq5hIky43R3QkNDUVxcTHq6vg+N0RERNS1264EdVwWIyIiIuqKpDdGExEREf27sAQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEi3Xb/F3kp2nJ+t9gRZC3qZ6PFjiB7F+75m9gRZG/gnQPEjiB7pc2VYkeQtUyfV8WOcNvhTBAREREpEksQERERKRJLEBERESkSSxAREREpEksQERERKRJLEBERESkSSxAREREpEksQERERKRJLEBERESkSSxAREREpEksQERERKRJLEBERESkSSxAREREpEksQERERKRJLEBERESkSSxAREREpEksQERERKZLkSlBZWRlUKpXDodfrxY52W8ldmYM181cJ51V/LEOM+wzEuM/A0pCFIiaTNpPFgj7DhwpHxvoPAQD5hfvsxtuO8mNHRU4sPT4PjxGOk1XHhfGm+ka7a9S1Pn37CEd5RbnD9fKKcvTp20eEZPLziX47DAlZAICv9n+BtAkJDoflpFHklNLjOXIU+t11N/rddTcKtm8Xxg9XVAjj/e66W8SEPSO5EtTGarXaHcnJyWJHum2c/+IMPs0rFs5bLF8jW7cGG6oLsKG6AA8MeQi5K3NETChdoTHROLjFgNaa8zi3vxRLM/QoP3YUs6ZOQ2vNeeFYrUtGsH8A/MaOEzuypCRGxGFlVjoqL5zEyqx0vPBUtHAtIfwFLExJROWFk3jm+ZlIjIgTMal0TX1yKnI356L1eityN+diUuAku+vlFeUOY9Q5y0kjjhWUCecjgx5FyudvC8fkRdOh9R4O5zEaEVNKT1JCIqJj5uLqd9dQdGA/YqKiYTaZAAChk4Nwovo0rn53DTHz52HG9KdETts9yZag7nScITIa2xt6fHx8p9dCQ0MRGhrKGaUbNi59D4/4ewjn9zs/iA3VBcL58Amj0NL4tRjRJO9syQGh2KidnaEZPBiNFy/a3WOyWLA0Q4/3lqeKEVHS1hqyEPTrUACAu+cjAGwzQADQZLZglJft69LDeywazRZxQkpc4d5CzJo5CwAw3ms8AMB04wdQRmYGJgVOQu7mXNHyycmelVug9R7e6bUrTZdx4L09CE2e2cuppC/z7bVIeOlFAIC3ry8A4OKFCwCAq99dg4taDQDw9feDsU7as2iyK0Gurq5IT0+H1WpFeno6goODAQB5eXkoKSkRZo5CQkKQkZEhfFxtbS1nlAB8smEXRvqNwfAJo7q8p+bzL7u9Tu2MDQ0Y9NBDdmOZG9djQcRsqJ2dRUolD9XHT2OgizMGDhkEAJgwyRdfHjkBADhx+BgGufDzdytVR6qg0WigvvFDR5ekQ+v1VpFTyUPlRwegmTAcLuOGdXr98JZSjJ3hj/4D7+vlZPJyuKICQHsZ6qiirByBUyb3dqQfRLIlqOOMjqurKwDAaDSirq5OKDLJycmoq6tDWVkZwsPDUVtbK3y8RmM/fdlWlpSsxfI1dmR+hMjX53d6/fwXZxDjPgOny05gXLBPL6eTn6mxcztd8so2bEXS3FiRUslDmM80vB63BL+OnCGMrTVk4WhZlbAfaK0hS6x4suDm7obIqEjMi5kndhTZaZvl+VXys13ec6ygDN5zAnsxlfzMmP4UQicHIWa+/deg2WRCv7vuxoacD/HsLGnPpEm2BHXcD9RWbpqbmwHYF6SOXF1dhfGsLH4DvVnuqhwsyHixy+tDHx2BDdUFeCU3Da+ExKPFwiWxrixKXY7ahnoUrt9oN55fuA/B/gGcBbqFHZX7UHnhJHblFiD3/f8WNkX/5sX5qLxwEh7eY7k5+hbOVp9F6/VWfLjhQ2RkZtz6A0hQpN+Gp9Oe7/L6V/u/gNZ7OGeBbqFgz25c/e4azCYTkhIShXEXtRpXv7uGE9WnETo5SJgtkiLJlqDOODk5AXDcNO3v7w+9Xg9XV1dhLC6OmypvdrrsBLJ1axDjPgM7Mj/C6bITdk+ItRn66AgAwNWWb3o7oixMjZ0Lk8WCsyUHHK4dOnoEj3tzFq2nJjzmgwuWZlz6ugUAMGa8JwAI+4Y6Pj1GnQuaEgRzvVnsGLJSd7gGO1P+gLQJCTjw3h7UHa4RnhADgIbjdV0uk5GjiZMmCRujO3JRq6HWaNDU1CRCqp6RVQnSaDTQarXC5ua2x+mNRiPM5vZvAkajkTNBnWh7+mtDdQHCkp7DI/4eeDFnmbAM1uaTDbsAtJcharcodTnUzs4OM0Bt9peXwcfTs5dTyUeYzzTs31UEwLYh+uM/bIOH91gMePB+AO2lp+2etlJE7dzc3ZC/LR+AbUN0dk42AvwDRE4lL509ARbxdvsvzsbPazBotFrEhNI2Y/pTePutNcL58tdShCLU7667hUJUsH07TEYjxo6T7lOyd4gd4Ieqra2FSqXCkiVLAACHDh2CRqOBTqeDVquFSqWCVquFwWBASkqKyGnlYeijI7Ag40W7IvRm8ToRE0mTyWJBtmErAAh/AsBqXTJ0sbY1cWNDA5weeFCUfHKwo3IffB4eg9fjbK/fhSmJwqzPB7s32T0y/8HuTaJklLqz1WfRp28fREZFAgBWv7FaeFqMfhpXGi/hFwPuETuGZBXs2Q3PkaOw/DXbz9iY+fOEp8WKDuyHh/sjwr1FB/YLT4tJkcpqtVrFDnG723jmY7EjyFrUz0aLHUH2jt7zN7EjyN64/u5iR5C91JP85epfoRvNBy7+Vff0/U+7c1kthxERERH9VFiCiIiISJFYgoiIiEiRWIKIiIhIkViCiIiISJFYgoiIiEiRWIKIiIhIkViCiIiISJFYgoiIiEiRWIKIiIhIkViCiIiISJFYgoiIiEiRWIKIiIhIkViCiIiISJFYgoiIiEiRWIKIiIhIkVRWq9Uqdojb3T/O1oodQdaWXt4pdgTZmzN0utgRZG9kdYvYEWTvnwcPih1B1lT9+okdQfZ+nrDI7pwzQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSJIpQWVlZVCpVJ0eer3+J/l36PV6hIaG/iT/LDkyWSzoM3yocGSs/xAAkF+4z2687Sg/dlTkxNL2p4yd2PHSeuH82+ZvkOGXLBzUOc8HhgvH8c+PAQCKdxbajd98ndqZmpvx80kBwpFh2NrpfcNnh3d5jdr1XbEMFQ31dmPu76xB3xXL0HfFMmw7/aVIyeRhRFoqtn1h/zq9M3GxcJguXxIpWc9IpgT5+/vDarXCarUiPT0dISEhwnlyMn+g/BRCY6JxcIsBrTXncW5/KZZm6FF+7ChmTZ2G1przwrFal4xg/wD4jR0ndmTJajplxomdlXZj2xM/xMT4J6Ar18PjaR+7gkQ2C2fF4s3sTBz/cw3ezM7E3CfnAABCnp6K43+uEY6E13XwfdwfnhPGipxYen6lewmfvvs+/n7wEM5uzcer2Vko//KU3T0Zhq0wNjeJlFA++q5Y5jD228K9mKJ1xfUVq3Am4SVE7dgmQjJ5GJGWCuMl+5Iz7YN1WOAXgO/XvovfPfkUnsh6X6R0PSOZEnQrbbM4KpUKoaGh0Ov1cHV1Fa7Hx8cjPj4egOOsktFoRF5eHpYsWYLi4mLhPqU5W3JAKDZqZ2doBg9G48WLdveYLBYszdDjveWpYkSUjU/S8uEyYZjd2NWmyxg4ygUAMMhDjauNl8WIJmnv569HyNNTAQAjHx0NAGist9jd01hvwdsrM7BUv7zX88lBzdY8+I2yfe7UTk7QOA1EU0uLcN3U3IxXs7MQ7DVerIiSV9FQj74rlmFz2EyHazlHqvDO1CcBAOr+/XF9xarejid5psuXcGfiYqQ+Mc3hWt2lFvhrtQAAb7XaoSRJjWxKEAAUFxejrq4ORUVF3d4XHR2NQ4cOwWq1wmAwID4+HuHh4cIM07p163opsbQZGxow6KGH7MYyN67HgojZUDs7i5RK+qq2HITL+GEYPNbVbtxlwjA0fWkGADSeMKHfoPvEiCcbX31xCoNcBmPQEPuvtc3vb8Sz0REO49Q5Y3MTBt5/v3C+eE0mcl9fIWIi6fMdPATXV6yC18BBduMVDfXQ3Hsvflu4V1gOu3mpjAD1fQPw/dp3MfNRx5naILcRKKurAwAcNpmgGTCgt+P9ILIqQVqtFhqNpkf3NjY2AgDCw8NvWZqUaGrs3E6XvLINW5E0N1akVNL3bfM3+GzdHzFF97TDtbC3YtFwrFbYDxT2Fj+PXXlyfDBeWZCEGVGOv4lv32RA1MK5IqSSn2kvJyHYa7wwM7St9AAAYGbgZDFjyZrxm28wpF8/XF+xCpvDZiJwI5e1f4h3nrW9pu9MXIxPz5/DmRRpz+jKqgT1VG1tLSIiIuyWw6jdotTlqG2oR+H6jXbj+YX7EOwfwFmgbuzP+BjTUmc7jLdtivaJngJduR6DPNTcHN2NvVUlOP7nGhRs3oZN77X/kCneWQjfx/05C9QDi9dkorapCft+nymMRa5cgXdfTBIx1e0hyS8AADDzkVEAwNmgH+DOxMXw12rx/dp3sTQ4RPKbo+8QO8CPNXjw4G6vW61WAEBeXh7i4+M5G3TD1Fjbb9hnSw44XDt09Age9/bp7UiyYv78HMyfn8O+5e1P3ex4aT18oqcAAAaOtu0JGj7FA/uWb0XTKbMwRo58JvmhuaF9A++xyqPwCvAWMZE8THvZVnRqtuYJY22bo91mzxLGSo5UAQB0EY7FnRw9/F+/BACYrlyBun9/kdPIT1vZ8RoyBADgp9FCM2AAjtTXQ32fNJfFZD0TVHdj3REAsrKyhL9z9qdzi1KXQ+3s7DAD1GZ/eRl8PD17OZW86Mr1wjEx/gm4TBiGsLdi8YsBtm+eTadse4Jq/nQCAFiAbvLk+GAU7ywEYNsAvX2TAWN92pdkKw+WY4wXvwa7s3hNJtROTnYzQADgN2o0/n7wkHAEe43H7xbEsQD9AOr+/RHk6oqPq08DADLLD0Fz773wHTxE5GTy0FZ0jtTbZs7KjXUwXroklCIpku1MUHh4ODZt2gSVSgUAiIuLE64ZDAZob+xOB9pnhXx9fbFkyRKEhoYqbmbIZLEg+8Z7hmR3eO+Q1bpk6GLnAbBtlHZ64EFR8sndPU73IiIrHoa49k33EVnKfAqxO3urSuD5wHC8ssA2k5Hwuk54WgwAGs0NuP+hB8SKJ3mm5mZk794FAMKfAFh2fkJ7I5+H+ztr8Nr+EgDAmYSXRE4kLzXLlmP4qlQ8t3kTAOCjqGjJzgIBgMra1hDo3+YfZ2vFjiBrSy/vFDuC7M0ZOl3sCLI3srrl1jdRt/558KDYEWRN1a+f2BFk7+cJi+zOZb0cRkRERPRjsQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKxBBEREZEisQQRERGRIrEEERERkSKprFarVewQRERERL2NM0FERESkSCxBREREpEgsQURERKRILEFERESkSCxBREREpEgsQURERKRILEFERESkSP8P8mW6ZoegM/8AAAAASUVORK5CYII=)
On analysis, there is a statistically significant difference between the comparison plate preferences and the datum preferences indicating that most people are drawn to some making approaches over others. The two graphs presented below are essentially inversions of each other but allow us to understand the data more effectively and identify trends (Figure 7). The graphs include the results for all four designs and are colour coded for the benefit of the reader.
As the toolpath variations have been explored against a set of four different pattern-based texture designs, the structural basis of these designs must be closely considered in any analysis, and each can be looked at in turn. The results for Surprise show that preferences are more aligned towards the datum as the raster angle moves away from 0° where the highest visual preferences are seen for the comparison plates. The lowest visual preference is seen for the 75° plate. The trend is not strictly linear but the trend towards preference for the datum is clear where the relative graph lines would intersect on their respective upwards and downwards trends. One notable deviation are the results for 15 ° and 30 °, which show a sharp decline and then a sharp increase in preference respectively. It is not clear why this is the case, but it is most likely due to subtle aesthetic differences leading to a subconscious judgement as the surface quality of both was very good.
A similar relationship is seen for Joy though it is not quite as pronounced and is subject to more variation against the different toolpath strategies. Generally, the datum plate was visually preferred but there were notable results for 0, 15 and 45° variations that recorded nearly half of the participants preferring. 30° recorded an unusually high number of “no preference” results leaving that relationship inconclusive. As the toolpath moves towards 75°, recorded preferences fall steeply, and the datum plate records its highest preference ratings. Why there is such a sharp drop in visual preference after a 45° raster angle is not entirely clear. The surface finish between the parts was consistent overall so is more probably connected to how the toolpath pattern interacts with the texture design. 45° and 15° respectively recorded 24/62 and 23/62 participant preferences. It can be speculated that the visual dynamics of the making process provide better visual energy, in turn enhancing the impact of the aesthetic symbolism that is evidenced the increased levels of perceived emotive intensity noted for love, joy and optimism.
Fear was built upon a cubic structural basis and the design was driven by a dynamic angularity. Considering the investigation results, there are several points of interest. Overall, the participants were drawn to the datum approach more than any other in terms of visual preference. The results for the 15° variation noted a tiny majority of one and the 75° variation recorded a just below half indicating a preference. In terms of preference trends, any relationships remain elusive and may only be established with further study. It appears that most participants aligned to a linear cutting approach as it visually compliments the cubic structure of the design. Examples such as the 60° variation, which recorded a notably low preference distribution, could be explained by a relatively poor surface quality, although this cannot account for all such examples.
On analysis of the visual preference results for Trust shows a more linear trend line with visual preference increasing as the raster angle becomes more extreme. Some of these results will have to be discussed against surface finish inadequacies, but this cannot account for the observed trends in this case. Visual preference at the 15° variation remains low, possibly owing to chatter-based machining errors, but increases steadily with the 60° and 75° variations recording majority preferences of 37/62 and 36/62 respectively. The subtle visual dynamics of the toolpath against the texture design may make these offset rastering angles more pleasing visually.
Machining strategies for tactile preferences
As the experiment was repeated is the same way and the “no preference” choices are similarly not included. Table 2 below provides the data for both the comparison plate preferences against the datum plates.
Table 2 - Participant preferences for tactile comparison of plates
![](data:image/png;base64,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)
Two graphs can again be generated from this data that can help in the identification of trends. The graphs are presented as follows and represent inversions of one another as before (Figure 8).
The variations in tactile preference present a bigger challenge to unpack given firstly the subtleties of the tactile changes and secondly the visual references that the participants also had had. The extent of the subtleties may account for the lower rate of preference variation that is seen across the two graphs above. Upon analysis, a statistically significant difference between the datum and comparison results was indicated, suggesting that most of the participants did prefer one kind of tactile interaction over another. However, the “no preference” responses were significantly higher than that of the visual preference examination indicating that tactile differences as a results of machining strategies of this scale are less detectable than visual differences. The “no preference” results are listed as follows: Surprise 73 / 372, Joy 83 / 372, Fear 97 / 372 and Trust 110 / 372.
While these results are skewed towards a preference for the datum or a “no preference” response, the most extreme toolpath preference are seen at the lower and higher ends of the raster angle spectrum. Surprise shows the highest preference ratings for 0, 15 and 60°. Joy shows a similar relationship with the highest preferences given for 15 and 30° with the preferences dropping sharply with the other variations. A relatively large number of participants (18) recorded a preference for fear at 75° and trust at 60° (16). Considering Table 2, the concentration of positive datum preferences is focused upon the central area from 15-60°. As these preferences significantly decrease around this area (indicated by red), this provides evidence that some proportion of people prefer the tactile experience of cutting angles that deviate slightly from a toolpath parallel to the workpiece edge. Interestingly, this result is echoed somewhat in the visual responses suggesting there may be an alignment between the visual and tactile preference choices although this is probably a weak relationship. Aesthetic uniformity may be another factor that influenced the responses. The trust pattern was created to be very uniform and structured, but surprise and joy less so. A tactile sense of uniformity may affect the “no preference” responses; if the pattern has a more uniform construction, tactile differences are less detectable.
Implications for design practice and manufacturing engineering
The work concluded that variances in process parameters had measurable impact on user preferences in terms of the visual and tactile domains. As this aspect of the work is of a subjective disposition and reliant on the interpretation of participants, any conclusions drawn are open to question and scrutiny. The accumulation of more data may produce clearer relationships but what can be concluded is that there are consistently a proportion of people who have aesthetic and tactile preferences with respect to toolpath. If the datum approaches represent a kind of “normality” or an orthodox technical approach and the comparison plates represent unorthodox approaches, it is clear that an orthodox approach is not always favoured with significant numbers of participants having preferences for an experiential space out with a kind of designated material-manufacture “normality”.
In essence, this points to the presence of an individuation that exists prior to the completion of the artefact as it is sold to the user. The evidence presented in this work suggests that the free modification of targeted process parameters can achieve specific experience factors. With respect to the patterning and texturing features explored in the previous sections, such features could for instance be used on housings of consumer products. Apple Mac laptops and Linn hi-fi systems utilise CNC metal machining in the construction of their products. A kind of patterning and texturing could be used to enhance the aesthetics of these artefacts and “guide” users (following affordance theory) in some ways by drawing them visually and emotively towards points of interest that may perform function or enhance product identity.
This raises the question of creating a generalizable tool; how can these new concepts of perception and experience be integrated with current production practices to achieve novel outcomes? To do this, the function and architecture of CAM systems can be re-examined. Standard CAM systems and systems of process control more generally have in-built biases towards particular kinds of manufacturing outcomes – efficient and free of “imperfections” (adhering to a Taylorist manufacturing philosophy). This bias to what Pallasma (2005/2012) called the “flatness” in modern production has led the architecture of CAM systems to limit the exploration capacity of processes and removing what can be broadly described as user-experience concepts. The diagram below shows the as-is status of CAM systems where the process has an in-built linearity with the cultural assumptions of hylomorphism (see Ainsworth, 2016). The designer wants to create something; accordingly, the processes are tailored to achieve this exact goal.
This presupposes that the in-process dynamics cannot offer anything in themselves of value out with the bounds of perfected object fabrication. As we have seen however, the intrinsic qualities of a process can introduce new and interesting properties into fabricated objects at both the level of form and perception. EdgeCAM for instance has a sophisticated rendering engine and allows the user to “see” how the part would look utilising a defined set of parameters (similar systems for additive manufacturing such as Fusion 360). As the process is simulated, interesting effects appear in the render as the simulation creates patterns. The problem is that these interesting possibilities are not highlighted by the software at all. Furthermore, CAM software does not offer any insight into UX or human factors; properties of perception and experience and not considered in reference to a simulated process (Wang, 2019). With respect to this observed short coming, a new CAM architecture is proposed and mapped out in the diagram below (Figure 10).
The new hypothetical architecture integrates human-factors options into a CAM software system and also unorthodox possibilities relating to the ductus of process. This could include perceptual information such as emotions, semantic meaning, and affordances. For example, a CAM simulation mapping the fabrication of a part through additive manufacturing could offer the user options with respect to the visual interest factors of the build. Additionally, a build could be tailored for comfort utilising texturing options. Handheld objects such as gaming controllers could be attuned for specific experiential outcomes within the CAM system, drawing on the dynamics of process ductus. This essentially reframes the CAM philosophies seen earlier as not just processes of material transformation control, but processes linked to potential perceptual experiences.