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7 / 15
Oct 2015

We are about to embark on a project to bring 3D models of molecules like proteins and DNA into science learning. Most of the objects that I have seen are single color from filament printers. Ok but much less powerful for learning than a multi-color model.

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HLpLNcWF48sL9f4Tk+v4+9WvBttaXmpo+tkvY+SFZQMl8//rrtPHPwHTTLa0ezuVISQmRJZDtZcfw56+uPet3wt8PLPSJrR9RVoYCqo0LA9TyGyefTj6etfLYnD18OqlSdNytqorqdkJQqWipJeZy/g74K2fjL4uaH4atL97LTtUuNkU2zcyLjJ4PXp3r9evgx8JtE+Cnge08OaGHaCIl5Z5TmSZz1Zq+bf2XPhLp3h3XJvEmoG1vZGQDT5mX5oc53Y9DjAr66tbtJUBRww9jXw/1/DY582Fmmuy6Nbq26ttqexGhKhpUVn+hotNwaxNcgtdWtJbS9gjubaQYeKUblIq7LN8p5rD1Cchic8U9zTRGV4d8LaD4NWVdG0y20/wAw5cwoAT9TX5/ftv211oXxPuriw3w/b41neT1bG3A/KvuXxh4pXRLBwki/bJVIhRj3x1PsK/N79p2HWtP1dtRvbia7aQndJKc4zXlVs6wmCxUMJOX7yXTql5jlgauJpOcF7qPmfVbW+Op/apJJGl3BtxJr061OlT6JJNKXllkX5SG+6a4CHxdBvlS/iBTs6Dn8RWxYiGa3Mlsd0Ui5DL0NfS06sKivE+eqwnDSS2Njw/4u1vQrRrK0maWy3FkQSEGPJ6D2r6J/Zx8b6hourtd3DM0kihVUZP4etfK2jW+pTamsdoHZc4xjrX3R+zbYaVo+kJc6pCn2gKCR6H3/ACq4zXNpuclaNo+R6/p8vifxfchpI5bS2mPySSHGQODgVsz+DvFVhKZrO9NxEozjdgtWjo3xZ0KF5RMyiMAlVQ9/asTxZ8ddP+zxLa7YlVfnfON3+P1rolSpuKdSo7+RwRlUu4wgreZZu/ijqnhqT+z763m+0KNpXYTj61u+GNT8RXd5pl/a25MU9wiOA2GjBbksD2IrlvOvbvQ7fxBLBHcRzxrgOAWIx1x+Ir0vwhq+nXuiKGl8lzyABgoR7VlSc6vuuRquWi1K3U9f3U0mszQ9cg1y1MsR+ZG2OPQ/4VoE815kk4uzPsYTU4qUdmKTQTSE03dUlik0zNBNNzSAQmmk+9GaTNIor561GDS561GDQMlBpwPpUQNODc0AS5pciog1BagCQtUbyYprSYFVZpsZoA8f/aT+IWg6b4K1fw3fJFf3+o2boLRwGVVYYDt6Y6j6V+ZDQaH4W0G40U3DSX8zExyr90HPC/SvfP2vr7xV4d+OWoztZztpWoJELacIWjKhACAfUEHIryXR/hlpvia/uY5LuRr24T5VX/lmD1598f5zX6DlOFw6w6nT96ck73dkrr9D5zFVJyqWlolt5njUVxq/i23Y2lw9z5DlFTJxkYyPyxXafDTT7zW7uSw1i9e2SIFdshPPXv8A09qwdO8G6n4W1O80yyZ7lYpTMSPlJYgDH6VaN3fHUgkm6zfjdhgcg9eRSpSlBp1m77MJW+zsZXhzxU3h/wAYXtnNK3kW07Kpc53IGOPT/OK95sfjRpyxQpHKsb8Bo1PD/Q+uOa8F+JPgy7TytXslM6EBXeJBnPqcf1rD8H+HtXvtYtlmSaCIsM5G1iOOmfatcPj8RhJ/V2ru+hU6UKkee56943+JV5/a8FtYSJc7nMibeAOBkEjHSu68AeJbbxmxk1ecC6TnyAp2ucY59O1WT8EbOO0jurefEjweV5iLlo2weeffFFl8J4fDMF1dWVzHZzsIzHGWdvmVQC2WJOWbLHtknAAAFfS4eli41VKvG8Gnfy8vmcM3TcbQep9HfC7xKPD00Wj6hC8WmuoMbE7Chzxn2z6171F4hs7G1aWGSQsvPmbiQPTk1+a3/C65fDLz2GoyxGTG1kkBGP8A6/PBH619I/BP4str3w3exvlYmQOkE4becEZX34GPwr+b/Ebw4/trESzjhqXLV3nTircz/njbaT6rrve9z7XI87+qQWGx+seku3k/Lt9x9T6F8S7W9sLp7mVGMGPmU9cnGP1FY2reI5PEMM0ltcCKKIMCoPJ4614H8J4TeeC9e0PWtR+x6/csJLS7H+rbZkhM9OTnrjOfaur+CvhXXdTN9beILryI4nIVIW+aQHnJPYV8bmWTcY1MJhsujfWPvN3jK938Unroreb1Wp6kcblFKpUxDlonot/uXqcDBf8Aivxr42igsi11DZSPFOXyREoGVJx61zHx0+Gev+LtJe1kgBhV8eYBz+FfXtn4S0vwTpd02mQCOaVvMlJO5pH9WNcfY+LrPWrxraaJBIpI6DNepQ4FlHE0sVia/wC8ileyvquvM9X9x49Til8k6WHp+5fS/b0PzU1j9kvXpkWS2u4oWJ+ZbkFR+GP8K9O0L9n7/hEdLtLIOL+UJ874xubqcDsK+xPid4csYvDt7em3acxRGRViGHOAeB+lfOnwb8b654u13VLe40mYf2fty2OzZwD74HavvqMJULUnK7XfqfO1sRKvHmtp5HNzfCLUPC0kl+9isagZ5Ocmtnwn4N8Q61ZyS6Xc7Y5s4jJ4z7HIr1L4k3L/ABH8H6n4WswBqc9u6xo2V2Ng4J9BnFebfsoeFPHXhHS9Ug8U2E9pp1lMIrb7RwSed+PVQcYPufSumM5zm6WzOaSh7NVL6Gr8MfhJ4p8T395DfTzRRo2N8eME/jXaR/sxXo1LN9fzzWyOrCJps7gDz096908K+KdF0yzjWLyllcncOhz65966aTUdKO65eVHkOQBu4A9q9WOEUoLmld9ThddxbSjY+ef2jfGmrfAr4WWr6Za/2mZJEt4VlU4hYgnLYPTCnvXnv7PH7QDeJ7S5fxBKlncJId0CEhfUEA816r8a/BniX486bc+GfCT2zqksctxcXMvlrEobI24ByeOnpmsW/wD+CdL/APCLadJo/ikWPioD/TpJFJtZc9lwNwI9ec+grnqSjSlaL2PRo4adekm0e6fArx9aeI/FGpWOnNvthbec/swYAH9TXt+a8X/Zw/Z3h+BOkXnn6o2s6zfbRPdbNiKo6Ig64yc5PX2r2bOK45zc3dnuUKXsqagKWpN1Jnmmk1mdFhSaaWpCaTHegYE880hcimk0lAysDUYalB496jBoAlBpwORUQanBjQBJupGemZprGgBsr8Vm3lzsHWrc78GsO/kPNAHz3+0N8drTwvrJ8LR+Q072pluJJQG2huAo9DjnP0r8/tT8b2tp4wuJdKndI0I/d59OOMda9e/bW+F3i61+Keo+IrSwu77R75UeO7to2kWHChSj4Hy4IOPYivj6SaXTJp2lDLOSRyCCK+ywOIo0KEI0laW7l+n6HiVoSqVG5P5HpGo/Fe60fxKb+OZQzru+RQQx6HIrhPFHj291rVReIm1SSXYLgsT1rlnu7i7uNuDI5Pyqq5Jr2zwN4Hkg0ayvNf0421ofnZJgRuHY4+lZRq18fJwg7K9xOEKCTauS/DLxd5zNb3eyXzCARNyBnoea9K8ayaNp0Gl3CHyD5imSaNhgnIBPX0J4/wD114V8Tb+w0PxZHPocu2JohuiGRtPp/XPvWBrnj661fTBazSgr1DMfnH5da61mEKCcJq849SfYue2zPseH4jaRDpkNtDOjsikKTjJY+uM/hXPar8WNKvr20tEnWNw7JIgPb1Br42PivVBEYUu5FjI28HnFLp2sXUD+YJCZB/EetdK4mrSdraPcn6jFan0R8R9G0HxVrkuoyXbi2jU4VSDl8E9MDril8A/F+Pwxb/2baNtjgl3R71+bPr35rF/Z9k0LxFqU9r4ijuryNQJPJhIXec/dZuoB5GRyK7fxf8FrHT9Tm8Q6dbw6baRqu2zWYyDdt6sZD3I7dz0HAq8K8SpPF4aHuvd9CKnJ/Dm9T0T/AIW6uvQp5QZ5uArxjDj04Hoa9h8PeNdd0fQoL+Nm+1NxJHnOQMDOe349OK+OPBvxesvDWo820S3CMckkjB6c/wD669Vtv2jbeeOGLe0aAksqYC54OBz6+1fUUcfQxEFGq0+h5lbDWd4o+oNC+PyfZt+qRTRB+VDoefb3rzK/fxX/AMJjca7ptlOdMuZC6RnoqnsfQ8dK6/8AZv1ey+Imja6t9H5ttbzxSQbgCAWB3gH8FP4mu2+LXjC48HyaFYWemsdO1CQxvclQVUAZAHuf8a/JswwtSGJVOEtE9H3NacoRUrrUofDLxLqviZLm3v8ATrq1SNtjtMCVYY6K2MV6D4bsdKiWeOwtLe0jZ8EKgUuehY4rU8J6hb3ejxo+Iw8YKp6VyWvPqljeS6pBNFHb2eASDyRjPIr0XRpwSlJXPN55t2i7Enh39n7SLDxtqfiG5u7u71G8IcbpisUKAABFUcH1yc1a+J1qNB0GcRqjRMNobrj0FWNE+KFlcWTXUs6OxwfvAAf4VwXxE+LtleMtnG8cjbg4xjjnv6U5RoUab5LJlwVSrJOWqPI7XSfF8ss1zb4MQJKqAWIrh/Gvx+1jwhfRaPdh5tQk/wBWsQ4I6ZJJ4FfUPwh8S2jRzJeSQy7G2EggjHrXlv7VXwr0LW7yx8X6RHHHf2GVuUBCrLFknPsVPP0J9K4JU5Kn7S+nU76c4ufI46mz8L/E+seB9KtdfivCZ7hBJcW8nKSr1247exFfamm3gvrG3uQComjWQKe2Rmvzz+EMesfG3VdO0rSQw022dHvbocJHEDz+J5AFfobaoIoUReFUAD6CvKbu32PpMLFqF2XAc0uaiBxTi3FI7RxNNJpM0ZzQAUE5oNMLUANzRmkzTSaAKuaYDijPvTQaAJM0uajzTs0APzTGNGajY0AQTtwaxb84BrYuDwaxNQ5U0MDmNSYHdnBFfE37X/wi8Jaj4n0q+URaXqN5HI1wIkwJQuMNgcZ5PPtX2pqRwTXw7+1Xr/8AwlPiqZbV0WPQVaEOnLO5AL/gOB7H616OXRc8Qla61ujjxTSp72Z5Ronwp03wrMdQs42nlVSoklO7t2HTmpb34kxG0uLHVAoQpiPemNnHb+Vec6J8V9R0uZg1wI2I2iR1B49MHjuffmsDx94kk8X3W6CU7cfOVAH6ivpa+MpwptYdJJ9DyYUpuXvv5nGeMruG51+4a2k8yHPDLwCe+KxAuTVhUiJIOVYGtnxP4H1rwobIarp8lmt7AtzbO2Cs0bchlYEg/wBK+Ul7zb7nrx91WMhYxjmrVvb73XaepwDUltol3foqxKS3ACgZya24PBV1pum3N3eN5TxfdiH3ge2a1hGTeiIlJJbnZ2Fy/wAO5LKdVAjmiCySoOrdefzNdVrvx5F7oE8E0jOWj2Ju55wRnH415QPGhvNK+w3cayrjAYj5gfaufaDz5CzEqo+6GNe5/aVSjSdChL3X+Hc4lQUpc01qbng2xg1/XILVZPLnuZQgMh5JJr7L8OfsZXM0OmvJq8KNPIDJvjOFTAJIHUkdOcV8gfC++tNE8e6TqU0XnQWMwncEZGBxn8CQfwr7b1/9puBbnSLqzmzCsextpzt6dcfSvMouMXzSe3RHLjPaX5YI9wHwsvfgl4RM+i3f9oadG3mXK7QkgQfeYY4OB16cVS8T/GnTdYi0a1niT7BFMGM7/MHcKduPpkn8qd8JfjhJ8SNRisLaeN2XDEs22m/tEeBJfE2kMhtxZBSDDLBgbG6dhzzXQ6lO7nHY8eNOfMoy3Ob8a/FBtDtzc2V8DABlQhyAPTrXhvjb9p7W9Z8Para6DDPcygANc8lBjOcjuOTWv4O+Cl3rTlPEOvPLZpkLGpI3D3P517XZ/BfR7XwVBZWdrbRwhGiaZIgGcZ43EDLHpya8mdeo7yjsetGjSi0mfC2h/HvxLDb/AGWVGllB4ZHKj8uatXHjHW9Xjlnv7ryDIBnY5V2x0HXgV774v+A3hv4c2t5qerYtolQyeYxGAMZ/yOtfEt9fS39/LM0jhGY7RnoKhTU3odUYeVj2PwP8d9W8NXEtqslw0RPDFiTnp3NfXnwklXxf4S1DU/EcjXVrdQNAtrg7VRhhiT6kccdK+KPhD8PNV+Jerpp+jWD3t9GpkO0gLtHdicAelfUmj/EC68EaG/hq606S1ubYiO5icAFSMdfwrVzhGL5tzCdKbknFWR9W/sm+DdJ8G+BbiLT5TLNNOWk3ptZVGQg9+Oc+pr3yF+BXz3+zJ4obxdZahfxQPHZRhIEkZcB26kD6cV79C/A5rjbTd47HuUOb2a5ty6GzS55qJW4p4NI6B+aVTTc0hOaAFc80wmlJpuaAGk80maCaaDQBUzxTAaXdxTQaAH5p2ajzS7qAHZpjng0pNRyNQBWnbg1jX54Nas7daxtQfANJgc1qh618C/tVfC3XvBWuah4q0rzLvRL6ZppxHy0DtyQw/uk9DX3rqT5zXJazaw39vNb3MSTwSqUeORdysp6gitqFeeHlzwZjUpqorM/IefSE8QS+XYxySXRJZrdFLPjqSAM8da6Dw1ZaTpNu63iP9pXnDLjn0r9DfC3wZ8G+AdWudS0PQ4LK9nzmYZYqD1C5ztHsKwvip8DfDfxQtGF1D9gvxkrfWihZCf8Aa/vD611RxUFK7ic7w8nHRn526loD6jqFzcwWxMLncDgDjFWLWDVtcisrC7vLi5sbEFbaCaQssKk8hATxnA6V9ID9kbxVY3ptbfWNOl03dgTvvVwv+7jr+NYv7Q/hK4+Glz4XtbKzLaLa2H2dbxI/mM24szMR3JOfxreM6EqkUtbmbjUUG2cf4I8KDRpnLx7zJkYbqDjjH6fWui17wnYatotzucRygFdw+7uyev6/lXnq+Mb2yu45LsNGWUOm8EeYtZl94+vZ7tooHYeZ8pjHA5r14zoQg9TktNs4rULVNLv54DiSWJiuF6E5qW9spY7GOaSBjGxHz44Fdno/ws1nXNVlluEEbsd53kc5/wDrV1+peG3i0R7BoMxICjPtwAc15EaXPFyZ0udmeq/Cj9kXwt4u+H+m62PENzLe3dvvcWhQxox/hwRnjpzXgFld3fgLxjc6dqVpIy28zQyW8o6EHGcGvVf2PfFlx4W+Jtx4dku2Gn38bgQsflMq8ggdjgGuL+Nl7rXijx5q+uX8X7ozvDAUjAVYlYheR1471yJSU2pPQqpyOCa3PffCfihfC+lWHinTbBBbxENL9mUhtp4yfpXS/En9rhLrws32C2kv70KNkKZBz/tHHGPpWD+yFYJ4h8KfZ9RlWe3Z3QQy8jbnHSvdvGPwX0HQPDN/qOhaZbG48ony8DLnrgema6XCaptQV0eJzwVX3t0fC+gftE6pe6mEuVa03P8AMqOSv0GelfWfw6/aI0m7XT9OnkCLjlJOSfrXx/8AEH4W3ng77RrO2MtM5Yoo4jZj0H0zU/gD4Z+MfFNut5oenyyuFLiSRSqMR2DkgZ9q5YzUVdHdKkqrsj7f/ag0fR/in8JdQ0zSpANV2Rz28URDGRlYMF9881+ftp8DvF93OYotA1CRw20gW7cH8q+tvgF4H8axa6l/4ps2sra2GUWaTczt0GBngDrX0za8EY/Ss51U5e6kjsw+GahaTZ4p+xn8ENV+FumahqmtR/Zb3UFVFtj95EBzlvQn0r1/xT+z74e8d+JBrFzNc20sm37RHAQFmwMdxwcV1dj2rptNTIFc797c71CKXKa3hLQ7Dw1pNtp2m2yWlnAu1I4xgD3+tdRCeKx7FcAVrQHAFUaFxDUgNQIakBoAfuo3U3ikJoAcWpM5puaN2KAGk80maaTmkz70AVM8U0NTQ2aaGoAlzS7qjDUZ5oAkLVE7UpaonPU0AV5261i6gMg1sSnIrKvFyDSYHK6ivWubvl5NdbqKDB4rmL9OTSA565XrWfKOa17mPrWdNGeagaKDcmq11aQXsRiuIY54z1SVQw/I1dkTHWoWXk0DPN/iZqHgTS4Le28R6Vb6g6riO3jtRLJGvTIx90V8T/HPQ9P8K+L5D4X3nR541lhdvmK5HKnIyMHsa+j/AIt6Vcr8V22ziQ3Fuk0ce7BQKMEc/TNYesy24EFrPbw3CXRIJlUENgnJzjp+fSvo8Ll8qlKNSEt+h5lWslJqS2PH/h74quJbSM3EroCu2Tp82K9H1XWdGubVYYJ2uGKZ3uGVtx5IIzjIrE8deFft9nbRaJbRwNDjdJbrtCggEfhWJZ/D28062B1K6khnk5RWIB2+tdrUqa9nJapHInrodJ+zR8PpPFvxQudbMnk2mlsZQAOWY5Cgfqfwr0DX/stqk/hbWbLyrxT5ayMBtcE/K4Poa6P9mK0tNLh161hX99vidnPORgjH5g/nV/46eCpPFer6VHZHZfSwupdeoCsCp/NjXzk5P2vKzrnBex5jxL4VaveeCvH13o9verBZsrMpckDORx+tfWGm+Kb6WyjF7fKS0W1yp4Cg4yenPPXFfBvxH8P+IvBniTyrjc06NkTKvJFez+DJ9M0P4cR+IvGOtX8V5IWa30qG4VDOg4XjG4A89+layqST93Y4oUY1Frueta9/wjeoaRPNfwxXUUDb5Yn5RlU5xj8K734a+K9C8X+G4Lnw+qwWUY8v7MIwhhP93aOlfHngqW58bx3qTXE1vYJllhLkswJ6E9/yr6Z/Zx8EyeHvDl3eSZQX0oMcRHRVyAfxzXntcrcWduHbvpsewW6YIrWtEyRgVUtoK2bK35FJHomjYREEV1GmxdKyLC25BNdHYxbQOKoDWtBjFaUVULYYFXozVAWUNSA55qBTTw1AEuaQmmbqTdQA4tSFuKaTxTS1ACFqM03NNoApqcZpM8U0HikBoAkDUob3qPdRnigCTPFRMeKUn9ajY0ARSGs66GQa0HPWqc65FJgc7qCZzXO3sOc11t5DkGsS6tSc8UgOUngPpVCW26mumns854qpJp5POKVgOYltznpVSSH24rpbiy2jpWbPbYzxUjueC/G/4F3nxF1Gz1bR9Sj0/UoI/KZbgHy3XOQcjJBGa851b9lnxZHoq3Fv4mgvNUhBZbQxssfrhXJ6/UAV9ZSw9eKrSRba7KeLr0oqMJWRjKjCb5mj4U0TxXe+Bbq503XbZ7W+UkSRSrtK5649q6Zrmy8QaRLdGQySKowZG2hMY+7nk96+nfF3w58OeOISmtaTb3rbdizMuJUHs45FeRN+ymtpq4Nh4inh0bdk2sse6QL3AbIH44r3KWZ0qkeXEK3p1OKeFnF3hqaX7M1q40fWb6RCvnXIjViOoUdvxNdh4+1STRNe0m7SMMs0bw7scqQQf612OlaNa6PYQ2dnCsFvCoVEQY/yazPGfhQeK9I+zJN9mu4m8yCfGdre47gjivnude05lsdk6TlScOp478XfDH/CYwQ3pQeYDtyOCeOo/SvFdF8AQp4hNtrU7yxhNylnwNvYDNe8f8IJ47u737BNEohRuLxXURkevXd+GK9G8M/BTRrR0u9UiXVb8KF3yj5FHoF7/jWsqqta55sMLProec/B34aRX2rxSJFI+lxjLyMPlOOiivpyxsFgiWOJAiKMKqjAAqLTdPitIUhgiSGJRhURcAD2ArobK1GOlcm7uenTgqasiO2tMHpW1ZW3Tii2s8444rXtbYAjiqNSxZW+MVt2ybcVTtocYrRhXGKaAuQjGKtJVaMdKsIaYEwOacGqIHinZoAfmjNMzmjNADi1NLUhNJmgAzSZpM0m7HagDPDcUgamZoBoAlzR1pg+tLnmgBSTSE0hPakzQA1zmq8i1OTUbDNAGdPFntWfPbA9q2ZEyKryRA1IGBLZA9qrSWftW9JBmq0kHWgDmrmzyDxWNd2oTPFdlPbbh71j3thuPX9KQHIzQ4PQVTnhJPp2rfubIqT7e1UJLSpAxmhPpTfIPpWobXB5pUtcnpmgdzKMFAirVeyxzmovsu2gLlaGE8fyrTtoOR2qOGIA9K0rZMkfLQIt2Vtkjit+0tgMcVRsY8AZWty3QYHFWBYt4fatK3hHHFVYhgDArRgHHSgCzAmKuRiq8dWUqgJ4zxUymoU7VKpoAkBpQeKjBpc0ASbqCeOKYDgUm6gB2cUmeaaWpM0ALmgmkpMZ7GgDMBpd3tRRQAoal3YFFFABu/Ck3CiigBpNMJoooAjbFRMKKKkCF1GKrSJRRQBVlSqNxECDRRQBlXFpk9Kzp7HriiigCo1rjtTo7aiikAktvgdKqmDJoooYEkVv3xWnY2xLgkUUUAbdvABjI5rVt4sAUUUwL0SfMvar0QAoooAtRmp1NFFUBMrU8MPWiigB27ijfRRQAbqN2aKKADNGaKKADNJ+NFFAH//Z)

LEFT: This Shapeways multi-color sandstone model of DNA would be great but which printer was it made on and is this machine affordable? RIGHT: Model of an HIV protein by biologicmodels.

Which printers are able to produce models with 2+ filament colors NOT separated horizontally by layer? Layering does not make sense for us since it’s hardly ever feasible in biological models. I have found a couple of interesting dual or even triple extrusion pictures (Felix3, Protos X400, CubeX Trio 1, 2). Now, I’m wondering what Hubbers would recommend and what experiences you have?

Ultimaker2 seems to have the option for a 2nd extruder and is well liked here. On the other hand, CubeX has 3 extruders already onboard but scores a low 6.2 on 3DHubs. FlashForge Creater Pro (dual) looks promising.

For a full color range one probably has to change printing method to powder (CubeJet not yet available and company doesn’t reply, X1 $12000 but company email bounces) or paper (Iris available but €13000 and a huge machine). Are there any other full color printers out there?

What would you recommend to generate multi-color science models for teaching? Would be great to hear from you.

All the best

  • created

    Oct '15
  • last reply

    Oct '15
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The full color sandstone models are usually made on the 3D Systems Projet series. The “entry-level” machine Projet 260 offers basic CMY color channel range and costs about 30’000€.

I believe 3dsystems has some nice full color printers that are now old enough that you can get used ones at a very reasonable price. They are quite complicated to learn compared to say an Ultimaker but they do full color. They use sand or something like that - it’s not FDM printing.

If it were me I would print in any color and then just paint them afterwards.

The only multi color FDM prints I like are water cooled heads. They are amazingly slow - when you have 6 colors per layer and you have to switch heads and let it heat up plus a wipe then that can be an extra 30 seconds per color per layer. That adds up!