|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4952967||1364505||2017||10 صفحه PDF||سفارش دهید||دانلود کنید|
- Discarding diagonal components in WT significantly reduces data amount.
- The proposed method outperforms a reversible method by 25-35% in size reduction.
- Most of computational time is consumed by the reversible compression step.
- The proposed method compresses 5122 models in 20Â ms, 81922 models in 7Â s.
Most of workpiece shapes in NC milling simulations are in Z-map representations that require a very large amount of data to precisely hold a high resolution model. An irreversible compression algorithm for Z-map models using a two-dimensional Haar wavelet transform is proposed to resolve this tight memory situation for an ordinary PC. A shape model is first transformed by using Haar wavelet to build a wavelet synopsis tree while the maximum errors caused by virtually truncating high-frequency components are simultaneously calculated. The total amount of the shape data can be reduced by truncating particular sections of the wavelet components that satisfy the error threshold given by the user. Our algorithm guarantees that any error due to its irreversible compression processes is smaller than the specified level measured against the original model. A series of experiments were conducted using an Apple iMac with a 3.2Â GHz CPU and 8Â GB of memory. The experiments were performed with 16 sample shape models on 512Ã512 to 8192Ã8192 grids to evaluate the compression efficiency of the proposed method. Experimental results confirmed that our compression algorithm requires approximately 20-30Â ms for 512Ã512 models and 7Â s for 8192Ã8192 models under a maximum error level of 10ÃÂ 10â6Â m (a typical criteria for NC milling simulations). The compressed binaries outputted by the proposed method are generally 25-35% smaller than the baseline results by gzip, one of common reversible compression libraries, while these two methods require almost the same level of computational costs.
Journal: Journal of Computational Design and Engineering - Volume 4, Issue 3, July 2017, Pages 238-247