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| Response Surface Methodology (RSM) Aplikasi Industri× | Reka Bentuk Komposit Berpusat× | |
|---|---|---|
| Bidang | Reka Bentuk Eksperimen | Reka Bentuk Eksperimen |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1951 (origin); widespread industrial adoption from 1980s onward | 1951 |
| Pengasas≠ | George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues | George E. P. Box and K. B. Wilson |
| Jenis≠ | Empirical optimization technique | Response surface experimental design |
| Sumber perintis≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916018 | Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45. DOI ↗ |
| Alias | Industrial RSM, RSM for manufacturing, process optimization RSM, industrial response surface analysis | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Berkaitan≠ | 5 | 3 |
| Ringkasan≠ | Industrial Applications Response Surface Methodology (RSM) applies the classical Box-Wilson response surface framework to manufacturing and process engineering problems. It builds an empirical polynomial model linking controllable process inputs — such as temperature, pressure, feed rate, or catalyst concentration — to one or more quality responses, then mathematically locates the input settings that optimize those responses. It is the de-facto standard statistical tool for process characterization and optimization in chemical, mechanical, food, materials, and pharmaceutical manufacturing. | Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing. |
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