เปรียบเทียบวิธี

	การประยุกต์ใช้ในภาคอุตสาหกรรม: ระเบียบวิธีพื้นผิวการตอบสนอง	Box-Behnken Design
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1951 (origin); widespread industrial adoption from 1980s onward	1960
ผู้ริเริ่ม	George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues	George E. P. Box and Donald W. Behnken
ประเภท	Empirical optimization technique	Response surface design (incomplete three-level factorial)
แหล่งต้นตำรับ	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., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.
ชื่อเรียกอื่น	Industrial RSM, RSM for manufacturing, process optimization RSM, industrial response surface analysis	BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial design
ที่เกี่ยวข้อง	5	3
สรุป	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.	The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test.