This posting presents the analysis of results from the Robust Parameter Design Experiment introduced in the previous posting. The five factors are A = CO_{2} pressure (bar), B = CO_{2} temperature ^{o}C, C = peanut moisture (% by wt), D = CO_{2} flow rate (liters/min), and E = peanut particle size (mm). The factor E is the noise factor). The purpose of the experiment is to show the effects of these factors on Solubility, S, or mg of oil removed from the peanuts. Continue reading Analysis of a Combined-Array Design in A Robust Parameter Experiment

# Tag Archives: Designed Experiment

# Estimating Interaction Effects using a Combined-Array Design in Robust Parameter Experiments

This posting presents another example illustrating the advantage of combined-array experiments in Robust Parameter Designs over the Taguchi crossed-array designs. Kilgo (1988) presents an example of a 2^{5-1} fractional factorial experiment providing data to construct a model for estimating the mean response. We modify the use of the experiment to make it relevant to a Robust Parameter Design experiment. Continue reading Estimating Interaction Effects using a Combined-Array Design in Robust Parameter Experiments

# Combined Array Designs in Robust Parameter Experiments

This posting introduces the use combined arrays in Robust Parameter Designs. Combined array designs have both controllable and noise factors in the same experimental design. The previous posting describes Taguchi Parameter Designs using crossed arrays consisting of an inner array containing the controllable factors and an outer array containing the noise factors. Continue reading Combined Array Designs in Robust Parameter Experiments

# Crossed Array Design Problems in Robust Parameter Experiments

This posting describes problems in using Taguchi Parameter designs with crossed arrays in Robust Parameter Designs. The two previous postings describe an application of Taguchi Parameter Designs to reduce plasma cutter cycle time. That is, the Taguchi Design posting and the Taguchi Results posting. The crossed array designs proposed by Taguchi when used with the maximum allowable factors can’t estimate the interaction effects among the controllable factors. Continue reading Crossed Array Design Problems in Robust Parameter Experiments

# Plasma Cutter Cycle Time Experimental Design Results

This post describes the results and their analysis of the experimental design to reduce plasma cutter cycle time. The experimental design is a Taguchi Parameter Design. The previous posting describes the experimental design, and refers to the Value Stream Map Case Study posting to review the Lean Six Sigma project that produced the experimental design. Continue reading Plasma Cutter Cycle Time Experimental Design Results

# Design of Experiments: Grinding Process Example (Part 4)

This posting continues the grinding process case study (Gigo, 2005) that illustrates the use of design and analysis of experiments to reduce common-cause variation. Continue reading Design of Experiments: Grinding Process Example (Part 4)

# Design of Experiments: Grinding Process Example (Part 3)

This posting continues the grinding process case study (Gigo, 2005) that illustrates the use of design and analysis of experiments to reduce common-cause variation. Continue reading Design of Experiments: Grinding Process Example (Part 3)

# Design of Experiments: Grinding Process Example (Part 2)

This posting continues the grinding process case study (Gigo, 2005) that illustrates the use of design and analysis of experiments to reduce common-cause variation. Continue reading Design of Experiments: Grinding Process Example (Part 2)

# Design of Experiments: Grinding Process Example (Part 1)

This posting describes a grinding process case study to illustrate the use of design and analysis of experiments to study cause and effect and reduce common-cause variation. Continue reading Design of Experiments: Grinding Process Example (Part 1)

# Cause and Effect Diagram

The Cause and Effect Diagram graphically portrays the potential causes of an effect. Continue reading Cause and Effect Diagram