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Quality function deployment (QFD) is a cross‐functional planning tool which ensures that the voice of the customer is systematically deployed throughout the product planning and design stages. One of the common mistakes in QFD is to perform analysis using an inconsistent house of quality (HOQ) chart. An inconsistent HOQ chart is one in which the information from the roof matrix is inconsistent with that from the relationship matrix. This paper develops a systematic procedure to check the consistency of information contained in an HOQ chart. The proposed consistency check can be performed prior to QFD’s main analysis to ensure the validity of the final results. A procedure for identifying the source of the inconsistency, if the HOQ chart should fail the consistency test, is also developed. The proposed procedures are illustrated through examples.

Poly(styrenesulphonic acid)‐doped polyaniline has been synthesised and the influence of this polymeric compound on the inhibition of corrosion of mild steel in 1M HCl has been investigated using weight loss measurements, galvanostatic polarisation studies, electropermeation studies and a.c. impedance measurements. The polymer acts predominantly as an anodic inhibitor. Hydrogen permeation studies and a.c. impedance measurements clearly indicate a very effective performance of the compound as a corrosion inhibitor. The adsorption of the compound on the mild steel surface obeys Temkin's adsorption isotherm.

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.