Hefin Rowlands

Methods and techniques to help quality function deployment (QFD)

Quality function deployment (QFD) is a management tool that provides a visual connective process to help teams focus on the needs of the customers throughout the total development cycle of a product or process. It provides the means for translating customer needs into appropriate technical requirements for each stage of a product/process‐development life‐cycle. It helps to develop more customer‐oriented, higher‐quality products. While the structure provided by QFD can be significantly beneficial, it is not a simple tool to use. This article outlines how techniques such as fuzzy logic, artificial neural networks, and the Taguchi method can be combined with QFD to resolve some of its drawbacks, and proposes a synergy between QFD and the three methods and techniques reviewed.

OPTIMIZING THE PARAMETERS OF MULTILAYERED FEEDFORWARD NEURAL NETWORKS THROUGH TAGUCHI DESIGN OF EXPERIMENTS

The size and training parameters of artificial neural networks have a critical effect on their performance. This paper presents the application of the Taguchi Design of Experiments (DoEs) off-line quality control method in the optimization of the design parameters of a neural network. Being a ‘parallel’ approach, the method offers considerable benefits in time and accuracy when compared with the conventional serial approach of trial and error. The use of the Taguchi method ensures that the quality of the neural network is taken into account at the design stage. The interpretation of the experimental results is based on the statistical technique known as analysis of variance (ANOVA). The signal-to-noise ratio (S/N) is used in designing a robust neural network that is less sensitive to noise. The effect of design parameters and neural network behaviour are also revealed as a result. Although a Wood Veneer Inspection Neural Network (WVINN) is the particular application presented here, the design methodology can be applied to neural networks in general. Copyright

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision-making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright

Evolutionary Artificial Neural Network Design and Training for wood veneer classification

This study addresses the design and the training of a Multi-Layer Perceptron classifier for identification of wood veneer defects from statistical features of wood sub-images. Previous research utilised a neural network structure manually optimised using the Taguchi method with the connection weights trained using the Backpropagation rule. The proposed approach uses the evolutionary Artificial Neural Network Generation and Training (ANNGaT) algorithm to generate the neural network system. The algorithm evolves simultaneously the neural network topology and the weights. ANNGaT optimises the size of the hidden layer(s) of the neural network structure through genetic mutations of the individuals. The number of hidden layers is a system parameter. Experimental tests show that ANNGaT produces highly compact neural network structures capable of accurate and robust learning. The tests show no differences in accuracy between neural network architectures using one and two hidden layers of processing units. Compared to the manual approach, the evolutionary algorithm generates equally performing solutions using considerably smaller architectures. Moreover, the proposed algorithm requires a lower design effort since the process is fully automated.

Application of design of experiments to a spot welding process

Design of experiments (DOE) is a scientific method for identifying the critical parameters associated with a process and thereby determining the optimal settings for these process parameters for enhanced performance and capability. It is a powerful technique employed in the improvement phase of the Six Sigma methodology as a means to improve process yield, stability, customer satisfaction and reduce process variability which results in high scrap, rework and warranty costs. This paper presents the application of DOE to a spot welding process in order to discover the key process parameters, which influence the tensile strength of welded joints. Statistical analysis was carried out to identify the process parameters, which affect the mean strength and variability in weld strength. The results of the study encouraged the engineering team within the company to extend the application of DOE to other core processes within the business as a process performance improvement technique.

An application of experimental design for process optimisation

Dr Taguchi is a Japanese engineer and an international quality consultant who has made breakthrough improvements in product and process quality through the use of statistical design of experiments (SDOE). The Taguchi method became popular in the West in the 1980s as a means to design robust products and processes. Although many companies and industries have used the method with success, the real benefits of the approach were not realised and fully understood in many cases. This lack of success could be attributed to a number of factors, but mainly because the experiments were treated in isolation and not integrated into a continuous improvement strategy. This paper briefly presents the results of the application of the Taguchi methodology in the UK industry. The paper also illustrates the application of the Taguchi method for optimising the production process of retaining a metal ring in a plastic body in a braking system.

Lean Six Sigma: an integrated strategy for manufacturing sustainability

Both Lean and Six Sigma are key business process strategies which are employed by companies to enhance their manufacturing performance. However, whilst there is significant information available on implementing Lean Six Sigma in companies, these business improvement strategies are still implemented primarily in a sequential manner and the results of the survey included in this paper identify the fact that little information exists regarding the integration of these approaches to provide a single and highly effective strategy for change in companies. This paper introduces an integrated approach to Lean Six Sigma and proposes a Lean Six Sigma strategy for industry as a result of the information collected from a survey of 100 manufacturing companies which apply Business Process Improvement (BPI) initiatives (Lean, Six Sigma or Lean Six Sigma). The rationale is that the effective implementation of Lean Six Sigma will lead to greater opportunities for companies to achieve economic sustainability through continued growth and improved manufacturing efficiency.

A novel NN-fuzzy-SPC feedback control system

It is a difficult challenge to develop a feedback control system for statistical process control (SPC) because there is no effective method that can be used to accurately calculate the magnitude of the feedback control actions in traditional SPC. Suitable feedback adjustments are normally generated from the experiences of process engineers. In this paper, fuzzy logic and neural network (NN) techniques are used to develop a NN-fuzzy-SPC control system. The fuzzy inference is used to generate the numeric feedback control actions and the neural network optimises the fuzzy membership functions in order to increase the control accuracy. A combined forecaster with EWMA chart and digital filtering is also developed for the NN-fuzzy-SPC system to reduce the control delay. Simulation results show that the NN-fuzzy-SPC system can provide high control accuracy and satisfactorily short control delay.

Expert systems and quality tools for quality improvement

This paper reflects on how expert systems along with quality tools can be used for quality improvement. The paper gives the background information on expert systems and on quality tools in order to show how they can be combined to build a quality improvement system. The way in which quality tools can fit into the plan-do-check-act cycle, and how this cycle can be integrated in the knowledge base of an expert system shell is also explored. Finally, the possible advantages such a quality information system could bring to companies are considered.

The meaning of ‘value’ in purchasing, logistics and operations management

We investigate the current conception of ‘value’ within the production-oriented disciplines of purchasing and supply, logistics and operations management. The research design entails a four-step content analysis of peer-reviewed journal articles drawn from these disciplines. Based upon the application of this method, we found that the conception of value within these production-oriented disciplines is subject to a considerable interpretive viability issue. Our findings suggest four contributory factors to this issue: a lack of theoretical rigour; a lack of definitional rigour; a plethora of [undefined] value-related terms in circulation within the field; and a large variety of linguistic usages of these terms, with ‘value’ being used as a verb, adjective and noun. This has profound implications as it throws into question much extant research in this field. How, for example, is it possible to effectively study something that is inadequately defined? It also has significant practical implications, as how is it possible to design an effective or efficient production system or supply chain premised upon the principle of value if none of the actors have a common understanding or consensus of what value is?