Wen Feng Lu

Automating knowledge acquisition for constraint‐based product configuration

Purpose – Product configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.Design/methodology/approach – Given a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product compon...

Efficient Web log mining for product development

With the new global economy, manufacturing companies are focusing their efforts on the product development process which is fast emerging as a new competitive weapon. Several product development solutions allow engineers, suppliers, business partners and even customers to collaborate throughout the entire product lifecycle via the Internet. To gain an additional edge over competitors, it is vital that companies utilize Web logs to discover hidden knowledge about trends and patterns in such a cyberworld. However, existing Web log mining techniques are not designed for web logs generated by product data management processes. In this paper, we propose a method termed Product Development Miner (PDMiner) to mine such Web logs efficiently and effectively using a trie structure and sequential mining techniques. Experiments involving real Web logs show that PDMiner is both fast and practical.

Analysis of Time-to-Failure Data with Weibull Model in Product Life Cycle Management

In remanufacturing practices, understanding and communicating the failure risk and reliability of a critical part, component or subsystem plays a crucial role as it has a significant impact on the lifecycle management of the product and thus determines the success of the remanufacturing process. In this respect, statistical time-to-failure analysis provides a very powerful and versatile analytical tool for reliability analysis and risk assessment. Among various statistical tools available, Weibull model which uses time series data on records of failure incidents of a product for the fitting of a parametric distribution, is a powerful approach to characterizing the time-to-failure probability function of the product. It is able to provide valuable information for optimized lifecycle management and remanufacturing process. This paper demonstrates successful applications of Weibull model for time-to-failure analysis using case studies in remanufacturing practices and proposes a statistical approach to assess the reliability of critical parts and components for remanufacturing in the product’s lifecycle management. It is envisaged that the research results are able to benefit remanufacturing practices in many ways such as reducing warranty loss by minimizing probability of failure of remanufactured critical parts and components.

Enabling Mass Customization through Semantic Web Services

In order to satisfy individual customer needs with near mass production efficiency, product manufacturers need to effectively collaborate with their dynamic value chain partners along the product lifecycle process. A primary need is to develop semantic interpretations related to business processes and documents to all value chain partners. We propose a semantic Web service oriented framework to fulfil the task using OWLS. We present a product family ontology and sketches of two core services: Product configuration and product lifecycle cost estimation. We believe that with the benefits of rich semantic descriptions from OWLS, effective value chain integration can be developed-this is the key to successful mass customization.

Parameterless Data Compression and Noise Filtering Using Association Rule Mining

The explosion of raw data in our information age necessitates the use of unsupervised knowledge discovery techniques to understand mountains of data. Cluster analysis is suitable for this task because of its ability to discover natural groupings of objects without human intervention. However, noise in the data greatly affects clustering results. Existing clustering techniques use density-based, grid-based or resolution-based methods to handle noise but they require the fine-tuning of complex parameters. Moreover, for high-dimensional data that cannot be visualized by humans, this fine-tuning process is greatly impaired. There are several noise/outlier detection techniques but they too need suitable parameters. In this paper, we present a novel parameterless method of filtering noise using ideas borrowed from association rule mining. We term our technique, FLUID (Filtering Using Itemset Discovery). FLUID automatically discovers representative points in the dataset without any input parameter by mapping the dataset into a form suitable for frequent itemset discovery. After frequent itemsets are discovered, they are mapped back to their original form and become representative points of the original dataset. As such, FLUID accomplishes both data and noise reduction simultaneously, making it an ideal preprocessing step for cluster analysis. Experiments involving a prominent synthetic dataset prove the effectiveness and efficiency of FLUID.

Deriving configuration knowledge and evaluating product variants through intelligent techniques

Mass customization has become a crucial business strategy for product manufacturers that aims at satisfying individual customer needs with near mass production efficiency. Companies must develop the necessary infrastructure to derive valid product configurations that satisfy the requirements of lifecycle cost along with customer’s constraints. In this paper, to overcome the drawback of current product configurators, we apply a rule mining approach to automatically generate configuration knowledge, and present a hybrid approach based on Activity Based Costing (ABC) and machine learning techniques to estimate LCC of derived product variants from a constraintbased configurator at the design stage. The proposed intelligent techniques would benefit companies in enhancing product development capability in a shorter lifecycle.

A mathematical model for surface roughness of ship hull grit blasting

Surface cleaning and blasting for the ship hull of oil tankers and passenger ships are conventional operations in a ship yard with surface roughness requirements. Several process parameters affect the blasting quality outcome, such as the distance between the blasting nozzles and ship hull, feed rate of copper grit, grit size, etc. In this paper, a mathematical model is derived to describe the relationship between several input parameters and blasting quality. In addition, a blasting experiment is designed using the Taguchi method in order to reduce the number of experiments required for validating the proposed model. Due to resource and time constraints, the experiments will be carried out as future work.

Randomized K-d tree ReliefF algorithm for feature selection in handling high dimensional process parameter data

In complex manufacturing processes, large amounts of process parameters are monitored and recorded, creating a high-dimensional and heterogonous data warehouse. In order to improve process yield and ensure product quality, comprehensive knowledge of the process should be acquired and critical features should be identified. However, in modern industry production, big data has become quite common; online monitoring and prediction is also usually required. Therefore, an effective feature selection algorithm for industry applications should be fast and robust. Traditional feature selection methods often fails to deal with such demands very well. In this paper, a modified approach for feature selection based on ReliefF is proposed for modelling and analysis of complex manufacturing processes, with improved speed and stability. Randomized k-d tree search is introduced to speed up the feature selection algorithm. The proposed method is also tested with two datasets from real industry process.

Identification and reconstruction of complex weld geometry based on modified entropy

In this paper, a modified entropy-based algorithm is proposed for identification and reconstruction of a complex weld geometry. The edge of the weld geometry is identified based on minimizing a modified entropy-type cost function, and the weld geometry is reconstructed based on the detected edge. In addition, the volume of the weld geometry is computed using the point cloud samples of the identified weld geometry, and the effects of Gaussian noise are also considered. Our simulation results using the proposed reconstruction algorithm demonstrate efficient identification and reconstruction of a complex weld geometry in the presence of Gaussian noise.

Critical component life prediction and cost estimation for decision support in remanufacturing

In remanufacturing industry, an efficient and effective decision making system is always needed to decide whether a used component should be repaired or replaced. The principle issue is to design a decision making strategy that can accommodate various application scenarios. In this paper, a generic framework is proposed for decision making in remanufacturing. The proposed approach is characterized by its ability to assist decision making when component condition is in the gray area, i.e., the condition is close to specification yet not exceeded. A case study based on cylinder bore in diesel engines shows that this framework is able to predict the remaining life of used components and give decision advice based on life-cycle cost estimation. This decision making system is also featured by its ability to improving the physical model based on the knowledge learned from real data. For example, in the case of cylinder bores, the wear rate is often associated with the specific operating conditions of the engines. In this case study, it is shown that this framework could regulate the parameters of the physical model according to data from different application scenarios and therefore offers better performances in decision making.