An activity-based-parametric hybrid cost model to estimate the unit cost of a novel gas turbine component

The first tool presented in this paper is a generic factory cost model that can estimate various costs at multiple levels of any manufacturing plant. The model is activity based which means that the cost of each manufacturing operation is calculated and then summed up so that the true £-per-hour factory cost rate as well as the exact unit cost (i.e. manufacturing cost) of an unlimited number of different components can be estimated.The second tool is a scalable cost model that predicts the unit cost of future integrally bladed disc (blisk) designs that are found in gas turbine compressors. The tool multiplies the machine cost rates, calculated by the factory cost model, by the operation times derived from blisk scaling rules. As the operation times often depend on the number of blades, the disc diameter and other design variables, many scaling rules are based on the correlation between operation times and certain design parameters. Conversely, the remaining process times are constant because they are independent of the blisk geometry. As future process times can only be estimated and the correlation between operation times and design parameters is never perfect, all operation times have uncertainty distributions. These are cascaded through the model to generate a probability distribution of the unit cost.Through the interactive exchange of detailed cost information at the manufacturing operation level as well as extrapolated operation times, the two cost models facilitate design and manufacturing engineering to concurrently optimise blisk designs and manufacturing processes in terms of cost

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Archives of Data Science, Series A. Vol. 1,1: Special Issue: Selected Papers of the 3rd German-Polish Symposium on Data Analysis and Applications

The first volume of Archives of Data Science, Series A is a special issue of a selection of contributions which have been originally presented at the {\em 3rd Bilateral German-Polish Symposium on Data Analysis and Its Applications} (GPSDAA 2013). All selected papers fit into the emerging field of data science consisting of the mathematical sciences (computer science, mathematics, operations research, and statistics) and an application domain (e.g. marketing, biology, economics, engineering)

Soft computing for tool life prediction a manufacturing application of neural - fuzzy systems

Tooling technology is recognised as an element of vital importance within the manufacturing industry. Critical tooling decisions related to tool selection, tool life management, optimal determination of cutting conditions and on-line machining process monitoring and control are based on the existence of reliable detailed process models. Among the decisive factors of process planning and control activities, tool wear and tool life considerations hold a dominant role. Yet, both off-line tool life prediction, as well as real tune tool wear identification and prediction are still issues open to research. The main reason lies with the large number of factors, influencing tool wear, some of them being of stochastic nature. The inherent variability of workpiece materials, cutting tools and machine characteristics, further increases the uncertainty about the machining optimisation problem. In machining practice, tool life prediction is based on the availability of data provided from tool manufacturers, machining data handbooks or from the shop floor. This thesis recognises the need for a data-driven, flexible and yet simple approach in predicting tool life. Model building from sample data depends on the availability of a sufficiently rich cutting data set. Flexibility requires a tool-life model with high adaptation capacity. Simplicity calls for a solution with low complexity and easily interpretable by the user. A neural-fuzzy systems approach is adopted, which meets these targets and predicts tool life for a wide range of turning operations. A literature review has been carried out, covering areas such as tool wear and tool life, neural networks, frizzy sets theory and neural-fuzzy systems integration. Various sources of tool life data have been examined. It is concluded that a combined use of simulated data from existing tool life models and real life data is the best policy to follow. The neurofuzzy tool life model developed is constructed by employing neural network-like learning algorithms. The trained model stores the learned knowledge in the form of frizzy IF-THEN rules on its structure, thus featuring desired transparency. Low model complexity is ensured by employing an algorithm which constructs a rule base of reduced size from the available data. In addition, the flexibility of the developed model is demonstrated by the ease, speed and efficiency of its adaptation on the basis of new tool life data. The development of the neurofuzzy tool life model is based on the Fuzzy Logic Toolbox (vl.0) of MATLAB (v4.2cl), a dedicated tool which facilitates design and evaluation of fuzzy logic systems. Extensive results are presented, which demonstrate the neurofuzzy model predictive performance. The model can be directly employed within a process planning system, facilitating the optimisation of turning operations. Recommendations aremade for further enhancements towards this direction

Intelligent Machining Systems

Machining is one of the most widespread manufacturing processes and plays a critical role in industries. As a matter of fact, machine tools are often called mother machines as they are used to produce other machines and production plants. The continuous development of innovative materials and the increasing competitiveness are two of the challenges that nowadays manufacturing industries have to cope with. The increasing attention to environmental issues and the rising costs of raw materials drive the development of machining systems able to continuously monitor the ongoing process, identify eventual arising problems and adopt appropriate countermeasures to resolve or prevent these issues, leading to an overall optimization of the process. This work presents the development of intelligent machining systems based on in-process monitoring which can be implemented on production machines in order to enhance their performances. Therefore, some cases of monitoring systems developed in different fields, and for different applications, are presented in order to demonstrate the functions which can be enabled by the adoption of these systems. Design and realization of an advanced experimental machining testbed is presented in order to give an example of a machine tool retrofit aimed to enable advanced monitoring and control solutions. Finally, the implementation of a data-driven simulation of the machining process is presented. The modelling and simulation phases are presented and discussed. So, the model is applied to data collected during an experimental campaign in order to tune it. The opportunities enabled by integrating monitoring systems with simulation are presented with preliminary studies on the development of two virtual sensors for the material conformance and cutting parameter estimation during machining processes