An intelligent knowledge based cost modelling system for innovative product development

This research work aims to develop an intelligent knowledge-based system for product cost modelling and design for automation at an early design stage of the product development cycle, that would enable designers/manufacturing planners to make more accurate estimates of the product cost. Consequently, a quicker response to customers’ expectations. The main objectives of the research are to: (1) develop a prototype system that assists an inexperienced designer to estimate the manufacturing cost of the product, (2) advise designers on how to eliminate design and manufacturing related conflicts that may arise during the product development process, (3) recommend the most economic assembly technique for the product in order to consider this technique during the design process and provide design improvement suggestions to simplify the assembly operations (i.e. to provide an opportunity for designers to design for assembly (DFA)), (4) apply a fuzzy logic approach to certain cases, and (5) evaluate the developed prototype system through five case studies. The developed system for cost modelling comprises of a CAD solid modelling system, a material selection module, knowledge-based system (KBS), process optimisation module, design for assembly module, cost estimation technique module, and a user interface. In addition, the system encompasses two types of databases, permanent (static) and temporary (dynamic). These databases are categorised into five separate groups of database, Feature database, Material database, Machinability database, Machine database, and Mould database. The system development process has passed through four major steps: firstly, constructing the knowledge-based and process optimisation system, secondly developing a design for assembly module. Thirdly, integrating the KBS with both material selection database and a CAD system. Finally, developing and implementing a ii fuzzy logic approach to generate reliable estimation of cost and to handle the uncertainty in cost estimation model that cannot be addressed by traditional analytical methods. The developed system has, besides estimating the total cost of a product, the capability to: (1) select a material as well as the machining processes, their sequence and machining parameters based on a set of design and production parameters that the user provides to the system, and (2) recommend the most economic assembly technique for a product and provide design improvement suggestion, in the early stages of the design process, based on a design feasibility technique. It provides recommendations when a design cannot be manufactured with the available manufacturing resources and capabilities. In addition, a feature-by-feature cost estimation report was generated using the system to highlight the features of high manufacturing cost. The system can be applied without the need for detailed design information, so that it can be implemented at an early design stage and consequently cost redesign, and longer lead-time can be avoided. One of the tangible advantages of this system is that it warns users of features that are costly and difficult to manufacture. In addition, the system is developed in such a way that, users can modify the product design at any stage of the design processes. This research dealt with cost modelling of both machined components and injection moulded components. The developed cost effective design environment was evaluated on real products, including a scientific calculator, a telephone handset, and two machined components. Conclusions drawn from the system indicated that the developed prototype system could help companies reducing product cost and lead time by estimating the total product cost throughout the entire product development cycle including assembly cost. Case studies demonstrated that designing a product using the developed system is more cost effective than using traditional systems. The cost estimated for a number of products used in the case studies was almost 10 to 15% less than cost estimated by the traditional system since the latter does not take into consideration process optimisation, design alternatives, nor design for assembly issue

A review of additive manufacturing technology and cost estimation techniques for the defence sector

“Additive Manufacturing” (AM) is a promising technology which will provide major advantages to Defence Support Service providers, given its ability of delocalised manufacturing near the point of use. The technology is gaining increasing interest due to its disruptive potential. AM groups together a wide range of different approaches which have the ability to convert a 3D file into a physical product by depositing layer upon layer of material. AM is still under development and considered an immature technology. This immaturity provides high level of uncertainty around key indicators such as time and cost. These indicators represent also key decision variables to evaluate AM and compare it with traditional manufacturing. This review paper represents an investigation of existing knowledge on AM and aims to present to the reader the various AM approaches with a detailed focus on the most applicable technologies to Defence Support Services. The paper is structured as follows, firstly the various technologies of AM and their economic aspects are presented, secondly the cost modelling techniques are investigated and finally a discussion is carried out. The contribution of this paper is to present to Defence Support Service stakeholders the various AM technologies and cost modelling techniques for measuring the product or service cost

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Design Transactions

Design Transactions presents the outcome of new research to emerge from ‘Innochain’, a consortium of six leading European architectural and engineering-focused institutions and their industry partners. The book presents new advances in digital design tooling that challenge established building cultures and systems. It offers new sustainable and materially smart design solutions with a strong focus on changing the way the industry thinks, designs, and builds our physical environment. Divided into sections exploring communication, simulation and materialisation, Design Transactions explores digital and physical prototyping and testing that challenges the traditional linear construction methods of incremental refinement. This novel research investigates ‘the digital chain’ between phases as an opportunity for extended interdisciplinary design collaboration. The highly illustrated book features work from 15 early-stage researchers alongside chapters from world-leading industry collaborators and academics

Design Transactions: Rethinking Information for a New Material Age

Design Transactions presents the outcome of new research to emerge from ‘Innochain’, a consortium of six leading European architectural and engineering-focused institutions and their industry partners. The book presents new advances in digital design tooling that challenge established building cultures and systems. It offers new sustainable and materially smart design solutions with a strong focus on changing the way the industry thinks, designs, and builds our physical environment. Divided into sections exploring communication, simulation and materialisation, Design Transactions explores digital and physical prototyping and testing that challenges the traditional linear construction methods of incremental refinement. This novel research investigates ‘the digital chain’ between phases as an opportunity for extended interdisciplinary design collaboration. The highly illustrated book features work from 15 early-stage researchers alongside chapters from world-leading industry collaborators and academics

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller