Hybrid Evolutionary Shape Manipulation for Efficient Hull Form Design Optimisation

‘Eco-friendly shipping’ and fuel efficiency are gaining much attention in the maritime industry due to increasingly stringent environmental regulations and volatile fuel prices. The shape of hull affects the overall performance in efficiency and stability of ships. Despite the advantages of simulation-based design, the application of a formal optimisation process in actual ship design work is limited. A hybrid approach which integrates a morphing technique into a multi-objective genetic algorithm to automate and optimise the hull form design is developed. It is envisioned that the proposed hybrid approach will improve the hydrodynamic performance as well as overall efficiency of the design process

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

INTELLIGENT OPTIMAL TOOL SELECTIONS FOR CNC PROGRAMMING OF MACHINE TOOLS

The aim of this paper is to improve CNC programming of machine tools and to minimize the need for human intervention or manual inputs. Proposed optimization method of tool selection is based on GA. Experimental approval is done using Matlab environment and shown, that proposed method is useful for optimization of cutting tool selection. This approach is going to more and more sophisticated, so called intelligent way of CNC programming

INTELLIGENT OPTIMAL TOOL SELECTIONS FOR CNC PROGRAMMING OF MACHINE TOOLS

The aim of this paper is to improve CNC programming of machine tools and to minimize the need for human intervention or manual inputs. Proposed optimization method of tool selection is based on GA. Experimental approval is done using Matlab environment and shown, that proposed method is useful for optimization of cutting tool selection. This approach is going to more and more sophisticated, so called intelligent way of CNC programming

CNC PROGRAM AND PROGRAMMING OF CNC MACHINE

CNC Programming (Computer Numerical Control Programming) is the art of programming CNC machines to make parts. An NC Program consists of a sequence of instructions that control the motion and automatic sequences of an NC Machine. In general sense, the term NC programming refers to the creation of control data for machining work-pieces on NC and CNC machines. The development of CNC systems has progressed as a result of the rapidly improving capabilities, coupled with falling prices, of small computers, a combination that makes the standard computer an attractive component of NC systems. NC programming has a decisive influence on the cost effectiveness and profitability of NC manufacturing. The selection of a programming system is essentially guided by the need for software that is suitable for the application at hand, readily available, and as universally applicable as possible

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin