A STEP AP224 agent-based early manufacturability assessment environment using XML

Early design assessment activities have a significant impact on reducing the cost of manufacture. The efficient utilisation of the product development time and the level of coordination of the early design activities between the major stakeholders are becoming key factors for success in manufacturing today. The paper reports on a new distributed early design manufacturability assessment methodology using collaborative autonomous agents. A product data model compliant with STEP AP224 and corresponding process and facility data models are proposed to support the decision-making process. XML was used as a medium for exchange of requests and information between design, manufacturing, and facility planning agents. A prototype Web-enabled system for rapid product manufacturability assessment in the extended enterprise has been developed using distributed multi-agent CORBA objects. The reported research is a step towards the development of a generic prototype tool for collaborative design evaluation and rapid assessment of the manufacturing feasibility and resource availability at different stages of the product development process.     

A multi-agent architecture for plug and produce on an industrial assembly platform

Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.     

An advanced FEA based force induced error compensation strategy in milling

The study introduces a multi-level machining error compensation approach focused on force-induced errors in machining of thin-wall structures. The prediction algorithm takes into account the deflection of the part in different points of the tool path. The machining conditions are modified at each step when the cutting force and deflection achieve a local equilibrium. The machining errors are predicted using a theoretical flexible force-deflection model. The error compensation is based on optimising the tool path taking into account the predicted milling error. The error compensation scheme is simulated using NC simulation package and is experimentally verified.     

Machining simulation and system integration combining FE analysis and cutting mechanics modelling

This work presents a method for machining complex three-dimensional surfaces using only one axis of controlled motion for positioning a cutting tool on a specially designed numerically controlled (NC) machine. This single controlled axis lathe is configured like a lathe, but is used to produce complex sculptured surfaces out of wood. This is accomplished by mechanically linking two axes of motion to produce a fixed helical footprint of a tool path with constant step-over distance. As the linked axes are rotated, their location is measured by an encoder and passed directly to a personal computer (PC). Software running on the PC determines the depth of the computer-controlled axis. The depth information is used to control the depth axis. Several test pieces have been machined out of cedar for evaluating the method.     

Automated experience-based learning for plug and produce assembly systems

This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.     

Distributed product and facility prototyping in extended manufacturing enterprises

In order to reduce the cost at the early product development stages, the planners need methodologies and tools that would allow them to judge upon the implications of the product design on the required manufacturing processes and facilities for their production. This paper reports on a new theoretical platform and a pilot implementation of a decision-making environment for distributed product and facility prototyping in an extended enterprise. The approach is based on an exchange of requests and information between collaborative autonomous agents that support the design, manufacturing planning and facility formation activities. The decision-making is formalized as iterative matching of design, process and facility attributes using multilevel resource capability representation within the extended enterprise. The system is implemented as an XML/CORBA-based environment for conveying design and manufacture messages across traditional technology boundaries. The reported research aims to provide the designers with a rapid manufacturing feasibility assessment tool to be used at different design and planning stages in extended manufacturing enterprises.     

Review of semantic modelling technologies in support of virtual factory design

Comprehensive data models and standardised terminologies are required across tools and their developers to rapidly design and prototype digitised virtual factories. Also, various types of software are required to be integrated for cost-effective modelling exercises. To meet this requirement, semantic modelling technologies involving ontologies have featured prominently in many manufacturing applications. This paper considers, extensively, the current semantic requirements for digitised virtual factory design and prototyping and compares them with the capabilities of existing semantic technologies. As a result, a review of methodologies, tools and languages for creating ontologies has been reported. The review concludes with the next generation requirements for methodologies, tools and languages for creating ontologies suitable for the virtual factory design process, and further explains ongoing work associated with the creation of ontologies and reasoning mechanisms through the integrated use of ObjectLogic, OntoStudio and OntoBroker.     

Mechanical issues of Cu-to-Cu wire bonding

Cu-to-Cu wire bonding provides benefits both from economical and from electrical point of view. However, since Cu is harder than Al or Au, it is expected to induce higher mechanical stresses in the substrate and more bonding problems during and/or after wire bonding. There are three steps during bonding: bond forming, ultrasonic vibration, and cooling down. In order to understand their physical nature, different techniques are applied. The bond forming process is simulated using finite element analysis (FEA) and the stress generated at the last stage (when the wire ball reaches its final shape) is presented. The grain distortion in the Cu wire bond after bonding is studied using scanning electron microscopy and orientation imaging microscopy. After ultrasonic vibration, the whole structure cools down. Due to the difference in coefficient of thermal expansion between Cu and Si, extra stress is built up. The final residual stress is measured by Raman spectroscopy. The result is compared with FEA and an excellent agreement has been achieved. The impact on the substrate by the capillary tool is clearly shown. The result also shows that the maximum tensile stress in the substrate is located near the edge of the pad and depends highly on the positioning of the wire bond.     

Preparation and properties ofX-sialon

X-sialon has been produced by hot pressing Si₃N₄-Al₂O₃-SiO₂ powder mixtures and Si₃N₄ Powder-mullite gel mixtures at 1650 °C. The formation mechanism ofX-sialon has been studied and is correlated with the processing technique for the two preparation routes. Microprobe analysis of the obtainedX-sialon phase, combined with previous observed compositions and literature formulae suggests that, at 1650 °C,X-sialon exists as a narrow solid solubility region on the Si₃N₄-mullite line in the Si₃N₄-SiO₂-Al₂O₃-AIN phase diagram. The physical, mechanical and chemical properties ofX-sialon have been evaluated.X-sialon has a modest hardness of 1280 kg mm^–2, a fracture toughness of 1.7 Mpa m^1/2, and an elastic modulus of 213 GPa.X-sialon exhibits excellent chemical stability in contact with iron-based alloys at 1200 °C.