Computational issues in the modelling of materials-based manufacturing processes

The manufacture of materials products involves the control of a range of interacting physical phenomena. The material to be used is synthesised and then manipulated into some component form. The structure and properties of the final component are influenced by both interactions of continuum-scale phenomena and those at an atomistic-scale level. Moreover, during the processing phase there are some properties that cannot be measured (typically the liquid-solid phase change). However, it seems there is a potential to derive properties and other features from atomistic-scale simulations that are of key importance at the continuum scale. Some of the issues that need to be resolved in this context focus upon computational techniques and software tools facilitating: (i) the multiphysics modeling at continuum scale; (ii) the interaction and appropriate degrees of coupling between the atomistic through microstructure to continuum scale; and (iii) the exploitation of high-performance parallel computing power delivering simulation results in a practical time period. This paper discusses some of the attempts to address each of the above issues, particularly in the context of materials processing for manufacture.

Uncertainty analysis to minimise risk in designing micro-electronics manufacturing processes

A design methodology based on numerical modelling, integrated with optimisation techniques and statistical methods, to aid the process control of micro and nano-electronics based manufacturing processes is presented in this paper. The design methodology is demonstrated for a micro-machining process called Focused Ion Beam (FIB). This process has been modelled to help understand how a pre-defined geometry of micro- and nano- structures can be achieved using this technology. The process performance is characterised on the basis of developed Reduced Order Models (ROM) and are generated using results from a mathematical model of the Focused Ion Beam and Design of Experiment (DoE) methods. Two ion beam sources, Argon and Gallium ions, have been used to compare and quantify the process variable uncertainties that can be observed during the milling process. The evaluations of the process performance takes into account the uncertainties and variations of the process variables and are used to identify their impact on the reliability and quality of the fabricated structure. An optimisation based design task is to identify the optimal process conditions, by varying the process variables, so that certain quality objectives and requirements are achieved and imposed constraints are satisfied. The software tools used and developed to demonstrate the design methodology are also presented.

Manufacturing knowledge verification in design support systems

This paper identifies the need for a verification methodology for manufacturing knowledge in design support systems; and proposes a suitable methodology based on the concept of ontological commitment and the PSL ontology (ISO/CD18629). The use of the verification procedures within an overall system development methodology is examined, and an understanding of how various categories of manufacturing knowledge (typical to design support systems) map onto the PSL ontology is developed. This work is also supported by case study material from industrial situations, including the casting and machining of metallic components. The PSL ontology was found to support the verification of most categories of manufacturing knowledge, and was shown to be particularly suited to process planning representations. Additional concepts and verification procedures were however needed to verify relationships between products and manufacturing processes. Suitable representational concepts and verification procedures were therefore developed, and integrated into the proposed knowledge verification methodology.

Multi-physics modelling - A vital component of virtual manufacturing

One of the core tasks of the virtual-manufacturing environment is to characterise the transformation of the state of material during each of the unit processes. This transformation in shape, material properties, etc. can only be reliably achieved through the use of models in a simulation context. Unfortunately, many manufacturing processes involve the material being treated in both the liquid and solid state, the trans-formation of which may be achieved by heat transfer and/or electro-magnetic fields. The computational modelling of such processes, involving the interactions amongst various interacting phenomena, is a consider-able challenge. However, it must be addressed effectively if Virtual Manufacturing Environments are to become a reality! This contribution focuses upon one attempt to develop such a multi-physics computational toolkit. The approach uses a single discretisation procedure and provides for direct interaction amongst the component phenomena. The need to exploit parallel high performance hardware is addressed so that simulation elapsed times can be brought within the realms of practicality. Examples of Multiphysics modelling in relation to shape casting, and solder joint formation reinforce the motivation for this work.

Risk mitigation framework for a robust design process

The increasing complexity of new manufacturing processes and the continuously growing range of fabrication options mean that critical decisions about the insertion of new technologies must be made as early as possible in the design process. Mitigating the technology risks under limited knowledge is a key factor and major requirement to secure a successful development of the new technologies. In order to address this challenge, a risk mitigation methodology that incorporates both qualitative and quantitative analysis is required. This paper outlines the methodology being developed under a major UK grand challenge project - 3D-Mintegration. The main focus is on identifying the risks through identification of the product key characteristics using a product breakdown approach. The assessment of the identified risks uses quantification and prioritisation techniques to evaluate and rank the risks. Traditional statistical process control based on process capability and six sigma concepts are applied to measure the process capability as a result of the risks that have been identified. This paper also details a numerical approach that can be used to undertake risk analysis. This methodology is based on computational framework where modelling and statistical techniques are integrated. Also, an example of modeling and simulation technique is given using focused ion beam which is among the investigated in the project manufacturing processes.

Manufacturing of uniformly-sized silicon particles for solar cell from molten metal jet by electromagnetic pinch force

Spherical silicon solar cells are expected to serve as a technology to reduce silicon usage of photovoltaic (PV) power systems[1, 2, 3]. In order to establish the spherical silicon solar cell, a manufacturing method of uniformly sized silicon particles of 1mm in diameter is required. However, it is difficult to mass-produce the mono-sized silicon particles at low cost by existent processes now. We proposed a new method to generate liquid metal droplets uniformly by applying electromagnetic pinch force to a liquid metal jet[4]. The electromagnetic force was intermittently applied to the liquid metal jet issued from a nozzle in order to fluctuate the surface of the jet. As the fluctuation grew, the liquid jet was broken up into small droplets according to a frequency of the intermittent electromagnetic force. Firstly, a preliminary experiment was carried out. A single pulse current was applied instantaneously to a single turn coil around a molten gallium jet. It was confirmed that the jet could be split up by pinch force generated by the current. And then, electromagnetic pinch force was applied intermittently to the jet. It was found that the jet was broken up into mono-sized droplets in the case of a force frequency was equal to a critical frequency[5], which corresponds to a natural disturbance wave length of the jet. Numerical simulations of the droplet generation from the liquid jet were then carried out, which consisted of an electromagnetic analysis and a fluid flow calculation with a free surface of the jet. The simulation results were compared with the experiments and the agreement between the two was quite good.

Modelling of dosator operation for improved manufacturing performance

Purpose: To develop an improved mathematical model for the prediction of dose accuracy of Dosators - based upon the geometry of the machine in conjunction with measured flow properties of the powder. Methods: A mathematical model has been created, based on a analytical method of differential slices - incorporating measured flow properties. The key flow properties of interest in this investigation were: flow function, effective angle of wall friction, wall adhesion, bulk density, stress ratio K and permeability. To simulate the real process and (very importantly) validate the model, a Dosator test-rig has been used to measure the forces acting on the Dosator during the filling stage, the force required to eject the dose and the dose weight. Results: Preliminary results were obtained from the Dosator test-rig. Figure 1 [Omitted] shows the dose weight for different depths to the bottom of the powder bed at the end of the stroke and different levels of pre-compaction of the powder bed. A strong influence over dose weight arising from the proximity between the Dosator and the bottom of the powder bed at the end of the stroke and the conditions of the powder bed has been established. Conclusions: The model will provide a useful tool to predict dosing accuracy and, thus, optimise the future design of Dosator based equipment technology – based on measured bulk properties of the powder to be handled. Another important factor (with a significant influence) on Dosator processes, is the condition of the powder bed and the clearance between the Dosator and the bottom of the powder bed.