Process Selection Using Variation and Cost Relations

Variations are inherent in all manufacturing processes and can significantly affect the quality of a final assembly, particularly in multistage assembly systems. Existing research in variation management has primarily focused on incorporating GD&T factors into variation propagation models in order to predict product quality and allocate tolerances. However, process induced variation, which has a key influence on process planning, has not been fully studied. Furthermore, the link between variation and cost has not been well established, in particular the effect that assembly process selection has on the final quality and cost of a product. To overcome these barriers, this paper proposes a novel method utilizing process capabilities to establish the relationship between variation and cost. The methodology is discussed using a real industrial case study. The benefits include determining the optimum configuration of an assembly system and facilitating rapid introduction of novel assembly techniques to achieve a competitive edge.

Banana and abaca fiber-reinforced plastic composites obtained by rotational molding process

Natural fibers can be used in rotational molding process to obtain parts with improved mechanical properties. Different approaches have been followed in order to produce formulations containing banana or abaca fiber at 5% weight, in two- and three-layer constructions. Chemically treated abaca fiber has also been studied, causing some problems in processability. Fibers used have been characterized by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), optical microscopy, and single-fiber mechanical tests. Rotomolded parts have been tested for tensile, flexural, and impact properties, demonstrating that important increases in elastic modulus are achieved with these fibers, although impact properties are reduced. © 2013 Copyright Taylor and Francis Group, LLC.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Characterisation of fluidised bed granulation processes using in situ Raman spectroscopy

Previous work by the authors Walker et al. [2007b. Fluidised bed characterisation using Raman spectroscopy: applications to pharmaceutical processing. Chemical Engineering Science 62, 3832–3838] illustrated that Raman spectroscopy could be used to provide 3-D maps of the concentration and chemical structure of particles in motion in a fluidised bed, within a relatively short (120 s) time window. Moreover, we reported that the technique, as outlined, has the potential to give detailed in-situ information on how the structure and composition of granules/powders within the fluidised bed (dryer or granulator) vary with the position and evolve with time. In this study we extended the original work by shortening the time window of the Raman spectroscopic analysis to 10 s, which has allowed the in-situ real-time characterisation of a fluidised bed granulation process. Here we show an important new use of the technique which allows in-situ measurement of the composition of the material within the fluidised bed in three spatial dimensions and as a function of time. This is achieved by recording Raman spectra using a probe positioned within the fluidised bed on a long-travel x–y–z stage. In these experiments the absolute Raman intensity is used to provide a direct measure of the amount of any given material in the probed volume, i.e. a particle density. Particle density profiles have been calculated over the granulation time and show how the volume of the fluidised bed decreases with an increase mean granule size. The Raman spectroscopy analysis indicated that nucleation/coalescence in this co-melt fluidised hot melt granulation system occurred over a relatively short time frame (t<30 s). The Raman spectroscopic technique demonstrated accurate correlation with independent granulation experiments which provided particle size distribution analysis. The similarity of the data indicates that the Raman spectra accurately represent solids ratios within the bed, and thus the techniques quantitative capabilities for future use in the pharmaceutical industry.

A work-based research assessment of the impact of 'lean manufacturing' on health and safety education within an SME

Globalisation has had a major impact on the engineering industry as pacific Rim countries undercut manufacturing costs and provide a more cost-effective location for many businesses. Engineering in Nortehrn Ireland has mostly declined owing to increased competition from these countries. Engineering companies are now forced to streamline their production processes and employ cost-reducing practices in order to meet customer demands at reduced prices. This article aims to analyse the effects of one such streamlining endeavour which was first introduced after World War II in Japan- 'lean manufacturing' . 'Lean manufacturing' aims to reduce all wasteful activities within the production process in order to improve productivity, while reducing manufacturing costs. The work-based project under consideration was concerned with the impact 'lean manufacturing' may have on health and safety performance and education within an engineering company. The focus of the project was to determine through work-based research, and quantitative analysis, the employee perception on health and safety: has it changed (either positively or negatively), as a consequence of implementing 'lean manufacturing'.

A virtual inspection framework for precision manufacturing of aerofoil components

The finite element method plays an extremely important role in forging process design as it provides a valid means to quantify forging errors and thereby govern die shape modification to improve the dimensional accuracy of the component. However, this dependency on process simulation could raise significant problems and present a major drawback if the finite element simulation results were inaccurate. This paper presents a novel approach to assess the dimensional accuracy and shape quality of aeroengine blades formed from finite element hot-forging simulation. The proposed virtual inspection system uses conventional algorithms adopted by modern coordinate measurement processes as well as the latest free-form surface evaluation techniques to provide a robust framework for virtual forging error assessment. Established techniques for the physical registration of real components have been adapted to localise virtual models in relation to a nominal Design Coordinate System. Blades are then automatically analysed using a series of intelligent routines to generate measurement data and compute dimensional errors. The results of a comparison study indicate that the virtual inspection results and actual coordinate measurement data are highly comparable, validating the approach as an effective and accurate means to quantify forging error in a virtual environment. Consequently, this provides adequate justification for the implementation of the virtual inspection system in the virtual process design, modelling and validation of forged aeroengine blades in industry.

Digital Methods for Process Development in Manufacturing and Their Relevance to Value Driven Design

This paper examines the applicability of a digital manufacturing framework to the implementation of a Value Driven Design (VDD) approach for the development of a stiffened composite panel. It presents a means by which environmental considerations can be integrated with conventional product and process design drivers within a customized, digital environment. A composite forming process is used as an exemplar for the work which creates a collaborative environment for the integration of more traditional design drivers with parameters related to manufacturability as well as more sustainable processes and products. The environmental stakeholder is introduced to the VDD process through a customized product/process/resource (PPR) environment where application specific power consumption and material waste data has been measured and characterised in the process design interface. This allows the manufacturing planner to consider power consumption as a concurrent design driver and the inclusion of energy as a parameter in a VDD approach to the development of efficiently manufactured, sustainable transport systems.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. © 2013 IEEE.

Molecular dynamics simulation (MDS) to study nanoscale machining processes

Molecular Dynamics Simulations (MDS) are constantly being used to make important contributions to our fundamental understanding of material behaviour, at the atomic scale, for a variety of thermodynamic processes. This chapter shows that molecular dynamics simulation is a robust numerical analysis tool in addressing a range of complex nanofinishing (machining) problems that are otherwise difficult or impossible to understand using other methods. For example the mechanism of nanometric cutting of silicon carbide is influenced by a number of variables such as machine tool performance, machining conditions, material properties, and cutting tool performance (material microstructure and physical geometry of the contact) and all these variables cannot be monitored online through experimental examination. However, these could suitably be studied using an advanced simulation based approach such as MDS. This chapter details how MD simulation can be used as a research and commercial tool to understand key issues of ultra precision manufacturing research problems and a specific case was addressed by studying diamond machining of silicon carbide. While this is appreciable, there are a lot of challenges and opportunities in this fertile area. For example, the world of MD simulations is dependent on present day computers and the accuracy and reliability of potential energy functions [109]. This presents a limitation: Real-world scale simulation models are yet to be developed. The simulated length and timescales are far shorter than the experimental ones which couples further with the fact that contact loading simulations are typically done in the speed range of a few hundreds of m/sec against the experimental speed of typically about 1 m/sec [17]. Consequently, MD simulations suffer from the spurious effects of high cutting speeds and the accuracy of the simulation results has yet to be fully explored. The development of user-friendly software could help facilitate molecular dynamics as an integral part of computer-aided design and manufacturing to tackle a range of machining problems from all perspectives, including materials science (phase of the material formed due to the sub-surface deformation layer), electronics and optics (properties of the finished machined surface due to the metallurgical transformation in comparison to the bulk material), and mechanical engineering (extent of residual stresses in the machined component) [110]. Overall, this chapter provided key information concerning diamond machining of SiC which is classed as hard, brittle material. From the analysis presented in the earlier sections, MD simulation has helped in understanding the effects of crystal anisotropy in nanometric cutting of 3C-SiC by revealing the atomic-level deformation mechanisms for different crystal orientations and cutting directions. In addition to this, the MD simulation revealed that the material removal mechanism on the (111) surface of 3C-SiC (akin to diamond) is dominated by cleavage. These understandings led to the development of a new approach named the “surface defect machining” method which has the potential to be more effective to implement than ductile mode micro laser assisted machining or conventional nanometric cutting.

Progressing from internal to supply chain level environmental strategies: The influence of internal support processes

The need for companies to consider the environmental impact of their operations and supply chains has been highlighted in the literature. However, few studies appear to consider how companies’ progress from proactive environmental strategies implemented at the internal, operations level to proactive environmental strategies implemented at the supply chain level. This study assesses the implementation process through the lens of the natural resource-based view and dynamic capabilities perspective. First, the link between the internal strategy ‘pollution prevention’ and the supply chain strategy ‘process stewardship’ is assessed. Second, the mediating influence of the internal support processes ‘integration’ and ‘learning’ on the implementation process is considered. Data collected from a sample of 1200 UK-based food manufacturing companies is analysed using multiple regression analysis. The findings suggest that the progression to environmental efforts at the supply chain level begins with internally-based environmental efforts and that the integration of these efforts and experience gained from them are important supporting factors in this progression.

Enhancing the Manufacturing Knowledge of Undergraduate Engineering Students: A Case Study of a Design-Build-Test Challenge Involving Folding Bicycles

Many engineers currently in professional practice will have gained a degree level qualification which involved studying a curriculum heavy with mathematics and engineering science. While this knowledge is vital to the engineering design process so also is manufacturing knowledge, if the resulting designs are to be both technically and commercially viable.
The methodology advanced by the CDIO Initiative aims to improve engineering education by teaching in the context of Conceiving, Designing, Implementing and Operating products, processes or systems. A key element of this approach is the use of Design-Built-Test (DBT) projects as the core of an integrated curriculum. This approach facilitates the development of professional skills as well as the application of technical knowledge and skills developed in other parts of the degree programme. This approach also changes the role of lecturer to that of facilitator / coach in an active learning environment in which students gain concrete experiences that support their development.
The case study herein describes Mechanical Engineering undergraduate student involvement in the manufacture and assembly of concept and functional prototypes of a folding bicycle.