Comparison of the measurement performance of high precision multi-axis metal cutting machine tools

High precision manufacturers continuously seek out disruptive technologies to improve the quality, cost, and delivery of their products. With the advancement of machine tool and measurement technology many companies are ready to capitalise on the opportunity of on-machine measurement (OMM). Coupled with business case, manufacturing engineers are now questioning whether OMM can soon eliminate the need for post-process inspection systems. Metrologists will however argue that the machining environment is too hostile and that there are numerous process variables which need consideration before traceable measurement on-the-machine can be achieved. In this paper we test the measurement capability of five new multi-axis machine tools enabled as OMM systems via on-machine probing. All systems are tested under various operating conditions in order to better understand the effects of potentially significant variables. This investigation has found that key process variables such as machine tool warm-up and tool-change cycles can have an effect on machine tool measurement repeatability. New data presented here is important to many manufacturers whom are considering utilising their high precision multi-axis machine tools for both the creation and verification of their products.

International technology collaboration for new product development

In recent years it has become increasingly common for companies to improve their competitiveness and find new markets by extending their operations through international new product development collaborations involving technology transfer. Technology development, cost reduction and market penetration are seen as the foci in such collaborative operations with the aim being to improve the competitive position of both partners. In this paper the case of technology transfer through collaborative new product development in the machine tool sector is used to provide a typical example of such partnerships. The research evidence on which the paper is based includes longitudinal case studies and questionnaire surveys of machine tool manufacturers in both countries. The specific case of a UK machine tool company and its Chinese partner is used to provide a specific example of the operational development of a successful collaboration. The paper concludes that a phased co-ordination of commercial, technical and strategic interactions between the two partners is essential for such collaborations to work. In particular, the need to transfer marketing know-how is emphasised, having been identified as an area of weakness among technology acquirers in China.

Reliability:its implications in production systems design

Research on production systems design has in recent years tended to concentrate on ‘software’ factors such as organisational aspects, work design, and the planning of the production operations. In contrast, relatively little attention has been paid to maximising the contributions made by fixed assets, particularly machines and equipment. However, as the cost of unproductive machine time has increased, reliability, particularly of machine tools, has become ever more important. Reliability theory and research has traditionally been based in the main on electrical and electronic equipment whereas mechanical devices, especially machine tools, have not received sufficiently objective treatment. A recently completed research project has considered the reliability of machine tools by taking sample surveys of purchasers, maintainers and manufacturers. Breakdown data were also collected from a number of engineering companies and analysed using both manual and computer techniques. Results obtained have provided an indication of those factors most likely to influence reliability and which in turn could lead to improved design and selection of machine tool systems. Statistical analysis of long-term field data has revealed patterns of trends of failure which could help in the design of more meaningful maintenance schemes.

Business implications of manufacturing innovation : the experience of increasing automation in smaller to medium sized companies.

Despite the considerable potential of advanced manufacturing technologies (AMT) for improving the economic performance of many firms, a growing body of literature highlights many instances where realising this potential has proven to be a more difficult task than initially envisaged. Focussing upon the implementation of new manufacturing technologies in several smaller to medium sized enterprises (SME), the research examines the proposition that many of these problems can be attributed in part to inadequate consideration of the integrated nature of such technologies, where the effects of their implementation are not localised, but are felt throughout a business. The criteria for the economic evaluation of such technologies are seen as needing to reflect this, and the research develops an innovative methodology employing micro-computer based spreadsheets, to demonstrate how a series of financial models can be used to quantify the effects of new investments upon overall company performance. Case studies include: the development of a prototype machine based absorption costing system to assist in the evaluation of CNC machine tool purchases in a press making company; the economics and strategy of introducing a flexible manufacturing system for the production of ballscrews; and analysing the progressive introduction of computer based printing presses in a packaging and general print company. Complementary insights are also provided from discussion with the management of several other companies which have experienced technological change. The research was conducted as a collaborative CASE project in the Interdisciplinary Higher Degrees Scheme and was jointly funded by the SERC and Gaydon Technology Limited and later assisted by PE-Inbucon. The findings of the research shows that the introduction of new manufacturing technologies usually requires a fundamental rethink of the existing practices of a business. In particular, its implementation is seen as ideally needing to take place as part of a longer term business and manufacturing strategy, but that short term commercial pressures and limited resources often mean that firms experience difficulty in realising this. The use of a spreadsheet based methodology is shown to be of considerable assistance in evaluating new investments, and is seen as being the limit of sophistication that a smaller business is willing to employ. Several points for effective modelling practice are also given, together with an outline of the context in which a modelling approach is most applicable.

State intervention in technology in the post war years: case studies in technology policy

This thesis is concerned with the means by which the state in Britain has attempted to influence the technological development of private industry in the period 1945-1979. Particular emphasis is laid on assessing the abilities of technology policy measures to promote innovation. With that objective, the innovation literature is selectively reviewed to draw up an analytical framework to evaluate the innovation content of policy (Chapter 2). Technology policy is taken to consist of the specific measures utilised by government and its agents that affect the technological behaviour of firms. The broad sweep of policy during the period under consideration is described in Chapter 3 which concentrates on elucidating its institutional structure and the activities of the bodies involved. The empirical core of the thesis consists of three parallel case studies of policy toward the computer, machine tool and textile machinery industries (Chapters 4-6). The studies provide detailed historical accounts of the development and composition of policy, relating it to its specific institutional and industrial contexts. Each reveals a different pattern and level of state intervention. The thesis concludes with a comparative review of the findings of the case studies within a discussion centred on the arguments presented in Chapter 2. Topics arising include the state's differential support for the range of activities involved in innovation, the location of state-funded R&D, the encouragement of supplier-user contact, and the difficulties raised in adoption and diffusion.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.

Modelling the dynamics of cutting during turning

This thesis presents an approach to cutting dynamics during turning based upon the mechanism of deformation of work material around the tool nose known as "ploughing". Starting from the shearing process in the cutting zone and accounting for "ploughing", new mathematical models relating turning force components to cutting conditions, tool geometry and tool vibration are developed. These models are developed separately for steady state and for oscillatory turning with new and worn tools. Experimental results are used to determine mathematical functions expressing the parameters introduced by the steady state model in the case of a new tool. The form of these functions are of general validity though their coefficients are dependent on work and tool materials. Good agreement is achieved between experimental and predicted forces. The model is extended on one hand to include different work material by introducing a hardness factor. The model provides good predictions when predicted forces are compared to present and published experimental results. On the other hand, the extension of the ploughing model to taming with a worn edge showed the ability of the model in predicting machining forces during steady state turning with the worn flank of the tool. In the development of the dynamic models, the dynamic turning force equations define the cutting process as being a system for which vibration of the tool tip in the feed direction is the input and measured forces are the output The model takes into account the shear plane oscillation and the cutting configuration variation in response to tool motion. Theoretical expressions of the turning forces are obtained for new and worn cutting edges. The dynamic analysis revealed the interaction between the cutting mechanism and the machine tool structure. The effect of the machine tool and tool post is accounted for by using experimental data of the transfer function of the tool post system. Steady state coefficients are corrected to include the changes in the cutting configuration with tool vibration and are used in the dynamic model. A series of oscillatory cutting tests at various conditions and various tool flank wear levels are carried out and experimental results are compared with model—predicted forces. Good agreement between predictions and experiments were achieved over a wide range of cutting conditions. This research bridges the gap between the analysis of vibration and turning forces in turning. It offers an explicit expression of the dynamic turning force generated during machining and highlights the relationships between tool wear, tool vibration and turning force. Spectral analysis of tool acceleration and turning force components led to define an "Inertance Power Ratio" as a flank wear monitoring factor. A formulation of an on—line flank wear monitoring methodology is presented and shows how the results of the present model can be applied to practical in—process tool wear monitoring in • turning operations.

Technology transfer and competitive operations:the valuation and collaboration questions

To improve competitiveness and find new markets companies are extending their operations through collaborations involving technology transfer. However, such collaborations have often been based on ad hoc agreements resulting from negotiations in which each side has been inadequately equipped with information about the other’s motivations and expectations. As a result there has been a gap in the ‘value’ attached to the technology, leading to delays or even failure in reaching an agreement. To address this problem a technology valuation and collaboration model has been developed using empirical data gathered from various points along the UK-China value chain for machine tool technology.

Improving the use of visual interactive simulation as a knowledge elicitation tool

Knowledge elicitation is a well-known bottleneck in the production of knowledge-based systems (KBS). Past research has shown that visual interactive simulation (VIS) could effectively be used to elicit episodic knowledge that is appropriate for machine learning purposes, with a view to building a KBS. Nonetheless, the VIS-based elicitation process still has much room for improvement. Based in the Ford Dagenham Engine Assembly Plant, a research project is being undertaken to investigate the individual/joint effects of visual display level and mode of problem case generation on the elicitation process. This paper looks at the methodology employed and some issues that have been encountered to date. Copyright © 2007 Inderscience Enterprises Ltd.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.