Implementation of a machine tool performance measurement and diagnostic system and its impact on parts verification

Five axis machine tools are increasing and becoming more popular as customers demand more complex machined parts. In high value manufacturing, the importance of machine tools in producing high accuracy products is essential. High accuracy manufacturing requires producing parts in a repeatable manner and precision in compliance to the defined design specifications. The performance of the machine tools is often affected by geometrical errors due to a variety of causes including incorrect tool offsets, errors in the centres of rotation and thermal growth. As a consequence, it can be difficult to produce highly accurate parts consistently. It is, therefore, essential to ensure that machine tools are verified in terms of their geometric and positioning accuracy. When machine tools are verified in terms of their accuracy, the resulting numerical values of positional accuracy and process capability can be used to define design for verification rules and algorithms so that machined parts can be easily produced without scrap and little or no after process measurement. In this paper the benefits of machine tool verification are listed and a case study is used to demonstrate the implementation of robust machine tool performance measurement and diagnostics using a ballbar system.

Green, Greener, BPM?

I am sure you’ve heard it too: Green is the new Black. While this was true back in the days when Henry Ford introduced process standardization with his assembly line for the Ford Model T (over 15 million of these were sold!), Green is also the color of choice for many business organizations, private and public. I am not talking about the actual color of their business shirts or their logo 2.0.; I am referring to the eco-aware movement that has pushed sustainability into the top ten list of business buzz-words. What used to be a boutique market for tourism and political activists has become the biggest business revolution since the e-commerce boom. Public and private organizations alike push towards “sustainable” solutions and practices. That push is partly triggered by the immense reputational gains associated with branding your organization as “green”, and partly by emerging societal, legal and constitutional regulations that force organizations to become more ecologically aware and sustainable. But the boom goes beyond organizational reality. Even in academia, sustainability has become a research “fashion wave” (see [1] if you are interested in research fashion waves) similar to the hype around Neuroscience that our colleagues in the natural sciences are witnessing these days. Mind you, I’m a fan. A big fan in fact. As academics, we are constantly searching for problem areas that are characterized by an opportunity to do rigorous research (studies that are executed to perfection) on relevant topics (studies that have applied practical value and provide impact to the community). What would be a better playground than exploring the options that Business Process Management provides for creating a sustainable, green future? I’m getting excited just writing about this! So, join me in exploring some of the current thoughts around how BPM can contribute to the sustainability fashion parade and let me introduce you to some of the works that scholars have produced recently in their attempts to identify solutions.

多粒度多维度软件过程度量和改进方法研究

随着软件对社会各领域、各层次的渗透，软件逐渐转变为一种对社会团体、 甚至对社会公众的服务，软件的规模越来越大、用户需求越来越多、功能和性能 要求也越来越复杂。因此，对软件的可用性、可靠性、可信性等质量要求不断提 高。伴随着软件业的逐渐发展，软件过程技术逐渐被应用于软件产品的开发当中。 “质量形成于产品的生产过程”这一理念逐渐被软件组织所接受。其核心思想体 现在通过对软件过程的策划、控制和改进来保证软件产品的质量，进而提高软件 组织的经营业绩。软件过程度量作为软件过程管理和过程改进的关键活动，越来 越为软件组织所重视。 通过实施过程管理，能够刻画项目或过程目标的满足程度，找到造成过程 或产品重大偏差的根本原因，进而实施过程改进。然而，在软件过程度量实施期 间，软件组织面对不同的软件开发过程、众多的过程性能度量指标、复杂的统计 分析方法，既要考虑量化管理方法的合理性和复杂程度，又要权衡量化管理的实 施成本，这使得实施有效的过程度量充满挑战。本文基于经验软件工程方法，提 出一种多粒度多维度软件过程度量框架，以及实现该框架的关键技术：软件过程 性能基线的建立和维护方法；同时介绍了该框架下的软件项目进度量化控制模 型，支持软件组织实施有效的过程管理和改进。 本文的主要贡献包括： 提出了一种多粒度多维度软件过程度量框架(Multi-granularity Multi-dimensional software Process Measurement Framework，M2-PMF)，该框架通 过综合考虑软件过程管理和改进的必要信息所属的特征维度和软件组织的过程 管理粒度，自底向上的通过实体层、度量分析层和目标层指导软件组织建立一套 可以覆盖软件全生命周期的、开放的、支持过程改进的综合指标体系和模型。支 持软件组织裁减和定制确定环境下的度量体系，清晰了解其软件过程能力和性 能，提高软件组织对软件过程的控制能力，保障软件开发过程和软件产品的质量。 提出了基于统计分析的过程性能基线的建立和改进方法（Baseline – Statistic - Refinement， BSR），该方法可以有效地建立和维护过程性能基线，支持软件 组织从定性管理提升到定量管理。该方法应用波动图，在过程尚不稳定、数据样 本不足的情况下尽可能多的获得过程改进信息，识别过程改进机会，确定过程改 进途径，帮助软件组织高效地改进其过程中明显的弱项。在过程逐步稳定之后， 利用控制图、排列图、因果图、散点图等统计工具，分析过程性能，建立过程性 多粒度多维度软件过程度量和改进方法研究 ii 能基线，并不断精化。 在M2-PMF 框架下，提出了基于统计过程控制（Statitical Process Control， SPC）和挣值管理（Earned Value Management，EVM）的项目进度量化控制模型 SEVM，该方法通过对项目进度指数的统计控制，分析其稳定性，并通过估算模 型，根据项目当前挣值数据推算项目总进度偏差，并加以控制。支持软件组织对 项目进度进行量化控制，提高了项目按期交付的可能性。 最后，介绍了本文提出的过程度量框架和量化管理方法在国内多家软件组 织中的实际应用。应用案例表明，本文的方法和模型具有广泛的适应性和高度的 可操作性。应用本文方法能够对项目进行有效的估算、度量和控制，进而提高产 品质量并改善客户满意度。

A measurement instrument for process modeling research : development, test and procedural model

Process modeling is an emergent area of Information Systems research that is characterized through an abundance of conceptual work with little empirical research. To fill this gap, this paper reports on the development and validation of an instrument to measure user acceptance of process modeling grammars. We advance an extended model for a multi-stage measurement instrument development procedure, which incorporates feedback from both expert and user panels. We identify two main contributions: First, we provide a validated measurement instrument for the study of user acceptance of process modeling grammars, which can be used to assist in further empirical studies that investigate phenomena associated with the business process modeling domain. Second, in doing so, we describe in detail a procedural model for developing measurement instruments that ensures high levels of reliability and validity, which may assist fellow scholars in executing their empirical research.

A formative measurement model of business process model quality

Business process management (BPM) is becoming the dominant management paradigm. Business process modelling is central to BPM, and the resultant business process model the core artefact guiding subsequent process change. Thus, model quality is at the centre, mediating between the modelling effort and related growing investment in ultimate process improvements. Nonetheless, though research interest in the properties that differentiate high quality process models is longstanding, there have been no past reports of a valid, operationalised, holistic measure of business process model quality. In attention to this gap, this paper reports validation of a Business Process Model Quality measurement model, conceptualised as a single-order, formative index. Such a measurement model has value as the dependent variable in rigorously researching the drivers of model quality; as antecedent of ultimate process improvements; and potentially as an economical comparator and diagnostic for practice.

Evaluation and social impact measurement amongst small to medium social enterprises : process, purpose and value

In recent years, the imperative to communicate organisational impacts to a variety of stakeholders has gained increasing importance within all sectors. Despite growing external demands for evaluation and social impact measurement, there has been limited critically informed analysis about the presumed importance of these activities to organisational success and the practical challenges faced by organisations in undertaking such assessment. In this paper, we present the findings from an action research study of five Australian small to medium social enterprises’ practices and use of evaluation and social impact analysis. Our findings have implications for social enterprise operators, policy makers and social investors regarding when, why and at what level these activities contribute to organisational performance and the fulfilment of mission.

Process compliance measurement based on behavioural profiles

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. On the other hand, the metrics to quantify process compliance have only been defined recently. A major criticism points to the fact that existing measures appear to be unintuitive. In this paper, we trace back this problem to a more foundational question: which notion of behavioural equivalence is appropriate for discussing compliance? We present a quantification approach based on behavioural profiles, which is a process abstraction mechanism. Behavioural profiles can be regarded as weaker than existing equivalence notions like trace equivalence, and they can be calculated efficiently. As a validation, we present a respective implementation that measures compliance of logs against a normative process model. This implementation is being evaluated in a case study with an international service provider.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.