A framework for digitisation of manual manufacturing task knowledge using gaming interface technology

Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill. The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance. This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models

Management: A bibliography for NASA managers

This bibliography lists 630 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1991. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

Impact assessment of AI-enabled automation on the workplace and employment. The case of Portugal

Artificial intelligence (AI) has the potential to lead to a wave of innovation in organiza-tional design, changes in the workplace and create disruptive effects in the employment sys-tems across the world. Moreover, the future deployment of broad-spectrum algorithms capa-ble of being used in wide areas of application (e.g., industrial robotics, software and data anal-ysis, decision-making) can lead to considerable changes in current work patterns, swiftly render many unemployed across the globe and profoundly destabilize labour relations. The impacts of AI are estimated to lead to a reduction of millions of workplaces. But qualitative research about AI and its governance is scarce. An emergent technology requires a technology assess-ment (TA) approach to understand the implications of AI in firms. Mechanisms of industrial democracy can help to adopt AI by ensuring adequate arrangements for employees and avoid-ing conflicts (mitigating negative effects, promoting reskilling, etc.). In this research work, the probable penetration of AI in the manufacturing sector is identified to study its effects in work organization and employment in Portugal. Is the employ-ment changing alongside recent AI trends in Portugal? What are the expectable changes in work organisation due to AI-enabled automation? Are there signs of work qualification to go with AI systems implementation? Are there visions on the role of humans on the interaction with the features of industry 4.0? Does that imply new forms of human interaction with AI? These are the questions this research work will try to answer. A TA approach using mixed methods was applied to conduct statistical analyses of relevant databases, interviews with ac-ademic, industrial and social actors and exploratory scenarios of AI-based automation systems, on work organization and employment. The manufacturing industry was the chosen sector since it is the sector where most cases of AI-based automation systems are in place. Findings suggest that, until now, it seems AI is still not able to replace most of the human skills and cognitive capacities but can replace humans on simple tasks. In the future, four different possible states may occur, according to the various initial conditions, the com-pany's motivation, their business strategy, the public policies in place and main social actors involved: Re-organisation of work; Substitution of the workforce; People at the centre and Fo-cus on Efficiency. These were the basis for our scenario outcomes.A inteligência artificial (IA) tem o potencial de levar a uma onda de inovação no desenho das organizações, nas mudanças no local de trabalho e em criar efeitos disruptivos nos sistemas de emprego em todo o mundo. Além disso, a futura implementação de algoritmos de amplo espectro, capazes de serem usados em muitas áreas de aplicação (por exemplo, robótica industrial, software e análise de dados, tomada de decisão), pode levar a mudanças consideráveis nos padrões de trabalho atuais, e rapidamente, levar ao desemprego em todo o mundo e à desestabilização profunda das relações laborais. Estima-se que os impactos da IA levem a uma redução de milhões de locais de trabalho. Mas a investigação qualitativa sobre IA é escassa. Uma tecnologia emergente requer uma abordagem de avaliação de tecnologia (AT) para entender as suas implicações. Mecanismos de democracia industrial podem ajudar a adotar a IA, garantindo condições adequadas para os trabalhadores e evitando conflitos (mitigando efeitos negativos, promovendo requalificação, etc.). Neste trabalho de investigação identifica-se a provável penetração da IA no setor da indústria transformadora para estudar os seus efeitos na organização do trabalho e emprego em Portugal. O emprego está a mudar a par das tendências recentes da IA em Portugal? Quais são as mudanças na organização do trabalho devido à automação baseada em IA? Há indícios de qualificação do trabalho para acompanhar a implementação dos sistemas de IA? Existem visões sobre o papel do ser humano na interação com os recursos da indústria 4.0? Isso implica novas formas de interação humana com a IA? Estas são as perguntas que este trabalho de investigação tentará responder. Na abordagem de AT, foram usados métodos mistos para realizar análises estatísticas de bases de dados, entrevistas com atores do ecossistema académico, industrial e social e cenários exploratórios sobre os efeitos da adoção de sistemas de automação baseados em IA, na organização do trabalho e emprego. A indústria transformadora foi escolhida por ser onde existem a maioria de casos de aplicação de sistemas de auto-mação baseados em IA. Os resultados sugerem que, até ao momento, que a IA não tem a capacidade de subs-tituir a maioria das competências e raciocínio humanos, mas apenas tarefas simples. No futuro, poderão ocorrer quatro situações, dependendo das condições iniciais, motivação e estratégia da empresa, das políticas e incentivos públicos existentes e do envolvimento de atores sociais: Reorganização do trabalho; Substituição da mão-de-obra; Pessoas no centro da transformação e foco na Eficiência. Estas foram a base para os nossos cenários de referência

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

The Cultural Landscape & Heritage Paradox; Protection and Development of the Dutch Archeological-Historical Landscape and its European Dimension

To what extent can we know past and mainly invisible landscapes, and how we can use this still hidden knowledge for actual sustainable management of landscape’s cultural and historical values. It has also been acknowledged that heritage management is increasingly about ‘the management of future change rather than simply protection’. This presents us with a paradox: to preserve our historic environment, we have to collaborate with those who wish to transform it and, in order to apply our expert knowledge, we have to make it suitable for policy and society. The answer presented by the Protection and Development of the Dutch Archaeological-Historical Landscape programme (pdl/bbo) is an integrative landscape approach which applies inter- and transdisciplinarity, establishing links between archaeological-historical heritage and planning, and between research and policy. This is supported by two unifying concepts: ‘biography of landscape’ and ‘action research’. This approach focuses upon the interaction between knowledge, policy and an imagination centered on the public. The European perspective makes us aware of the resourcefulness of the diversity of landscapes, of social and institutional structures, of various sorts of problems, approaches and ways forward. In addition, two related issues stand out: the management of knowledge creation for landscape research and management, and the prospects for the near future. Underlying them is the imperative that we learn from the past ‘through landscape’