A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection

Smarter–lighter–greener : research innovations for the automotive sector

This paper reviews the changing nature of research underpinning the revolution in the automotive sector. Legislation controlling vehicle emissions has brought urgency to research, so we are now noticing a more rapid development of new technologies than at any time in the past century. The light-weighting of structures, the refinement of advanced propulsion systems, the advent of new smart materials, and greater in-vehicle intelligence and connectivity with transport infrastructure all require a fundamental rethink of established technologies used for many decades—defining a range of new multi-disciplinary research challenges. While meeting escalating emission penalties, cars must also fulfil the human desire for speed, reliability, beauty, refinement and elegance, qualities that mark out the truly great automobile

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

QUALITY ANALYSIS IN FLEXIBLE MANUFACTURING SYSTEMS WITH BATCH PRODUCTIONS

To improve product quality and reduce cost, batch production is often implemented in many exible manufacturing systems. However, the current literature does not provide any method to analyze the quality performance in a flexible manufacturing system with batch production. In this research, we present an analytical method with closed-form formula to evaluate the quality performance in such systems. Based on the model, we discover and investigate monotonic and non-monotonic properties in quality to provide practical guidance for operation management. To improve product quality, we introduce the notions of quality improvability with respect to product sequencing. In addition, we develop the indicators for quality improvability based on the data available on the factory floor rather than complicated calculations. We define the bottleneck sequence and bottleneck transition as the ones that impede quality in the strongest manner, investigate the sensitivity of quality performance with respect to sequences and transitions, and propose quality bottleneck sequence and transition indicators based on the measured data. Finally, we provide a case study at an automotive paint shop to show how this method is applied to improve paint quality. Moreover, we explore a potential application to reduce energy consumption and atmospheric emissions at automotive paint shops. By selecting appropriate batch and sequence policies, the paint quality can be improved and repaints can be reduced so that less material and energy will be consumed, and less atmospheric emissions will be generated. It is shown that such scheduling and control method can lead to significant energy savings and emission reduction with no extra investment nor changes to existing painting processes. The successful development of such method would open up a new area in manufacturing systems research and contribute to establish a solid foundation for an integrated study on productivity, quality and exibility. In addition, it will provide production engineers and operation managers a quantitative tool for continuous improvement on product quality in flexible manufacturing environmen

A cross‐sectorial review of industrial best practices and case histories on Industry 4.0 technologies

Industry 4.0 (I4.0) was introduced in 2011, and its advanced enablers strongly affect industrial practices. In the current literature, while several papers offer general reviews on the topic, contributions exploring the evidences coming from the implementation of I4.0 in multi-sector Small and Medium Enterprises (SMEs) and large enterprises are few and expected. To address this gap, a comprehensive review of the main I4.0 enabling technologies is conducted, focusing on implementation experiences in companies belonging to different sectors. Forty (40) real case studies are analyzed and compared. The results show that 63% of the identified applications involve large enterprises in the transport sector, that is, automotive, aeronautics, and railway, adopting a structured set of enabling technologies. SMEs engaged in I4.0 projects primarily belong to the mechanical engineering sector, and 37% of such projects deals with the preliminary feasibility analysis of introducing a single enabling technology. Conclusions and trends guide researchers and practitioners in understanding the implementation level of I4.0 technologies