Soft computing for tool life prediction a manufacturing application of neural - fuzzy systems

Tooling technology is recognised as an element of vital importance within the manufacturing industry. Critical tooling decisions related to tool selection, tool life management, optimal determination of cutting conditions and on-line machining process monitoring and control are based on the existence of reliable detailed process models. Among the decisive factors of process planning and control activities, tool wear and tool life considerations hold a dominant role. Yet, both off-line tool life prediction, as well as real tune tool wear identification and prediction are still issues open to research. The main reason lies with the large number of factors, influencing tool wear, some of them being of stochastic nature. The inherent variability of workpiece materials, cutting tools and machine characteristics, further increases the uncertainty about the machining optimisation problem. In machining practice, tool life prediction is based on the availability of data provided from tool manufacturers, machining data handbooks or from the shop floor. This thesis recognises the need for a data-driven, flexible and yet simple approach in predicting tool life. Model building from sample data depends on the availability of a sufficiently rich cutting data set. Flexibility requires a tool-life model with high adaptation capacity. Simplicity calls for a solution with low complexity and easily interpretable by the user. A neural-fuzzy systems approach is adopted, which meets these targets and predicts tool life for a wide range of turning operations. A literature review has been carried out, covering areas such as tool wear and tool life, neural networks, frizzy sets theory and neural-fuzzy systems integration. Various sources of tool life data have been examined. It is concluded that a combined use of simulated data from existing tool life models and real life data is the best policy to follow. The neurofuzzy tool life model developed is constructed by employing neural network-like learning algorithms. The trained model stores the learned knowledge in the form of frizzy IF-THEN rules on its structure, thus featuring desired transparency. Low model complexity is ensured by employing an algorithm which constructs a rule base of reduced size from the available data. In addition, the flexibility of the developed model is demonstrated by the ease, speed and efficiency of its adaptation on the basis of new tool life data. The development of the neurofuzzy tool life model is based on the Fuzzy Logic Toolbox (vl.0) of MATLAB (v4.2cl), a dedicated tool which facilitates design and evaluation of fuzzy logic systems. Extensive results are presented, which demonstrate the neurofuzzy model predictive performance. The model can be directly employed within a process planning system, facilitating the optimisation of turning operations. Recommendations aremade for further enhancements towards this direction

Does entry business regulation deter FDI? Evidence from Dynamic Estimators

The present paper aims to examine the effects of entry business regulation on the Foreign Direct Investment (FDI) inflows (net amount) of 185 countries covering a period from 2004 to 2017. For that reason, we estimate a dynamic panel FDI specification, which additionally checks for macroeconomic and institutional factors, using Fixed Effects, Bootstrap Fixed Effects and GMM estimators. Overall, the empirical findings reveal, a negative and statistically significant association between entry regulation and FDI. This suggests that bureaucratic burdens concerning entry regulation can exert adverse effects on the inflow of FDI. Furthermore, when the full sample is separated into different income groups, our findings still indicate evidence of significance, which, however, arises only in the countries of Low and Middle-income.

Assessing human-centricity in AI enabled manufacturing systems a socio-technical evaluation methodology

The emerging interest in Industry 5.0 is consistent with the growing importance of instilling human-centricity in manufacturing technological innovations. Human-centricity concerns the creation of a human-technology symbiosis that enables the capitalization of respective human and technical capabilities for optimal system performance. While Industry 5.0 advocates the need to consider human aspects already at the design of technical systems, there is currently a lack of insights regarding the relevant performance criteria to consider when evaluating human-centric manufacturing. This paper presents an evaluation methodology for artificial intelligence (AI)-enabled manufacturing in the transition towards Industry 5.0. It adopts a multi-viewpoint assessment via an appropriate set of social, technical and operational factors to be considered when designing or implementing human-centric AI. The methodology can guide designers and decision-makers to evaluate the embedding of AI into industrial work systems, providing clarity on relevant criteria to consider when moving towards human-centricity in AI-enabled manufacturing

Development and application of a human-centric co-creation design method for AI-enabled systems in manufacturing

The integration of AI-enabled solutions in manufacturing is creating unprecedented challenges to design human-centric, safe and trusted systems. As opposed to designing a system with as little human input as possible, humans will still be expected to continue playing a vital role in the design, operation, and control of AI supported manufacturing systems. Yet, till now, there has been little discussion of what the requirements of human-centric designs might be in an AI environment. To facilitate the consideration of human skills, capabilities and human factors, a human-centric design method was developed and tested through co-creation workshops addressing industrial use cases of AI deployment in manufacturing. The method proved successful in encouraging relevant stakeholders to identify human factors-related issues linked to the different collaboration scenarios of humans with AI systems early in the design process

Anticipating human presence for safer worker - robot shared workspaces

The co-existence for human and mobile robots in modern industrial environments is increasingly common. Safety primitive behaviours are typically built-in mobile robots, to ensure safety. However, when fleets of multiple robots are operating in such environments, robot path planning becomes complicated and is often left sub-optimal to avoid compromising human, equipment, or process safety. Enhanced performance can be achieved if path planning takes into account not just current human presence, but projected human movement trajectories. While this problem has received extensive attention in outdoor environments in autonomous driving contexts, its indoors workspace equivalent has received less attention. This paper presents an approach for human movement prediction in industrial work environments, based on past and current heatmap occupancy grids and convolutional neural networks. The adopted heatmap format is appropriate for dealing with privacy concerns so as to avoid individual person identification. Obtained results from a range of simulation data are presented, following by a discussion on limitations, and challenges to be handled by further work