Development of a New Type of Incisal Table for Prosthetic Articulators

This study illustrates the effectiveness of an advanced incisal table surface, featuring adjustable curvature, in the sake of more accurate articulator kinematics in anterior teeth reconstruction. Prosthetic articulators, used by dental technicians in reconstructive dentistry, are adjustable instruments that simulate the motion of mastication between dental casts: usually, the forward motion (protrusion) of the mandible is guided by sliding a pin over a flat table in order to recreate those movements when incisal teeth are missing. However, such protrusion is an approximation of the exact motion, since flat incisal tables have a limited set of adjustments. Customized software has been developed in order to simulate the kinematics of articulators in three-dimensional space: animations and measures of the envelope of teeth profiles show the unfeasibility of reconstructing with good approximation the profile of incisive teeth, when a simple ‘flat' incisal table is used. A new incisal table with an adjustable curvature has been proposed, simulated, and built, and computer simulations demonstrated the superior precision of the new design when compared to a conventional articulator which uses a flat incisal table

A deep learning based-decision support tool for solution recommendation in cloud manufacturing platforms

Abstract Industry 4.0 key enabling technologies such as cloud manufacturing allow for the dynamic sharing of distributed resources for efficient use at industrial network level. Interconnected users, i.e. suppliers and customers, offer and request manufacturing services over a cloud manufacturing platform, where an intelligent engine generates a number of solutions based on functional and geometrical requirements. A high number of suppliers leads to a higher number of solutions available for customers increasing the decision-making complexity from a customer perspective. Recommendation systems play a crucial role in expanding the opportunities in decision-making processes under complex information environments. In this scope, this paper proposes the conceptualization and the development of a recommendation decision support tool to be implemented in a cloud manufacturing platform to assist customers in appropriately selecting manufacturing services with reference to sheet metal cutting operations. In terms of solution selection, a Deep Neural Network (DNN) paradigm is adopted to allow for the automatic learning of optimal solution recommendation list based both on customers past experiences and new choices. In this respect, a virtual interaction environment is firstly built for system pre-training. Subsequently, users' data are inputted in the pre-trained model to predict a recommendation list. This is then subject to user interaction, i.e. selection, which will be fed back into the model to update the training parameters. This paper concludes with a simulated case study reported to exemplify the proposed methodology for a variety of decision-making scenarios

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes

Tool state assessment for reduction of life cycle environmental impacts of aluminium machining processes via infrared temperature monitoring

Modern industrial machining environments face new challenges in implementing process monitoring systems to improve energy efficiency whilst ensuring quality standards. A process monitoring methodology for tool state identification during milling of aluminium has been implemented through the utilisation of an infrared (IR) camera. A features extraction procedure, based on statistical parameters calculation, was applied to temperature data generated by the IR camera. The features were utilised to build a fuzzy c-means (FCM) based decision making support system utilising pattern recognition for tool state identification. The environmental benefits deriving from the application of the developed monitoring system, are discussed in terms of prevention of rework/rejected products and associated energy and material efficiency improvements

Resource Efficiency Optimization Engine in Smart Production Networks via Intelligent Cloud Manufacturing Platforms

Abstract The aim of this paper is to develop an optimization engine to be implemented in a cloud manufacturing platform to promote resource efficiency in sharing of manufacturing services related to sheet metal cutting. The optimization engine allows to properly select the manufacturing service requests collected through the cloud platform, analyse the possible pairings with the supplier ongoing production orders and dynamically choose the best production strategy (e.g. incorporate, queue, prioritize or reject) considering the surface utilization rate of the metal sheets as key performance index. A simulation of different possible scenarios in terms of customer and supplier orders is reported to exemplify the diverse decision-making scheduling strategies proposed by the platform and the related quantification of resource efficiency improvement

A multi-sensor approach for fouling level assessment in clean-in-place processes

Clean-in-place systems are largely used in food industry for cleaning interior surfaces of equipment without disassembly. These processes currently utilise an excessive amount of resources and time, as they are based on an open loop (no feedback) control philosophy with process control dependent on conservative over estimation assumptions. This paper proposes a multi-sensor approach including a vision and acoustic system for clean-in-place monitoring, endowed with ultraviolet optical fluorescence imaging and ultrasonic acoustic sensors aimed at assessing fouling thickness within inner surfaces of vessels and pipeworks. An experimental campaign of Clean-in-place tests was carried out at laboratory scale using chocolate spread as fouling agent. During the tests digital images and ultrasonic signal specimens were acquired and processed extracting relevant features from both sensing units. These features are then inputted to an intelligent decision making support tool for the real-time assessment of fouling thickness within the clean-in-place system

Improving energy efficiency within manufacturing by recovering waste heat energy

Improving energy efficiency within manufacturing by recovering waste heat energ

A multi-sensor approach for fouling level assessment in clean-in-place processes

Clean-in-place systems are largely used in food industry for cleaning interior surfaces of equipment without disassembly. These processes currently utilise an excessive amount of resources and time, as they are based on an open loop (no feedback) control philosophy with process control dependent on conservative over estimation assumptions. This paper proposes a multi-sensor approach including a vision and acoustic system for clean-in-place monitoring, endowed with ultraviolet optical fluorescence imaging and ultrasonic acoustic sensors aimed at assessing fouling thickness within inner surfaces of vessels and pipeworks. An experimental campaign of Clean-in-place tests was carried out at laboratory scale using chocolate spread as fouling agent. During the tests digital images and ultrasonic signal specimens were acquired and processed extracting relevant features from both sensing units. These features are then inputted to an intelligent decision making support tool for the real-time assessment of fouling thickness within the clean-in-place system

Eco-intelligent monitoring for fouling detection in clean-in-place

Clean-in-place (CIP) is a widely used technique applied to clean industrial equipment without disassembly. Cleaning protocols are currently defined arbitrarily from offline measurements. This can lead to excessive resource (water and chemicals) consumption and downtime, further increasing environmental impacts. An optical monitoring system has been developed to assist eco-intelligent CIP process control and improve resource efficiency. The system includes a UV optical fouling monitor designed for real-time image acquisition and processing. The output of the monitoring is such that it can support further intelligent decision support tools for automatic cleaning assessment during CIP phases. This system reduces energy and water consumption, whilst minimising non-productive time: the largest economic cost for CIP

A material flow modelling tool for resource efficient production planning in multi-product manufacturing systems

Resource efficiency is recognized as one of the greatest sustainability challenges facing the manufacturing industry in the future. Materials are a resource of primary importance, making a significant contribution to the economic costs and environmental impacts of production. During the manufacturing phase the majority of resource efficiency initiatives and management methodologies have been concerned primarily with improvements measured on an economic basis. More recently, the need for even greater levels of resource efficiency has extended the scope of these initiatives to consider complete manufacturing and industrial systems at an economic and environmental level. The flow of materials at each system level relates directly to material efficiency, which in turn influences the consumption of other resources such as water and energy. Initial research by the authors in material efficiency focused on material flow, proposing a material flow assessment approach, comprising a systematic framework for the analysis of quantitative and qualitative flow in manufacturing systems. The framework was designed to provide greater understanding of material flow through identification of strengths, weaknesses, constraints and opportunities for improvement, facilitating the implementation of improvement measures for greater efficiency in both environmental and economic terms. This paper presents an extension of this work, applying the material flow assessment framework to a complex multi-product and multi-site manufacturing system scenario. It begins with a description of the Resource Efficient Scheduling (RES) tool that supports the implementation of this framework. The tool models the interactions of quantitative and qualitative material flow factors associated with production planning and the resulting impacts on resource efficiency. This provides a more detailed understanding of the economic and resource impacts of different production plans, enabling greater flexibility and the ability to make better informed decisions. Finally a case study is presented, highlighting the application of the tool and its potential benefits