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

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

Infrared monitoring of aluminium milling processes for reduction of environmental impacts

In modern manufacturing contexts, process monitoring is an important tool aimed at ensuring quality standard fulfilment whilst maximising throughput. In this work, a monitoring system comprised of an infrared (IR) camera was employed for tool state identification and surface roughness assessment with the objective of reducing environmental impacts of a milling process. Two data processing techniques, based on statistical parameters and polynomial fitting, were applied to the temperature signal acquired from the IR camera during milling operations in order to extract significant features. These features were inputted to two different neural network based procedures: pattern recognition and fitting, for decision making support on tool condition and surface roughness evaluation respectively. These capabilities are discussed in terms of reducing waste products and energy consumption whilst further improving productivity

Energy-efficient systems for the sensing and separation of mixed polymers

Polymers are ubiquitous in modern manufactured products. The potential detrimental impacts of their end-of-life disposal have stimulated significant increases in recycling rates. Recyclate purity is paramount; however this must be achieved with a positive net energy balance. Existing technologies for identification and separation of polymers are often both expensive and energy intensive. This paper investigates Infrared (IR) imaging to extract information on thermal properties of various product polymers within a recycling line. An intelligent decision making support system is enabled using neural network based pattern recognition for automatic polymer identification and classification. Potential energy savings versus current technologies are discussed

A decision support system for waste heat recovery in manufacturing

One third of energy consumption is attributable to the industrial sector, with as much as half ultimately wasted as heat. Consequently, research has focused on technologies for harvesting this waste heat energy, however, the adoption of such technologies can be costly with long payback time. A decision support tool is presented which computes the compatibility of waste heat source(s) and sink(s), namely the exergy balance and temporal availability, along with economic and environmental benefits of available heat exchanger technologies to propose a streamlined and optimised heat recovery strategy. Substantial improvement in plant energy efficiency together with reduction in the payback time for heat recovery has been demonstrated in the included case study

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

Optimized assembly design for resource efficient production in a multiproduct manufacturing system

Resource efficiency is one of the greatest challenges for sustainable manufacturing. Material flow in manufacturing systems directly influences resource efficiency, financial cost and environmental impact. A framework for material flow assessment in manufacturing systems (MFAM) was applied to a complex multi-product manufacturing case study. This supported the identification of options to alter material flow through changes to the product assembly design, to improve overall resource efficiency through eliminating resource intensive changeovers. Alternative assembly designs were examined using a combination of intelligent computation techniques: k-means clustering, genetic algorithm and ant colony algorithm. This provided recommendations balancing improvement potential with extent of process modification impact