An automated feature extraction method with application to empirical model development from machining power data

Machining shop floor jobs are rarely optimised for minimisation of the energy consumption, as no clear guidelines exist in operating procedures and high production rates and finishing quality are requirements with higher priorities. However, there has been an increased interest recently in more energy-efficient process designs, due to new regulations and increases in energy charges. Response Surface Methodology (RSM) is a popular procedure using empirical models for optimising the energy consumption in cutting operations, but successful deployment requires good understanding of the methods employed and certain steps are time-consuming. In this work, a novel method that automates the feature extraction when applying RSM is presented. Central to the approach is a continuous Hidden Markov model, where the probability distribution of the observations at each state is represented by a mixture of Gaussian distributions. When applied to a case study, the automated extracted material cutting energies lay within 1.12% of measured values and the spindle acceleration energies within 3.33% of their actual values

A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility

Remanufacturing often seems a sensible approach for companies looking to adopt sustainable business plans to achieve long term success. However, remanufacturing must not be treated as a panacea for achieving a sustainable business, as issues such as market demand, product design, end of life condition and information uncertainty can affect the success of a remanufacturing endeavour. Businesses therefore need to carefully assess the feasibility of adopting remanufacturing before committing to a particular activity or strategy. To aid this decision process, a number of tools and techniques have been published by academics. However, there is currently not a formal review and comparison of these tools and how they relate to the decision process. The main research objective of this study has therefore been to identify tools and methods which have been developed within academia to support the decision process of assessing and evaluating the viability of conducting remanufacturing, and evaluate how they have met the requirements of the decision stage. This has been achieved by conducting a content analysis. Three bibliographic databases were searched (Compendex, Web of Science and Scopus) using a structured keyword search to identify relevant literature. The identified tools were then split into 6 categories based upon the specific decision stages and applications, then evaluated against a set of key criteria which are, the decision factors (economic, environmental, social) and the inclusion of uncertainty. The key finding of this study has been that although decision factors are generally well covered, operational tools and the use of uncertainty are often neglected

Cyber-physical systems in the re-use, refurbishment and recycling of used electrical and electronic equipment

The aim of the research outlined in this paper is to demonstrate the implementation of a Cyber-Physical System (CPS) within the End of Life (EoL) processing of Electrical and Electronic Equipment (EEE). The described system was created by reviewing related areas of research, capturing stakeholder’s requirements, designing system components and then implementing within an actual EoL EEE processer. The research presented in this paper details user requirements, relevant to any EoL EEE processer, and provides information of the challenges and benefits of utilising CPSs systems within this domain. The system implemented allowed an EoL processer to attach passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags to cores (i.e. mobile phones and other IT assets) upon entry to the facility allowing monitoring and control of the core’s refurbishment. The CPS deployed supported the processing and monitoring requirements of PAS 141:2011, a standard for the correct refurbishment of both used and waste EEE for reuse. The implemented system controls how an operator can process a core, informing them which process or processes should be followed based upon the quality of the core, the recorded results of previous testing and any repair efforts. The system provides Human-Computer Interfaces (HCIs) to aid the user in recording core and process information which is then used to make decisions on the additional processes required. This research has contributed to the knowledge of the advantages and challenges of CPS development, specifically within the EoL domain, and documents future research goals to aid EoL processing through more advanced decision support on a core’s processes

Enhanced condition monitoring of the machining process using wavelet packet transform

© 2018 Taylor & Francis Group, London. Tool wear in machining processes can have a detrimental impact upon the surface finish of a machined part, increase the energy consumption during manufacture and potentially, if the tool fails completely, damage incurred may require the part to be scrapped. Monitoring of the tools condition can therefore lead to preventative steps being taken to avoid excessively worn tools being used during machining, which could cause a part becoming damaged. Several studies have been devoted to condition monitoring of the machining process, including the evaluation of cutting tool condition. However, these methods are either impractical for a production environment due to lengthy monitoring time, or require knowledge of cutting parameters (e.g. spindle speed, feed rate, material, tool) which can be difficult to obtain. In this study, we aim to investigate if tool wear can be directly identified using features extracted from the electrical power signal of the entire Computer Numerical Control (CNC) machine (three phase voltage and current) captured at 50 KHz, for different cutting parameters. Wavelet packet transform is applied to extract the feature from the raw measurement under different conditions. By analyzing the energy and entropy of reconstructed signals at different frequency sub-bands, the tool wear level can be evaluated. Results demonstrate that with the selected features, the effects due to cutting parameter variation and tool wear level change can be discriminated with good quality, which paves the way for using this technique to monitor the machining process in practical applications

Industrie 4.0 implementations in the automotive industry

To address the challenges imposed by the adoption of new technology to realise the Industrial Internet also known as Industrie 4.0, manufacturing companies are recognising the need to set up and manage “intelligent test factories”. The result is networks of cyber-physical systems (CPS) where software interfaces and services are developed to support interoperability between physical and control structures. A test factory using Radio Frequency Identification (RFID) as a first generation enabler of CPS in industrial production systems is presented in this paper. The research outlined in this paper describes the first generation of CPS that uses identification technologies such as RFID tags embedded into engine components and their carries, which allow unique identification. Data storage, processing and analytics are also provided to support real-time algorithmic intelligent services that may be used in manufacturing operations including supply chain logistics, quality audits and manufacturing strategies

A data management system for identifying the traceability of returnable transit items using radio frequency identification portals

The advancement of paradigms such as Industry 4.0 and cyber physical systems herald increased productivity and efficiency for manufacturing businesses through increased capture and communication of data, information and knowledge. However, interpreting the raw data captured by sensing devices into useful information for decision making can be challenging as it often contains errors and uncertainty. This paper specifically investigates the challenges of analysing and interpreting data recorded using Radio Frequency IDentification (RFID) portals to monitor the movements of Returnable Transit Items (RTI), such as racks and stillage, within an automotive manufacturing environment. Data was collected over a yearlong pilot study using an RFID portal system installed across two automotive facilities to trace the movement of RTIs between the sites. Based upon the results key sources of errors and uncertainty have been identified and a data management framework is proposed to alleviate these errors

Cost estimation for remanufacture with limited and uncertain information using case based reasoning

Assessing products prior to remanufacture is an important part of the remanufacturing process, ensuring that unsuitable cores are removed at an early stage to avoid unnecessary processing. In particular, understanding the economic cost of remanufacturing a product can be an important aspect of the assessment, especially for businesses operating in low volumes and on high value products, where the risk associated with unexpected costs or failure to complete remanufacture are much greater. Estimating these costs can however be difficult, as important information required to make a prediction is often uncertain, such as the product design, its condition and also the understanding of the resource requirements for remanufacture. Within this research a method has been developed to estimate the economic cost and risks of conducting a remanufacturing activity to a product when information is uncertain. Summation of the individual activities can then be conducted to determine the economic cost and risks of the entire remanufacturing process. The method utilises a combination of case based reasoning and probability theory to identify similarities between historical data records and the product under assessment, to predict the cost and risks of remanufacture. In particular this method enables cost estimation when important product information is missing including the manufacturer, model or condition. Additionally estimates can be made when exact historical information is not present, which can be useful to business remanufacturing bespoke or rare products. The method is then implemented within a service oriented architecture and functionally demonstrated using an example of an independent wind turbine gearbox remanufacturer

Performance measurement and KPIs for remanufacturing

The paper provides a brief background to remanufacturing and the general use of Performance Measurement and Key Performance Indicators (KPIs) before introducing selected and newly formulated KPIs designed specifically for remanufacturing. Their relationships with the remanufacturing challenges faced by two contrasting remanufacturing businesses and the wider reman industry are described in detail. Subsets of KPIs forming a ‘Balanced Scorecard’ for each of the two remanufacturing cases conclude the paper. They arise through close working with Centro Ricerche FIAT (CRF) and SKF, and are triangulated by literature review and wider expert interviews. The two businesses represent contrasting remanufacturing scenarios: well-established high-volume low-margin automotive engine remanufacturing by the OEM ( >1000 units per year, < €10 k per unit) verses low-volume high-value wind turbine gearbox reman by an independent start-up ( €100 k per unit). The 10 general production engineering KPIs selected for the reman KPI toolbox are as follows: Work In Progress (WIP), Overall Equipment Effectiveness (OEE), Lead Time (LT), Cycle Time (CT), Hours Per Unit (HPU), Product Margin (PM), Quotation Accuracy (QA), Number of Concessions (NC), Number of managed mBOMs (BOM), and Personnel Saturation (PS). The Eco KPIs selected are: Material Used (MU), Recycled Material Used (RMU), Direct Energy Consumption (ECD), Indirect Energy Consumption (ECI), Water Withdrawal (WW), Green House Gas emissions (GHG), Total Waste (TW) by weight. The 8 Remanufacturing KPIs compiled and formulated as part of this research are: Core / Product Ratio (CPR), Core / Product Value Ratio (CPV), New Component Costs (NCC), Component Salvage Rate (SRC), Product Salvage Rate (SRP), Core Disposal Rate (CDR), Core Class Accuracy (CCA), and Core Class Distribution (CCD)

A cyber physical system for tool condition monitoring using electrical power and a mechanistic model

© 2020 Tool Condition Monitoring (TCM) systems, which aim to identify tool wear automatically to avoid damage to the machined part, are often not suitable for industrial applications due to: (i) sensing methods which can be expensive and invasive to install and (ii) testing regimes that only evaluate a narrow operating window under controlled conditions. To combat these issues, the research outlined in this paper explores the feasibility of TCM using electrical power consumption of an industrial Computer Numeric Control (CNC) cutting machine in combination with a mechanistic model for end milling operations. End milling of aluminium 6082 was performed over a range of cutting parameters (i.e. spindle speed, feed rate, tool diameter) and repeated with increasing levels of tool wear to ascertain the suitability of this approach. Reasonable correlation between the predicted and observed tool wear was found using the mechanistic model (R2 = 0.801), however variance between the cutting parameters highlights the limitations of the predictions. To combat this variance, an averaging window is taken over the course of a cutting program to reduce the overall error. A concept TCM system is then proposed, utilising cyber-physical models of the milling process to determine the width and depth of cuts for complex geometries which change in real time, with the challenges of implementing such a system discussed

A data-driven simulation to support remanufacturing operations

Simulations are a vital component in developing smart manufacturing systems, predicting the behaviour of the manufacturing shop floor operations to support production planning, scheduling and maintenance decisions within manufacturing environments. However, simulations are often limited in their ability to support real-time business decisions in complex fast changing environments due to the cost and time required to build, update and maintain simulation models. Remanufacturing operations in particular could benefit from the use of simulations as a tool to support the assessment of different strategies to real-time scenarios due to the uncertain nature of product returns. This research develops a data-driven simulation approach to predict material flow behaviour within remanufacturing operations, by utilising data from digital manufacturing systems (i.e. databases, traceability systems, process plans) to update and automatically modify the simulation constructs to reflect the real world or planned system. A data-driven simulation is proposed comprising of three elements: (i) an adaptive remanufacturing simulation algorithm to model the complex material flow found within a remanufacturing process in a generic and reusable way, (ii) a remanufacturing information model to structure and highlight the simulation data requirements and (iii) an information service layer to collect and analyse sensor data for use within the simulation. The simulation is implemented to demonstrate how it can automatically reconfigure and adapt to changes within the data inputs (process and factory models) using a case study of operations from a Waste Electrical and Electronic Equipment (WEEE) remanufacturer, utilising data collected from a Radio Frequency Identification (RFID) traceability system installed within the remanufacturing facility