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Schmidt, B., Gandhi, K. & Wang, L. (2018). Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 1327-1332
Open this publication in new window or tab >>Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed) Published
Abstract [en]

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

Keywords
population based maintenance, condition monitoring, automatic signal selection
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15117 (URN)10.1016/j.procir.2018.03.208 (DOI)2-s2.0-85049577336 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-10-31Bibliographically approved
Gandhi, K. & Ng, A. H. C. (2018). Machine maintenance decision support system: A systematic literature review. In: Peter Thorvald, Keith Case (Ed.), Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden. Paper presented at 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden (pp. 349-354). Amsterdam: IOS Press
Open this publication in new window or tab >>Machine maintenance decision support system: A systematic literature review
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 349-354Conference paper, Published paper (Refereed)
Abstract [en]

Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords
Decision Support System, Literature Review, Machine Maintenance, Artificial intelligence, Decision making, Industrial research, Maintenance, Manufacture, Decision making process, Information flows, Literature reviews, Maintenance Action, Maintenance decision support system, Systematic literature review, Tools and techniques, Decision support systems
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16493 (URN)10.3233/978-1-61499-902-7-349 (DOI)000462212700056 ()2-s2.0-85057392073 (Scopus ID)978-1-61499-901-0 (ISBN)978-1-61499-902-7 (ISBN)
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2019-06-05Bibliographically approved
Gandhi, K., Schmidt, B. & Ng, A. H. C. (2018). Towards data mining based decision support in manufacturing maintenance. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 261-265
Open this publication in new window or tab >>Towards data mining based decision support in manufacturing maintenance
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 261-265Article in journal (Refereed) Published
Abstract [en]

The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Maintenance, Decision Support System, Data Mining, Classification Methods, Knowledge Extraction
National Category
Information Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15901 (URN)10.1016/j.procir.2018.03.076 (DOI)2-s2.0-85049600893 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-07-01 Created: 2018-07-01 Last updated: 2018-10-31Bibliographically approved
Schmidt, B., Gandhi, K., Wang, L. & Galar, D. (2017). Context preparation for predictive analytics – a case from manufacturing industry. Journal of Quality in Maintenance Engineering, 23(3), 341-354
Open this publication in new window or tab >>Context preparation for predictive analytics – a case from manufacturing industry
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

Design/methodology/approach

This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

Findings

Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

Originality/value

This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

Keywords
Predictive maintenance, Context awareness, Condition monitoring
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14033 (URN)10.1108/JQME-10-2016-0050 (DOI)000412478700007 ()2-s2.0-85027991065 (Scopus ID)
Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2019-01-24Bibliographically approved
Nupur, R., Gandhi, K., Solanki, A. & Jha, P. C. (2017). Six Sigma Implementation in Cutting Process of Apparel Industry. In: P.K. Kapur, Uday Kumar, Ajit Kumar Verma (Ed.), Quality, IT and Business Operations: Modeling and Optimization (pp. 279-295). Springer
Open this publication in new window or tab >>Six Sigma Implementation in Cutting Process of Apparel Industry
2017 (English)In: Quality, IT and Business Operations: Modeling and Optimization / [ed] P.K. Kapur, Uday Kumar, Ajit Kumar Verma, Springer, 2017, p. 279-295Chapter in book (Refereed)
Abstract [en]

The present competitive market is focusing on industrial efforts in producing high-quality products with the lowest possible cost. In every real-life system, there are a number of factors that cause disturbance in the process performance and their output. Process improvements through minimizing or removing such factors provide advantages such as reduced wastage or re-machining and improved market share. To help in accomplishing these objectives, various quality improvement philosophies have been put forward in recent years that can maximize the quality characteristics to ensure the enhancement of product and process. Six Sigma is an emerging data-driven approach that uses methodologies and tools that lead to improved quality levels and fact-based decision-making. This paper presents the application of the Six Sigma methodology to reduce defects in a cutting process of a garment manufacturing company in India, which is concluded through an action plan for improving product quality level. The define–measure–analyze–improve–control (DMAIC) approach has been followed here to solve the underlying problem of reducing defects and improving sigma level through continuous improvement process. The process helps in establishing specific inspection methods adapted for defect type which causes maximum rejection and to prevent their appearance in product.

Place, publisher, year, edition, pages
Springer, 2017
Series
Springer Proceedings in Business and Economics, ISSN 2198-7246, E-ISSN 2198-7254
Keywords
Six Sigma, Pareto chart, P-chart, Cause and effect diagram, DPMO
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:his:diva-14794 (URN)10.1007/978-981-10-5577-5_22 (DOI)978-981-10-5577-5 (ISBN)978-981-10-5576-8 (ISBN)
Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-05-30Bibliographically approved
Gandhi, K., Goyal, K. & Jha, A. (2016). A Fuzzy Multi-Criteria Optimization Model for Allocating SKU and Suppliers in SC System. In: Anshu Gupta, Kartik Dave (Ed.), Retail Marketing in India: Trends and Future Insights (pp. 117-129). Emerald Group Publishing (India)
Open this publication in new window or tab >>A Fuzzy Multi-Criteria Optimization Model for Allocating SKU and Suppliers in SC System
2016 (English)In: Retail Marketing in India: Trends and Future Insights / [ed] Anshu Gupta, Kartik Dave, Emerald Group Publishing (India) , 2016, p. 117-129Chapter in book (Refereed)
Abstract [en]

Supply chain stakeholders are increasingly paying attention to the optimal design of their supply chains because of several reasons like increasing production cost, reducing product life cycles, shrinking resources, and environmental sustainability. There has been greater emphasis on environmental concerns whilst designing the supply chains because of emerging government legislation in this domain and pressure from society.As a result, supply chain partners need to analyse their operations more critically. This study proposes a strategic decision-making model considering the operational costs caused by coordination and optimization of the sustainable supply chain design to satisfy the demand at retailers. In the study, an integrated supplier selection, procurement,inventory control and transportation model is discussed that helps in evaluating the suppliers, determining optimum quantity to procure, choosing transportation vehicle type along with managing environmental issues, obtaining optimal stock keeping units(SKU) and safety stock for each product category to fulfil a specified service level for retailers at minimum cost for the next planning horizon. The model demonstrates that how demand at retailer drives the full supply chain coordination and selection of distribution centre. The model has been validated through a case study.

Place, publisher, year, edition, pages
Emerald Group Publishing (India), 2016
Keywords
Credibility, Fuzzy analytical network process, Travelling salesman models, Stock keeping units, service level
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:his:diva-14795 (URN)978-1-7863541-0-5 (ISBN)
Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-05-30Bibliographically approved
Schmidt, B., Gandhi, K., Wang, L. & Ng, A. H. C. Integration of events and offline measurement data from a population of similar entities for condition monitoring. International journal of computer integrated manufacturing (Print)
Open this publication in new window or tab >>Integration of events and offline measurement data from a population of similar entities for condition monitoring
(English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed) Submitted
Abstract [en]

In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.

Keywords
condition monitoring, population-wide data, double ball-bar measurement, feature selection, machine learning
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15116 (URN)
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2019-06-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2545-7838

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