Control Software for Cutting Glass Machine Tool Built Using COSME Platform: Case Study

2017 ◽  
pp. 291-316
Author(s):  
Félix Serna ◽  
Carlos Catalán ◽  
Alfonso Blesa ◽  
José Manuel Colom ◽  
Josep Maria Rams
Keyword(s):  
Machine Tool ◽  
Control Software ◽  
Glass Machine

scholarly journals UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY

2009 ◽  
Vol 9 (3) ◽  
pp. 239-261 ◽  
Author(s):  
EDWARD G. ANDERSON ◽  
CHARLES H. FINE ◽  
GEOFFREY G. PARKER
Keyword(s):  
Supply Chain ◽  
Machine Tool ◽  
Machine Tool Industry

Fault Tree Analysis: A Case Study from Machine Tool Industry

2018 ◽  
Author(s):  
Rajkumar B. Patil ◽  
Digvijay A. Mhamane ◽  
Pruthwiraj B Kothavale ◽  
Basavraj Kothavale
Keyword(s):  
Machine Tool ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Machine Tool Industry

HYPOPHILOSOPHY

2018 ◽  
Vol 35 (02) ◽  
pp. 138-157 ◽  
Author(s):  
Elijah Millgram
Keyword(s):  
Immune System ◽  
Machine Tool ◽  
Machine Tool Industry

Abstract:The more obvious forms of corruption are often preceded and explained by the etiolation of a practice: as its participants lose track of the point of their activities, their moral immune system is disabled. The argument is developed via a case study, philosophy itself. After advancing the hypothesis that philosophy is in the first instance the machine tool industry of the intellect—that its task is making the intellectual tools that make the intellectual tools—we consider what becomes of such an enterprise when this objective slips out of focus.

Virtual prototyping of embedded control software in mechatronic systems: A case study

Mechatronics ◽  
2017 ◽  
Vol 43 ◽  
pp. 99-111 ◽  
Author(s):  
Alessandro Beghi ◽  
Fabio Marcuzzi ◽  
Paolo Martin ◽  
Fabio Tinazzi ◽  
Mauro Zigliotto
Keyword(s):  
Virtual Prototyping ◽  
Control Software ◽  
Mechatronic Systems ◽  
Embedded Control

scholarly journals Automatic planning for machine tool calibration: A case study

2012 ◽  
Vol 39 (13) ◽  
pp. 11367-11377 ◽  
Author(s):  
S. Parkinson ◽  
A.P. Longstaff ◽  
S. Fletcher ◽  
A. Crampton ◽  
P. Gregory
Keyword(s):  
Machine Tool ◽  
Automatic Planning

scholarly journals Adaptive decision support for suggesting a machine tool maintenance strategy

2018 ◽  
Vol 24 (3) ◽  
pp. 376-399 ◽  
Author(s):  
Abubaker Shagluf ◽  
Simon Parkinson ◽  
Andrew Peter Longstaff ◽  
Simon Fletcher
Keyword(s):  
Decision Support ◽  
Machine Tool ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Content Type ◽  
Machine Tool Accuracy

Purpose The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis. Findings A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

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