Machining capacity measurement of an industrial product service system for turning process

Abstract

Machining capacity measurement is of great importance in an industrial product service system for turning processes (TP-iPSS) owing to its roles in TP-iPSS evaluation, accounting, and optimization. In this paper, the concepts of potential machining capacity (MCp), real machining capacity (MCr), and total machining capacity (MCt) are presented. As for a configured TP-iPSS whose lathe is given and determined, MCp is majorly influenced by the lathe's attachments, especially the cutting tool, MCr is majorly correlative with cutting parameters (cutting depth, cutting velocity and feed) and workpiece properties besides parameters of cutting tool. Combining different cutting tool with the TP-iPSS, different MCp and potential machining area (A _p) can be obtained, then the measurement models of MCp and A _p are established through using multivariate regression analysis, and MCr is calculated with the product of MCp and the ratio of A _p to A _r (real machining area). In addition, MCt in a time interval can be calculated with the sum of MCr in the time interval. Finally, a case is studied to demonstrate the MCp, MCr, and MCt models and their basic applications to TP-iPSS evaluation, accounting, and optimization.

Keywords

industrial product service system machining capacity model measurement

References

Meier

Kortmann

, 'Leadership - from technology to use operation fields and solution approaches for the automation of service processes of industrial product-service-systems’ Proceedings of 14th CIRP conference on Life cycle engineering, Tokyo, Japan, 2007, pp. 159–163.

Maussang

Zwolinski

Brissaud

, ‘A representation of a product-service system during its design phase: a case study of a helium liquefier’ In Proceedings of 13th CIRP International Conference on Life cycle engineering, Leuven, Belgium, 2006, pp. 555–561.

Baines

T. S.

Lightfoot

H. W.

Evans

, ‘State-of-the-art in product-service systems’ Proc. IMechE, Part B: J. Engineering Manufacture 221 (10) (2007): 1543–1552 DOI: 10.1243/09544054JEM858.

Azarenko

, Development of technical product-service system scenarios for machine tool industry (2007) MSc Thesis, Cranfield University.

Copani

Marvulli

Tosatti

L. M.

, ‘An innovative pattern to design new business models in the machine tool industry’ Innovation Mfg Networks 266 (2002): 317–324.

Zhu

Jiang

Zhang

, ‘et al. Configuration and operation architecture for product service systems of CNC machine tool’ Computer Integ. Mfg Systems, CIMS 15 (6) (2009): 1140–1147.

Jiang

Zheng

, ‘Using iPSS as a new run-time for service-oriented manufacturing executive systems’ In Proceedings of International Conference on Computers and Industrial Engineering, Troyes, France, 2009, pp. 635–639.

Robinson

C. J.

Ginder

A. P.

, Implementing TPM: The North American experience (Productivity Press 1995).

Nakajima

, Introduction to TPM: total productive maintenance (Productivity Press 1984).

10.

Pomorski

T. R.

, Total productive maintenance (TPM) concepts and literature review (Brooks Automation 1994).

11.

SEMI. SEMI E79 Specification for definition and measurement of equipment productivity. San Jose, CA, Semiconductor Equipment and Materials International, 2000.

12.

SEMI. SEMI E10 standard for definition and measurement of equipment reliability, availability, and maintainability (RAM). San Jose, CA, Semiconductor Equipment and Materials International, 2001.

13.

Fang

X. D.

Jawahir

I. S.

, ‘Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters’ Int. J. Prod. Res. 32 (4) (1994): 833–849.

14.

Jawahir

I. S.

Balaji

A. K.

Rouch

K. E.

Baker

J. R.

, ‘Towards integration of hybrid models for optimized machining performance in intelligent manufacturing systems’ J. Mater. Processing Technol. 139(1–3) (2003): 488–498.

15.

Jawahir

I. S.

Wang

, ‘Development of hybrid predictive models and optimization techniques for machining operations’ J. Mater. Processing Technol. 185(1–3) (2007): 46–59.

16.

Lee

B. Y.

Tarng

Y. S.

, ‘Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations’ J. Mater. Processing Technol. 105(1–2) (2000): 61–66.

17.

Wong

S. V.

Hamouda

A. M. S.

, ‘Development of genetic algorithm-based fuzzy rules design for metal cutting data selection’ Robotics Computer-Integrated Mfg 18 (1) (2002): 1–12.

18.

Draganescu

Gheorghe

Doicin

C. V.

, ‘Models of machine tool efficiency and specific consumed energy’ J. Mater. Processing Technol. 141 (1) (2003): 9–15.

19.

Flores

Ortega

Alberti

Rodriguez

Ciurana

Elias

, ‘Evaluation and modelling of productivity and dynamic capacity in high-speed machining centers’ Int. J. Advd Mfg Technol. 33(3–4) (2007): 403–411.

20.

Mont

, ‘Clarifying the concept of product-service system’ J. Cleaner Prod. 10 (3) (2002): 237–245.

21.

Manzini

Vezzoli

, ‘A strategic design approach to develop sustainable product service systems: examples taken from the ‘environmentally friendly innovation’ Italian prize’ J. Cleaner Prod. 11 (8) (2003): 851–857.

22.

Dillon

W. R.

Goldstein

, Multivariate analysis: methods and applications (New York: John Wiley 1984): pp. 209–242.

23.

Chatterjee

Price

, Regression analysis by example fourth edition, (New York: Wiley 2006): pp. 69–70.

24.

Kohli

Dixit

U. S.

, ‘A neural-network-based methodology for the prediction of surface roughness in a turning process’ Int. J. Advd Mfg Technol. 25(1–2) (2005): 118–129.

25.

Zhou

Jiang

Huang

, ‘A game-theory approach for job scheduling in networked manufacturing’ Int. J. Advd Mfg Technol. 41(9–10) (2009): 972–985.