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A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment

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Abstract

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.

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Abbreviations

i and j :

Workpiece i processed by process j

C ij :

Production cost

C L :

Cost per unit time of the labor and machine tool

\( {T}_{m_{ij}} \) :

Machining time

\( {T}_{TL_{ij}} \) :

Tool life

\( {C}_{TC_{ij}} \) :

Tool cost

Di, Li :

Workpiece diameter and length

υij, fij, dij :

Cutting speed, feed rate, and depth of cut,

D c :

Diameter of the milling cutter or the drill diameter

z :

No. of teeth in milling cutter or No. of flutes in a tap

θ :

Drill point angle

V ij :

Volume of the removed material

MRR ij :

Material removal rate

t e :

Time required to exchange a tool

t r :

Tool replacement time

BHN :

Workpiece hardness (Brinell hardness number)

E :

Young’s modulus of elasticity

NR :

Nose radius on the tool point

\( {r}_{e_{ij}} \) :

Tool radius nose

∆T :

Mean temperature rise at tool-chip interface

ρ :

Density of workpiece material

C p :

Specific heat capacity of workpiece material

α w :

Thermal diffusivity of the workpiece material

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Funding

This effort has been sponsored in part under the cooperative agreement No. 70NANB12H284 between NIST and Pennsylvania State University. The work described was funded by the United States Government and is not subject to copyright.

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Authors and Affiliations

  1. Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, 310 Leonhard Building, University Park, PA, 16802, USA

    Qais Y. Hatim & Soundar Kumara

  2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Drive, Atlanta, GA, 30332, USA

    Christopher Saldana

  3. National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899, USA

    Guodong Shao, K. C. Morris & Paul Witherell

  4. Department of Manufacturing and Engineering Technology, Tennessee Technological University, 920 N Peachtree Avenue, Cookeville, TN, 38505, USA

    Duck Bong Kim

  5. Department of Energy, 1000 Independence Avenue, SW, Washington, D.C, 20585, USA

    Sudarsan Rachuri

Authors
  1. Qais Y. Hatim
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Correspondence to Soundar Kumara.

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No approval or endorsement of any commercial product by the National Institute of Standards and Technology is intended or implied. Certain commercial software systems are identified in this paper to facilitate understanding. Such identification does not imply that these software systems are necessarily the best available for the purpose.

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Appendix A:

Appendix A:

Table 9 A list of the most involved databases in this research
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Hatim, Q.Y., Saldana, C., Shao, G. et al. A decision support methodology for integrated machining process and operation plans for sustainability and productivity assessment. Int J Adv Manuf Technol 107, 3207–3230 (2020). https://doi.org/10.1007/s00170-019-04268-y

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