(767e) Physics-Based Models for Precision Machining

According to the U.S. Energy Information Administration, approximately
70% of the electricity consumed by Industry in 2011 was used for Machining [1].
Machining is the process in which a material is cut into a desired shape by
controlled removal of material [2]. Computer numerical control (CNC) automates
the control of tools to perform machining on a work piece through milling,
drilling, lathe, etc. Digital twins of machining processes (digital replicas of
the system of interest [3]) are highly valuable for the analysis, inference, control,
and optimization of these processes. In this study, we explore the limits of
capabilities of physics-based digital twins that can infer energy consumption
in precision machining, along with computing material removal, cutting fluid utilization,
scrap generation and wear of the cutting tool. Physics-based models can provide
unique insights into the energy utilization in the machining process and assist
with energy consumption reduction by optimizing system critical variables using
available or new inexpensive measurements.

The comprehensive review of machining by Zhou et al. [3]
discusses different types of models that have been developed to determine
energy utilization in machining. The energy models can be divided into three
categories: (i) linear models based on the relationship between material
removal rate (MRR) and specific energy consumption (SEC), where SEC determines
the energy consumption in the process; (ii) models of the energy consumption as
a function of material removal rate, cutting fluid, tool, material properties,
and amount of tool wear; and (iii) energy consumption models that are based on
the movement process of the machining tool (tool motion, power transmission and
parts processing). Various studies on models developed using cutting parameters
show that feed rate, cutting speed and cutting depth are the main parameters
that impact energy consumption. These models can be further classified into
black box models and empirical models between process variables and energy
consumption.

Previous studies on energy consumption during machining
process have developed models by considering only certain process aspects, such
as metal deformation, amount of tool wear, cutting force, or cutting parameters
to compute the energy consumption. Hence, a model that can address all the aforementioned
aspects is needed. In this presentation we will propose a closed-form physics-based
model that can calculate the energy consumption in precision machining. This
model provides information about the loss of cutting fluid and material scrap
generation. The models relate measured operating variables to infer energy
consumption. The digital twin proposed in this study addresses the problem of
energy waste in CNC machining and will help to better understand the energy
utilization and to minimize the loss of energy in the respective sub-processes,
such as material removal, tool wear, scrap generation, cutting fluid loss. Fig.
1 shows the machining process model framework used in this study. It can be
divided into four model blocks: material removal model, cutting fluid model,
material scrap model and tool life model. The material removal model is the
main model block and calculates the energy consumption from inputs such as geometries
of initial and final work, speed of tool, depth of cut. The cutting fluid model
has the flowrate and type of cutting fluid as inputs. In addition, the material
removed from the material removal model is also an input. The cutting fluid
model gives the loss of the cutting fluid. This model also computes the
friction factor for the material removal model. The tool life model along with
the material removal model computes the chip/scrap generation and the wear of
tool. The material removal model can be used to determine power and forces
utilized during machining operation. The digital twin for CNC machining will
help provide a better understanding to control the operations of machining.
This will further improve the efficiency of precision machining to minimize the
energy consumption and cost associated with the process.

Fig. 1 Model
framework for machining process. [4]

Acknowledgment: This material is based upon
work supported by the U.S. Department of Energy’s Office of Energy Efficiency
and Renewable Energy (EERE) under the Advanced Manufacturing Office Award
Number DE-EE0007613.

Disclaimer: This report was prepared as an
account of work sponsored by an agency of the United States Government. Neither
the United States Government nor any agency thereof, nor any of their
employees, makes any warranty, express or implied, or assumes any legal
liability or responsibility for the accuracy, completeness, or usefulness of
any information, apparatus, product, or process disclosed, or represents that
its use would not infringe privately owned rights. Reference herein to any
specific commercial product, process, or service by trade name, trademark,
manufacturer, or otherwise does not necessarily constitute or imply its
endorsement, recommendation, or favoring by the United States Government or any
agency thereof. The views and opinions of authors expressed herein do not
necessarily state or reflect those of the United States 11.5pt">Government or any agency thereof.

References

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