This document discusses the evolution and applications of computer numeric control (CNC) machining, focusing on the development of in-process models (IPMs) and their role in optimizing machining processes towards sustainable manufacturing. It highlights challenges in traditional CNC systems and introduces new techniques, such as voxel-based IPMs, for training and improving machining efficiency while reducing waste. The research presented emphasizes the need for first-part correct machining and the potential for automation in manufacturing to enhance productivity.

1. Towards Smart and Competitive Sustainable Machining
Mr. Liu Peiling, Principal Research Engineer
Singapore Institute of Manufacturing Technology
71 Nanyang Drive Singapore 638075
plliu@simtech.a-star.edu.sg
Keywords: in-process model, CNC simulation, virtual training，machining model,
ABSTRACT
Computer Numeric Control (CNC) revolutionized the machining technology and has been the
cutting edge of digital manufacturing since 1950s. CNC machining model, simulation, verification,
and optimization have been a vivid research topic of Smart Machining that automated the CNC
programming (CAM) and cutting process, hence greatly increased machining productivity since
1990s. This paper traces back the history of CNC simulation, analysis the different CNC machining
models, tested with application examples, and listed different CNC verification industry applications
for the last 20 years. The new challenge comes from sustainable manufacturing. Towards smart and
competitive sustainable machining, CNC model and simulation will be used to optimize the
machining process, where the raw material could be saved through First Part Correct technology, the
energy could be saved through cutting speed optimization, and used parts could be saved by
remanufacturing.
Introduction
Machining had been a low productivity manual operation until the invention of Numerical Control
(NC) during 1940s-1950s, when the hand wheels and levers was replaced by punch tapes control,
similar to telegraphs at that time. These early servomechanisms were rapidly augmented with digital
computers, creating the computer numerical controlled (CNC) machine tools that have
revolutionized the machining process and radically changed the manufacturing industry. Complex 3-
D shapes are relatively as easy to cut as plane face, and manual polishing works have been
dramatically reduced. CNC was the mother of digital manufacturing revolution.
In 1958 the MIT published its report on the economics of NC. They concluded that the tools were
competitive with human operators, but simply moved the time from the machining to the creation
of the tapes. NC programming became a bottle neck in machining. Computer Aided Manufacturing
(CAM) was developed to quicken and automate this process, where the shape could be defined by
Computer Aided Design (CAD) software.
A NC program has thousands lines of tool movement instructions that may contain errors.
Following these instructions, CNC machine tool will move blindly, without any check on gauging,
overcut, or cutting force. It is not possible to verify code manually, so the NC verification software
was developed during 1980s. Initially CAD/CAM geometry model was tested to model machining
process but failed, since the in-process geometry of workpiece is deforming but conventional CAD
model is static [1]. How to develop a in-process geometrical model (IPM) that could simulate the
deforming workpiece has been a research challenge [2].
1. In-process Model (IPM) Evolution
The in-process model represents the state of the product at each step in the machining process. It
is a 3D geometrical construct that reflects the results of the machining operations. This model allows
the user to visually verify that the machining operations have been defined accurately and that their
sequence is correct. It can be automatically re-generated when there are changes in the product
design, machining parameters or sequence of the operations [2]. In this section, the geometric
representation techniques of IPMs for traditional manufacturing simulation are presented.

2. 1.1 Boundary Representation (B-rep)
The first choice of IPM should be naturally the geometry model used in commercial Computer
Aided Deign (CAD) system, B-rep. The benefits of using the same geometry model for CAD as the
IPM are obvious. The CAD geometry model is matured and available through CAD development kit,
so there is little need to develop a new geometry model kernel. Sharing a common geometry model
with CAD, the IPM facilitates seamless integration of CAD-CAPP-CAM. However, the creation of
B-rep IPMs took days of calculation and often failed due to Boolean operation failure. With a great
deal of research effort in the last two decades, the B-rep geometry model has been improved
significant in term of Boolean operation stability, but the B-rep based IPMs are still limited to 2.5-
axis milling [1].
1.2 Section Representation
Since the integrated B-rep IPMs can not be created inside a CAD geometry model, a new, ad-hoc
cross-section-wire-frame based approach was proposed [6-7]. The aim was to use a series of
paralleled cross-section drawing to represent 3D shapes. Calculation of intersections and trimming
between two sections are time consuming and the re-ordering of the line segments requires more
computing time. This can be improved with the regulated sections, where the line segments are
indexed by both cutter section and part section. Only the line segments with the same index are
compared and trimmed, there is no need to trim two line segments. If all the line segments fall on the
regulated nodes, there is no need to trim two line segments. The set operation can be simplified to
the comparison of two Z values, which is very fast and stable. Hence, the Z map representation of
IPM emerges [3,4].
1.3 Z Map
If all the section line segments fall on the nodes, the object surface can be represented by the Z
values of the nodes. A map of Z values represents the object geometry. In computer language terms,
the Z map can be expressed as a two-dimension array Z[i, j], where i represents the index in X
direction and j represents the index in Y direction. The XY position of the Z map can be calculated
by i or j times grid size.
Because of the simplicity of its data structure and fast computation time, the Z map model is used
by most commercial CAM software [10]. However, a Z map can not approximate vertical wall very
well since it always has a slope. This is a serious problem for milling parts since profiling nearly
always creates vertical walls [5].
1.4 Extended Z Map
Since the precision of the Z map is determined mainly by XY resolution along the vertical walls,
increasing the resolution along these walls while reducing memory is a key issue. Viewing from the
top, the vertical walls only cover a small percentage of the Z direction projection, so it should be
possible to use finer resolution along the vertical walls while maintaining a rough resolution in the
planar area. This was the initial idea for an extended Z map. The detailed description of the extended
Z map IPM can be found in two patents [6].
2. Unified In-process Model of multi machining and layered manufacturing
The limitation of the extended Z map is indeed the ‘stick representation’ of the workpiece, which
can only simulate 3 axis machining in a fixed orientation. In practice, even for a 3 axis milling
machine, there is a need to machine on six sides. So the stick method could be used to simulate 3
axis machining on one side but cannot be used to train machinist for machining operation, where flip
the workpiece is a must.

3. The term voxel represents a volume element in space decomposition geometrical model schema,
just like the term pixel denotes a picture element in raster graphics. Voxelization is the process of
converting a 3D object into a voxel model. Volume graphics, voxelization and volume rendering
have attracted considerable research works in recent years. However, all of this work is directed at
the display of volume data, mainly for medical applications. In this paper a simplified voxel-based
IPM is proposed to simulate the multi-machining processes.
3. Industry Applications
Based on this new in-process model, SIMTech has developed several practical applications for
local mould manufacturers. These include QuickCNC and QuickSeeNC, which provide “What You
See is What You Cut” functionality for shop floor machine operators and mould designers. The
system starts with a solid model of the machined part and quickly simulates and optimizes machining
processes. NC code could be selectively reverse post processed into 3D tool path graphics display
and interactively viewed, edited, and optimized. Tool paths and cutting results can be viewed from
any viewpoint and checked automatically. The machined part and the design part are compared for
the remaining stock and over cut, eliminating the need for a time-consuming test cut. QuickCNC has
been adopted by local die and mould makers for its speed and simplicity.
Fig 1: Voxel machining simulation
Fig 2: Display the remaining of stock and scallop height
4. Virtual CNC Training Applications
Based on this new in-process model, SIMTech developed a VR training sysem for training CAM
programmer and CNC machinists – Virtual CNC Training Lab. The students can simulate the milling
process and save the “machined” model for other downstream machining process. The virtual CNC
simulator also allows for different situations to be tested during training, which would be costly if
done on the machines.

4. Fig 3: Machine frame, dial indicator, and control panel
5. Sustainable Machining Technology Challenges
Sustainable machining is a material removal technology that can cut raw materials to precise
component with reduced emission of greenhouse gases, reduced use of non-renewable or toxic
materials and reduced generation of waste.
Competitive sustainable manufacturing is the fundamental enabler of the transition from economic
to sustainable development. It requires transformation of manufacturing industry towards
Knowledge-based, High-Value, Competitive, Sustainable Products and Services, Processes and
Business Models. The Competitive Sustainable Machining Technology, which is a cost effective
subset of Sustainable Machining Technologies, focuses on replacing trial cut and manual training by
virtual machining simulation, increasing productivity by high efficiency cutting, and reuse material by
re-machining of used component. Competitive Sustainable Machining reduces the cost of metal
cutting production and is a cutting edge for Asia manufacturing industry.
Faster High Efficiency Machining to Save Energy
Typically either a single conservative feed rate is used for an entire machining sequence, or a
higher feed rate is used but with a very conservative machining strategy. Both methods attempt to
ensure that the cutter is not overloaded, but at the expense of very inefficient machining. Both of
these strategies result in too slow cutting speeds or too light removal rates that waste time, increase
costs, and prematurely wear cutters. Achieving the best feed rates for each cut in an NC program has
always been a goal for NC programmers but has traditionally been a very difficult task plagued by a
number of problems. First, trying to imagine the cutter contact and cutting conditions or each cut in
a large NC program is virtually impossible. Manually inserting different feed rates for each changing
condition is not practical. An incorrect feed rate estimate can break the cutting tool, damage the
fixture, or scrap the part. Machining simulation can be used to check the cutting force and optimize
speed.
Re-machining to Save Material
The most of material and energy are wasted in the in manufacturing processes. These wastes can
be saved through re-machining of the damaged component, where the damage can be repaired by
welding or thermal spray. However, re-machining is a great challenge for CNC machining since the
damaged component geometry is warping. Re-machining techniques allow successful machining
when some geometry is unknown, by predicative machining model and simulation, using in-process
measurement to close any information gaps.
The programming of most machining operations is based around knowing three geometries: the
position of the workpiece on the machine, the starting shape, and the final shape. Re-machining
techniques allow successful machining when some geometry is unknown, by predicative machining
model and simulation, using in-process measurement to close any information gaps. They are being
applied for an increasing number of applications in the manufacture and repair of large and complex
components in the aerospace industry.

5. Towards First Part Correct, Full Automatic Machining, and No man Workshop
The ‘First Part Correct’ idea embodies much of the lean concept that we are attempting to
achieve. In the time of High-Mix Low-Volume production, there is no time for trial cut. Towards
smart and competitive sustainable machining, CNC simulation will be used to optimize the machining
process, where the raw material could be saved through First Part Correct, the energy could be
saved through cutting speed optimization, and used parts could be saved by remanufacturing.
6. Conclusion
Machining is the cutting edge of industry production and its productivity has been the focus since
industry revolution. Machining technology used to be a craft of machinist, where the precision and
productivity depended on the skill of a machinist. NC technology revolutized machining process by
shifting the control from machinist to NC programmer, where the process is digital and repeatable.
Smart machining built brain (IPM) and eyes (TCM) into machining tool, so it could be run faster.
Towards First Part Correct machining, it is possible to machine without human interference. Higher
productivity could be achieved with no-man machining operation.
References
[1] Sang C. Park, Gopalan Mukundan, Shuxin Gu and Gustav J. Olling, 2003, “In-process Model
Generation for the Process Planning of a Prismatic Part”, Journal of Advanced Manufacturing
Systems, Vol. 2, No. 2, pp. 147–162
[2] Jerard, R. B., Hussaini, S. Z., Drysdale, R. L. and Schaudt, B., “Approximate methods for
simulation and verification of numerically controlled machining programs”, Visual Computer,
5(4), pp. 329–348, 1989.
[3] Liu P.L et al, “A New Concept Integrated CAD/CAM System for Complicated Die & Mold”,
Advances in Computer Science Application to Machinery, International Academic Publisher,
1991.8, ISBN 7-8003-154-3/TH.2, pp.90-95
[4] Liu P.L. et al, “3D Complicated Parts Design Based on the Automatic Shape Generation”,
Chinese Journal of Mechanical Engineering (English Edition), Volume 5 Number 2, 1992, pp88-
92.
[5] Seung Ryol Maenga, Nakhoon Baekc, Sung Yong Shinb, Byoung Kyu Choid, “A Z-map update
method for linearly moving tools”, Computer-Aided Design 35 (2003) 995–1009
[6] Liu P.L et al, 2002, “An object representation method”, WO04032001A1
[7] Chandru, V., Manohar, S., Prakash, C. E., “Voxel-Based Modeling for Layered Manufacturing”,
IEEE Computer Graphics and Applications (1995), v.15 n.6
[8] Jang Donggo, Kim Kwangsoo, and Jung Jungmin ,”Voxel-based Virtual Multi-axis Machining”,
International Journal of Advanced Manufacturing Technology 16(10), 709-713, 2000
[9] http://www.engineeringchallenges.org

6. Towards First Part Correct, Full Automatic Machining, and No man Workshop
The ‘First Part Correct’ idea embodies much of the lean concept that we are attempting to
achieve. In the time of High-Mix Low-Volume production, there is no time for trial cut. Towards
smart and competitive sustainable machining, CNC simulation will be used to optimize the machining
process, where the raw material could be saved through First Part Correct, the energy could be
saved through cutting speed optimization, and used parts could be saved by remanufacturing.
6. Conclusion
Machining is the cutting edge of industry production and its productivity has been the focus since
industry revolution. Machining technology used to be a craft of machinist, where the precision and
productivity depended on the skill of a machinist. NC technology revolutized machining process by
shifting the control from machinist to NC programmer, where the process is digital and repeatable.
Smart machining built brain (IPM) and eyes (TCM) into machining tool, so it could be run faster.
Towards First Part Correct machining, it is possible to machine without human interference. Higher
productivity could be achieved with no-man machining operation.
References
[1] Sang C. Park, Gopalan Mukundan, Shuxin Gu and Gustav J. Olling, 2003, “In-process Model
Generation for the Process Planning of a Prismatic Part”, Journal of Advanced Manufacturing
Systems, Vol. 2, No. 2, pp. 147–162
[2] Jerard, R. B., Hussaini, S. Z., Drysdale, R. L. and Schaudt, B., “Approximate methods for
simulation and verification of numerically controlled machining programs”, Visual Computer,
5(4), pp. 329–348, 1989.
[3] Liu P.L et al, “A New Concept Integrated CAD/CAM System for Complicated Die & Mold”,
Advances in Computer Science Application to Machinery, International Academic Publisher,
1991.8, ISBN 7-8003-154-3/TH.2, pp.90-95
[4] Liu P.L. et al, “3D Complicated Parts Design Based on the Automatic Shape Generation”,
Chinese Journal of Mechanical Engineering (English Edition), Volume 5 Number 2, 1992, pp88-
92.
[5] Seung Ryol Maenga, Nakhoon Baekc, Sung Yong Shinb, Byoung Kyu Choid, “A Z-map update
method for linearly moving tools”, Computer-Aided Design 35 (2003) 995–1009
[6] Liu P.L et al, 2002, “An object representation method”, WO04032001A1
[7] Chandru, V., Manohar, S., Prakash, C. E., “Voxel-Based Modeling for Layered Manufacturing”,
IEEE Computer Graphics and Applications (1995), v.15 n.6
[8] Jang Donggo, Kim Kwangsoo, and Jung Jungmin ,”Voxel-based Virtual Multi-axis Machining”,
International Journal of Advanced Manufacturing Technology 16(10), 709-713, 2000
[9] http://www.engineeringchallenges.org