process planning for die and mold machining based on

Bulletin of the JSME

Journal of Advanced Mechanical Design, Systems, and ManufacturingVol.15, No.2, 2021

© 2021 The Japan Society of Mechanical Engineers[DOI: 10.1299/jamdsm.2021jamdsm0015]Paper No.20-00119

Process planning for die and mold machining based on pattern recognition and deep learning

Mayu HASHIMOTO* and Keiichi NAKAMOTO* *Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology

2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan

E-mail: [email protected]

Abstract Dies and molds are necessary elements in the manufacturing of current industrial products. There is increasing pressure to machine high quality complicated surfaces at low cost. The standardization of process planning is said to be a key to improving the efficiency of machining operations in practice. Thus, computer aided process planning (CAPP) systems are urgently needed to reduce the time and effort of preparing machining operations. However, it is difficult to generalize process planning that continues to depend on skillful experts and requires long preparation time for die and mold machining. On the other hand, to overcome issues that are difficult to generalize, it is well known that machine learning has the capability to estimate valid values according to past case data. Therefore, this study aims to develop a CAPP system that can determine machining process information for complicated surfaces of die and mold based on pattern recognition and deep learning, a kind of machine learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Using a voxel model representing targeted shape, it becomes easier to deal with the complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. Cutting tool type and tool path pattern are treated as machining process information determined in a CAPP system. The results of case studies confirm that the developed CAPP system is effective in determining the machining process information even for complicated surfaces according to the implicit machining know-how.

Keywords : Process planning, Deep learning, Pattern recognition, Die and mold, Machining process information

1. Introduction

There is an increasing demand for high value-added industrial products with complicated shapes. In order to create

these shapes, there is pressure to ensure the die and mold needed to manufacture them be machined with high quality and low cost. It is said that the standardization of process planning is a key to improving the efficiency of machining operations in practice. The preparation of machining operations commonly takes a long time, because commercial CAM systems require a variety of machining process information such as machining method, machining sequence, etc., to generate NC programs (Igari, et al., 2012). Therefore, a computer aided process planning (CAPP) system able to bridge CAD and CAM systems is urgently needed to reduce the time and effort involved in preparing machining operations (Isnaini and Shirase, 2014).

CAPP systems are usually expected to recognize machining features, to select suitable machining methods, and to automatically allocate machining sequences. Many studies have already been conducted to develop CAPP systems based on feature recognition technologies to detect specific areas that can be used to decide related machining process information (Isnaini, et al., 2015; Morinaga, et al., 2014; Shirase and Nakamoto, 2013). However, these rule-based CAPP systems deal with relatively simple target shapes consisting of cylinders and cuboids because it is difficult to define and recognize suitable machining features. In addition, there are usually several candidates for machining process information of complicated shapes. As a result, it is difficult to generalize the process planning that continues

Received: 28 February 2020; Revised: 21 June 2020; Accepted: 27 July 2020

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2© 2021 The Japan Society of Mechanical Engineers[DOI: 10.1299/jamdsm.2021jamdsm0015]

Hashimoto and Nakamoto, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.15, No.2 (2021)

to depend on skillful experts and requires long preparation times in die and mold machining. For example, depending on the required accuracy and the used cutting tool, it may be possible to calculate some machining process information such as feed speed, pickfeed and so on. However, it is generally difficult to find the specific reasons to decide tool path pattern to machine complicated surfaces. From the results of a number of interviews with skillful experts of a machine tool builder, it is found that they decide the tool path pattern by considering the complicated shapes with used cutting tool types on the basis of the experience of past machining cases. Similarly, the cutting tool type is thought to be decided by taking account of the complicated shapes and required accuracy. Therefore, both the cutting tool type and the tool path pattern are treated as machining process information determined in this study.

On the other hand, to overcome issues that are difficult to generalize, it is well known that machine learning has the capability to estimate valid values according to past case data. Therefore, this study aims to develop a CAPP system that can determine the cutting tool type and the tool path pattern for die and mold machining of complicated surfaces based on pattern recognition and deep learning, a kind of machine learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Using a voxel model representing targeted shape, it becomes easier to deal with the complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. Cutting tool types and tool path patterns are inferred for the complicated surfaces in the developed CAPP system. The results of case studies confirm that the developed CAPP system is effective in determining the machining process information even for complicated surfaces according to the implicit machining know-how.

2. Process planning based on deep learning

In order to overcome issues that are difficult to generalize, it is well known that machine learning is capable of estimating a valid value according to past case data. Machine learning has already been adapted to recognize machining features and to predict tool wear for complicated shapes (Ezugwu, et al., 1995; Onwubolu, 1999). As shown in Fig. 1, the authors also developed a CAPP system using past case data and a neural network that is a representative method of machine learning. In the system, a complicated surface is classified into three kinds of tool path patterns by reference to geometrical information of CAD models (Hashimoto and Nakamoto, 2019). However, a complicated surface defined in CAD software and enclosed by character lines is treated individually, and the tool path pattern is assigned for each surface without considering the adjacent surfaces and the overall geometrical features.

Therefore, this study aims to develop a CAPP system to determine machining process information for complicated surfaces of die and mold based on pattern recognition and deep learning using a multilayered neural network. Moreover, by employing a voxel model representing targeted shape, it becomes easier to deal with complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. It is thought that a voxel model with volume information could also be used to infer machining process information even for rough machining operations in future work.

In this study, a network architecture called 3D u-net (Cicek, et al., 2016) is adapted to develop a CAPP system. As shown in Fig. 2, 3D u-net is a convolutional neural network that can effectively analyze whole images by producing segmented regions. There are two main differences between 3D u-net and a general convolutional neural network. 3D u-net has a de-convolutional network part in addition to a regular convolutional network part. The convolutional network part extracts features of the image, while the de-convolutional network part divides the regions. The other

Fig. 1 Overview of developed neural network-based CAPP system (Hashimoto and Nakamoto, 2019). In the system, the target shape is input as a CAD model, and inferred tool path pattern is output as color information of the complicated surface. The neural network is trained using past machining cases of die and mold.

CAD model Neural network Tool path pattern

Geometrical information

of complicated surface

Past case data

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difference is a concatenate path that connects the output of each convolutional layer in each de-convolutional layer. This path connects pre-operation information to post-operation information and restores the whole image lost through the operation.

This architecture has analysis and synthesis paths that consist of some layers. The convolution layer extracts local features of the image. This layer contains 3×3×3 convolution, followed by a rectified linear unit (ReLU) as an activation function, which converts the linear or non-linear shape input signals, imitating human nerve cells. ReLU is defined as f(u) in the following equation.

𝑓 𝑢 𝑚𝑎𝑥 𝑢, 0 (1)

If u is less than zero, ReLU outputs zero. Otherwise, it linearly outputs a value of 𝑢. However, the last layer contains 1×1×1 convolution followed by a sigmoid function of Eq. (2) as an activation function.

𝑔 𝑢 1

1 𝑒𝑥𝑝 𝑢 (2)

The output of the sigmoid function defined as g(u) varies from 0 to 1. The pooling layer reduces the feature map and blurs positional information. In the layer, 2×2×2 max pooling with two strides is used in each dimension. The sampling layer magnifies the feature map while maintaining local features. The layer consists of 2×2×2 up sampling with strides of two in each dimension. The concatenation path connects layers of the same resolution in the same stage, and restores the whole image. Moreover, batch normalization is used before each ReLU in order to promote training by normalizing values in the network. By following the above procedure, 3D u-net is effective for the recognition of the three-dimensional local features and the whole image. The learning parameters are searched by error-correction learning in the network. The loss function is an index to search the parameters in the neural network. In this network, Eq. (3) is a loss function using the Dice coefficient of Eq. (4).

𝑓 𝑢 1 𝐷𝑖𝑐𝑒 𝑅,𝑌 (3)

𝐷𝑖𝑐𝑒 𝑅,𝑌 2|𝑅 ∩ 𝑌||𝑅| |𝑌|

(4)

The teacher signal 𝑅 is the value of training data, and the output 𝑌 is the value of output from the network.

Fig. 2 Architecture of 3D u-net based on a convolutional neural network (Cicek, et al., 2016). Blue boxes are feature maps indicating 4 dimensional data including targeted shape, required accuracy and machining process information. White boxes are the copied feature maps. The height of boxes indicates the data size and the width expresses the channel number, respectively. 3D u-net has a regular convolutional network and a de-convolutional network. Blue arrows show operations of convolution, batch normalization and activation function. Red arrows show a max pooling operation that extracts the maximum value in a specific region of feature map and reduces the size of feature map. Yellow arrows show an up sampling operation that expands the size of feature map while maintaining the maximum value. Green arrows show a concatenate operation that connects feature maps to reconstruct feature information on a whole region.

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During neural network learning, the mini-batch method (Andrew, et al., 2011) is adapted to divide a learning set into a few subsets and updates. Moreover, an optimization technique, Adam (Diederik, et al., 2017), is used in this study to smooth the adaptive movement of the searching parameters. The 3D u-net is trained using targeted shape, required accuracy and machining process information of past machining cases conducted by skillful experts of a machine tool builder to reproduce the implicit machining know-how. 3. Developed CAPP system for die and mold machining 3.1 Overview of developed system

Figure 3 presents an overview of the CAPP system developed in this study, based on pattern recognition and deep learning. In this system, complicated surfaces of CAD model are expressed by using voxels. Each voxel has channels allocated to targeted shape, required accuracy and machining process information mentioned in the following section. Complicated surfaces of CAD model designed following the axes of the CAD coordinate system, here colored in purple, are extracted first, and the lengths are detected in X, Y, and Z directions, respectively. The voxel model is

Fig. 3 Overview of the developed CAPP system based on pattern recognition and deep learning. In this system, complicated surfaces of CAD model are expressed by using voxels. 3D u-net, a form of deep learning, receives the voxel model as input data and infers machining process information for each voxel as a result of pattern recognition. Machining process information of each complicated surface is finally determined depending on the number of included voxels, and the major voxel color is given to the complicated surface of CAD model.

Voxel model of complicated surfaces

Maximum length

Length_X Length_Y

Cube size：

Maximum length / Division number

Length_Z

(a) Lengths detection of complicated surfaces of CAD model

(b) Decision of cube size of voxel model

1 1 1 0 0 0 1 0 0 0 0 1 1 1 0

0 0 1 0 0 0 0 1 1 0 0 0 1 1 1

Deep Learning

Input data using binary digits

CAD model of complicated surfaces

CAD model with determined machining process information

Voxel model of complicated surfaces

Voxel model with inferred machining process information

Output data using binary digits

3D U-net

Fig. 4 Generation of voxel model. (a) shows the lengths in X, Y, and Z directions of complicated surfaces of CAD model, and the maximum length. (b) shows cube size decided depending on the maximum length and division number.

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converted from CAD model according to the maximum length to decide the size of voxels as shown in Fig. 4. In this study, the maximum length is divided by 128 for calculating the cube size of voxels in order to obtain inference results in practical time.

3D u-net, a form of deep learning, receives the voxel model of complicated surfaces with required accuracy or cutting tool type as input data and infers machining process information for each voxel as a result of pattern recognition. Each voxel is then colored according to the channel that means the inferred machining process information, which is cutting tool type or tool path pattern in this study. The voxels included in a complicated surface of CAD model are detected, and the number of voxels is counted for each channel. Machining process information of each complicated surface is finally determined as the channel having the maximum counted number, and the voxel color is given to the complicated surface of CAD model as shown in Fig. 5. In this study, the developed CAPP system is implemented using the API of one of commercial CAD system, NX, the machine learning library of the TensorFlow, and the hyper parameter optimization library of the Optuna with C++ and python. 3.2 Input and output data of deep learning

3D u-net is trained using targeted shape, required accuracy and machining process information of past machining cases conducted by skillful experts in order to reproduce the implicit machining know-how. In this system, a complicated surface defined in CAD software and enclosed by character lines is treated individually to extract and infer machining process information. The following characteristic information items are assumed as input and output data of

Fig. 5 Determination procedure of machining process information. By counting voxels included in the complicated surface for each channel, the channel having the maximum number is determined as machining process information of the complicated surface. The scanning-line path is adopted as in this figure.

Voxel model with inferred machining process information

CAD model with determined machining process information

Complicated surface

Red Green Blue 50

Machining process information for a complicated surface

Green：Scanning-line path

2000 0

Ex. Tool path pattern

Sum of voxels included in a complicated surface

Voxel included in surface requiring high accuracy Magenta Yellow

( 1 , 0 )

Binary digit expressing targeted shape information

( 1 )

( 0 )

Voxel model Fig. 6 Targeted shape information. A voxel, included partially or completely in complicated surfaces, shows the digit 1,

and a voxel that does not exist in the surfaces completely, shows the digit 0.

Fig. 7 Required accuracy information. A voxel that requires to be machined with high accuracy in complicated surfaces becomes magenta, while a voxel that does not require to be machined with high accuracy becomes yellow.

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3D u-net. Each voxel has 6 channels consisting of one channel for targeted shape, two channels for required accuracy, two channels for cutting tool type and three channels for tool path pattern. Though these channels are determined by discussing with skillful experts, it is assumed that the number of channels and those kinds are arbitrary adjusted according to an intended machining operation.

3.2.1 Targeted shape As shown in Fig. 6, a voxel has one channel for targeted shape of complicated surfaces, and targeted shape is

expressed as the binary digit. The digit 1 means the voxel is included partially or completely in the complicated surfaces. On the other hand, the digit 0 indicates the voxel does not exist in the complicated surfaces completely 3.2.2 Required accuracy

As shown in Fig. 7, a voxel has two channels for required accuracy of complicated surfaces, and required accuracy is expressed by 2 channels having respective binary digits. In this study, skillful experts decide complicated surfaces where required accuracy is high by considering the assigned surface roughness and tolerance on the basis of the experience of past machining cases. Depending on the required accuracy of the complicated surfaces, either channel becomes 1 for voxels included in the complicated surfaces as follows.

High accuracy required: Magenta High accuracy unrequired: Yellow

3.2.3 Cutting tool type

The cutting tool type is considered as machining process information, cutting tools are classified into two groups: a ball endmill having spherical cutting edges of the tool tip; and a flat endmill having flat cutting edges of the tool tip. The colors orange and cyan are allocated by 2 channels to identify the cutting tool type as follows:

Ball endmill: Orange Flat endmill: Cyan

3.2.4 Tool path pattern The considered tool path patterns are classified into three groups: contour-line, scanning-line, and along-surface.

Red, green, and blue are allocated by 3 channels to identify each tool path pattern as follows: Contour-line pattern: Red Scanning-line pattern: Green Along-surface pattern: Blue The contour-line pattern comprises tool paths on the same level plane along the tool axis direction, as shown in Fig.

8(a). In addition, the scanning-line pattern comprises tool paths that proceed in one direction, following the complicated surface sequentially, as shown in Fig. 8(b). In this study, the cutting directions are not distinguished in the scanning-line pattern, including zigzag paths. Moreover, the along-surface pattern comprises smooth paths with equal pitch following the complicated surface, as shown in Fig. 8(c).

(a) Contour-line pattern (b) Scanning-line pattern (c) Along-surface pattern

Tool path

Complicated surface Tool path

Tool path

Complicated surface Complicated surface

Fig. 8 Tool path pattern considered in this study. (a) shows tool paths of contour-line pattern for a red-colored complicated surface. (b) shows tool paths of scanning-line pattern for green-colored complicated surfaces. (c) shows tool paths of along-surface pattern for a blue-colored complicated surface.

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3.3 Inference of machining process information Figure 9 shows the inference flow of cutting tool type based on pattern recognition and deep learning in this study.

The targeted shape and required accuracy information of complicated surfaces are extracted from the CAD model and set to voxels included in the complicated surfaces as input data. After the inference by deep learning, the cutting tool type is obtained as the output data, and then cutting tool types of the complicated surfaces are determined for the CAD model. Furthermore, tool path pattern is inferred as shown in Fig. 10. The targeted shape information and cutting tool type obtained above of the complicated surfaces are set to voxels included in the complicated surfaces as input data. The tool path pattern is similarly inferred as the output of deep learning, and tool path patterns of the complicated surfaces are finally determined for the CAD model.

3.4 Hyper parameters optimization

In this study, a suitable combination of hyper parameters is searched in order to optimize the network architecture according to the inferred machining process information. Hyper parameters, which are not determined by training of past machining cases, are required for the optimization before the inference.

Table 1 summarizes the details of hyper parameters optimization in this study. Three hyper parameters, number of stages, number of convolutions, and dropout rate, are optimized. For example, a 3D u-net shown in Fig. 11 has three stages that convolutional layers or de-convolutional layers are connected by max pooling or up sampling, two convolutions in each convolution layer in the architecture. Dropout is a way to prevent the network from overfitting by deleting it depending on the ratio determined at training (Srivastava, et al., 2014). The number of combinations, number of repeated generations, and batch size of learning are fixed as 10 times, 100 times, and 4, respectively. The optimization technique is a tree-structured parzen estimator (TPE) (Bergstra, et al., 2011). Each network is optimized for the inference of the cutting tool type and tool path pattern, respectively.

Targeted shape Output data Tool path pattern

Deep

Learning

Cutting tool type

Input data

Fig. 9 Inference flow of cutting tool type. Targeted shape and required accuracy information are input to the network using deep learning, and cutting tool type is obtained after the inference.

Targeted shape Output data Cutting tool type

Deep

Learning

Required accuracy

Input data

Fig. 10 Inference flow of tool path pattern. Targeted shape information and cutting tool type are input to the network using deep learning, and tool path pattern is obtained after the inference.

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4. Case study

A case study is conducted to confirm the usefulness of the developed CAPP system for determining machining process information for complicated surfaces of die and mold according to the implicit machining know-how. The inferred machining process information, cutting tool types and tool path patterns are compared to those of past machining cases determined by skillful experts. 4.1 Dataset

The network is trained using 243 CAD models of past machining cases provided by a machine tool builder. Figure 12 shows examples of complicated surfaces of die and mold, including targeted shape, required accuracy, cutting tool type, and tool path pattern as machining process information assigned for CAD models in the machining cases. Complicated surfaces considered in this study are colored in purple. Moreover, required accuracy (magenta or yellow), cutting tool type (orange or cyan) and tool path pattern (red, green or blue) are colored in the corresponding colors according to machining process information described in Section 3.2. Machining process information for complicated surfaces are uniformly decided by one skilled expert.

As shown in Fig.13, 172 machining cases are used as training data in the optimization of hyper parameters, and 31 machining cases are used for validation to compare the network performance. Then, 203 machining cases are used to train the network to infer machining process information, and 40 machining cases remained for evaluating the inferred machining process information. This dataset is randomly divided.

Item Variable

Hyper-parameter Number of stages 3, 4, 5

Number of convolutions 1, 2, 3 Dropout rate 0, 0.2, 0.5

Fixed condition

Number of combinations 10 Number of repeated generations 100

Batch size 4 Optimization technique TPE

Table 1 Hyper parameters optimization. Three kinds of hyper parameters, number of stages, number of convolutions, and dropout rate are investigated before the inference of machining process information.

3 stages

2 convolutions

Stage 0

Stage 1

Stage 2

Fig. 11 Hyper parameters of 3D u-net optimized in this study. This network has three stages connected by max pooling and up sampling, and two convolutions in each convolution layer in the architecture.

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Validation data

172 31 40Hyperparameters

optimization

Training data

All machining case

243

203 40Inference of

process information

Training data Validation data

Fig. 12 Examples of complicated surfaces of die and mold, including targeted shape, required accuracy, cutting tool type, and tool path pattern as machining process information assigned for CAD models in past machining cases used in case study. (a) shows target shape colored in purple. (b) shows high accuracy required complicated surfaces colored in magenta, otherwise yellow. (c) shows cutting tool type (ball endmill in orange, and flat endmill in cyan). (d) shows tool path pattern (contour-line pattern in red, scanning-line pattern in green, and along-surface pattern in blue).

(a) Targeted shape (b) Required accuracy (c) Cutting tool type

Model A

Model B

Model C

Targeted shape High accuracy unrequired

High accuracy required Ball endmill

Flat endmill

Contour-line pattern Scanning-line pattern

Along-surface pattern

(d) Tool path pattern

Inference of machining process information

Fig. 13 Dataset of 243 machining cases for case study. In the optimization of hyper parameters, 172 and 31 machining cases are used for training and validation, respectively. In the inference of machining process information, 203 and 40 cases are used for training and validation, respectively. This dataset is randomly divided.

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4.2 Inference of cutting tool type

Hyper parameters of a network for inference of cutting tool type are optimized as summarized in Table 2. Although combinations of hyper parameter are listed with the validation accuracy that the inferred machining process information matches those of the past cases of validation data. It is noted that the trainings of the 8th and 9th trials are pruned because there is no possibility of success. After the optimization, it is determined that 4 stages, 3 convolutions, and a dropout rate of 0.0 are the best combination to obtain high validation accuracy. These parameters are then used in the network for inference of cutting tool type. The number of repeated generations of optimization is set as 200 and the batch size of learning is 4.

Cutting tool type is inferred using the 3D u-net with the optimized hyper-parameters. The obtained average validation accuracy of 40 validation data is about 97.8%. Figure 14 shows an example of a voxel model with inferred cutting tool types and a voxel model expressing used cutting tool types of a past machining case. Moreover, the average validation accuracy of determined cutting tool types is about 98.6% after voxel models are converted to CAD models. Figure 15 shows an example of a CAD model with finally determined cutting tool types and cutting tool types used in past machining cases.

Table 2 Optimization of hyper parameters for inference of cutting tool type. Number of stages, number of convolutions, and dropout rate are investigated to obtain high validation accuracy.

Trial Number of stages

Number of convolutions Dropout rate Validation

accuracy 1 4 3 0.2 0.8096 2 4 2 0.2 0.8293 3 5 2 0.5 0.4111 4 4 1 0.2 0.7826 5 4 3 0.0 0.9696 6 4 2 0.5 0.4144 7 3 2 0.2 0.6129

8* PRUNED 9* PRUNED 10 5 1 0.0 0.6627

* No. 8 and 9 trials are pruned as combinations without possibility.

(a) Inferred cutting tool types (b) Used cutting tool types in past machining case

Ball endmill Flat endmill

Fig. 14 Evaluation of inferred cutting tool types by comparison with those in past machining case (voxel model). (a) shows voxel model with inferred cutting tool types (orange-colored voxel indicates ball endmill and cyan-colored voxel indicates flat endmill). (b) shows voxel model with cutting tool types used in past machining case.

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Thus, it is found that the cutting tool types for the complicated surfaces are successfully determined with a high probability according to targeted shape, required accuracy in past machining cases using the developed CAPP system based on pattern recognition and deep learning. 4.3. Inference of tool path pattern

The hyper parameters of a network for inference of tool path pattern are similarly optimized as summarized in Table 3. The trainings of the 8th trial are again pruned as a combination without possibility. Following the optimization, it is found that number of stages of 5, number of 3 convolutions, and dropout rate of 0.0 are the best combination to obtain high validation accuracy. These parameters are then used in the network for inference of tool path pattern. The number of repeated generations of optimization is set at 500 times, and the batch size of learning is 4.

Tool path pattern is inferred using 3D u-net with the optimized hyper parameters. The obtained average validation accuracy of 40 validation data is about 87.3%. Figure 16 shows an example of a voxel model with inferred tool path patterns and a voxel model expressing used tool path patterns in a past machining case. Moreover, the average

Trials Number of stages

Number of convolutions Dropout rate Validation

accuracy 1 5 2 0.0 0.7659

2 3 2 0.5 0.3273

3 3 1 0.5 0.2838

4 5 3 0.0 0.7834

5 4 3 0.0 0.7787

6 5 3 0.2 0.5254

7 5 3 0.5 0.3177

8* PRUNED

9 5 1 0.2 0.4481

10 5 3 0.2 0.6073

* No. 8 trials is pruned as combinations without possibility.

Table 3 Optimization of hyper parameters for inference of tool path pattern. Number of stages, number of convolutions, and dropout rate are investigated to obtain high validation accuracy.

(a) Determined cutting tool types (b) Used cutting tool types in past machining case

Ball endmill Flat endmill

Fig. 15

Evaluation of determined cutting tool types by comparison with those in past machining case (CAD model). (a) shows CAD model with determined cutting tool types (orange-colored surface machined using ball endmill and cyan-colored surface machined using flat endmill) (b) shows CAD model with cutting tool types used in past machining case.

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validation accuracy of determined tool path patterns is about 74.3% after voxel models are converted to CAD models. Figure 17 shows an example of a CAD model with finally determined tool path patterns and tool path patterns used in a past machining case. Compared to the determined cutting tool types, the average validation accuracy here is relatively low, because tool path pattern is extremely sensitive to the curvature of complicated surfaces flattened due to quantization errors in a voxel model.

Nevertheless, it is recognized tool path patterns of complicated surfaces can be determined with a high probability based on pattern recognition and deep learning by using targeted shape and cutting tool type in past machining cases.

5. Conclusions

A computer aided process planning (CAPP) system is urgently needed to reduce time and effort involved in the preparation of machining operations. However, it is difficult to deal with complicated shapes by recognizing machining features on rule-based CAPP systems, and there are usually several candidates for machining process information of complicated shapes of die and mold. Thus, this study aimed to develop a CAPP system to determine the cutting tool type and the tool path pattern for die and mold machining based on pattern recognition and deep learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Usage of a voxel model of complicated surfaces makes it easier to deal with them in die and mold generally and

(a) Inferred tool path pattern (b) Past machining case

Contour-line pattern

Scanning-line pattern


Fig. 16

Evaluation of inferred tool path patterns by comparison with those in past machining case (voxel model). (a)

shows voxel model with inferred tool path patterns (red-colored voxel indicates contour-line pattern,

green-colored voxel indicates scanning-line pattern and blue-colored voxel means along-surface pattern) (b)

shows voxel model with tool path patterns used in past machining case.

(a) Inferred tool path pattern (b) Past machining case

Contour-line pattern

Scanning-line pattern


Fig. 17

Evaluation of determined tool path patterns by comparison with those in past machining case (CAD model). (a) shows CAD model with determined tool path patterns (red-colored surface is machined by contour-line pattern, green-colored surface by scanning-line pattern and blue-colored surface by along-surface pattern). (b) shows CAD model with tool path patterns used in past machining case.

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three-dimensionally as skillful experts pay attention to whole geometrical features. The results of case studies confirm that the developed CAPP system is effective in determining the cutting tool type and the tool path pattern even for complicated surfaces according to the implicit machining know-how in past machining cases.

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process planning for die and mold machining based on

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