BACKGROUND

Field of Invention

The disclosure relates to an additive manufacturing (AM) system, an AM method and an AM feature extraction method, and more particularly, to an AM system, an AM method and an AM feature extraction method that support virtual metrology (VM).

Description of Related Art

Additive manufacturing (AM), also referred to as 3D-printing, is a technique of heating metal powders or plastic material to be melt-shapeable after a digital computing model file is built, and then fabricating a workpiece by stacking layers. A powder bed fusion process is one of popular additive manufacturing techniques. The powder bed fusion process may be such as a selective laser melting (SLM) process, or a selective laser sintering (SLS) process. The selective laser melting process is performed by placing powders on a substrate, and using a high energy laser to irradiate a position at which a powder molding is desired to be formed, thereby melting and fusing the powders. The selective laser sintering process is also performed by using a laser to irradiate powders to sinter and fuse the powders into blocks, and then placing another layer of powders thereon to repeat the laser process until the product is formed.

A conventional AM tool lacks an online (on production line) tuning mechanism. While fixed values of process parameters (such as laser power, scan strategy, layer thickness, and scan speed, etc.) are used by the conventional AM tool for production, the production quality of the AM tool would vary with process variations (such as power distribution, flow control, and moisture content). A conventional AM technique performs quality measurements only after the products are completed for ensuring production quality. However, additive manufactured products are made by processing powders layer by layer, and thus poor processing quality of one certain layer often affect the quality of end product. Therefore, there is a need to provide an AM system, an AM method and an AM feature extraction method for obtaining product quality in time to adjust values of process parameters of an AM tool on a production line.

SUMMARY

An object of the disclosure is to provide an AM system, an AM method and an AM feature extraction method, thereby obtaining product quality in time such that values of process parameters of a AM tool can be adjusted on a production line.

According to the aforementioned object, an aspect of the disclosure is to provide an additive manufacturing (AM) system. The AM system includes an AM tool, a product metrology system, an in-situ metrology system and a virtual metrology system. The AM tool is configured to fabricate workpiece products, in which the AM tool includes a power bed and a fusion system. The power bed includes a powder bed container in which powder layers powder layers that are stacked on each other are held. The fusion system is configured to direct an energy beam to powder bodies on each of the powder layers respectively after the each of the powder layers is placed on the powder bed during a process of fabricating each of the workpiece products, thereby melting the powder bodies to form melt pools. The product metrology system is configured to measure qualities of the workpiece products respectively after the workpiece products are completely fabricated, thereby obtaining actual metrology values. The in-situ metrology system includes a pyrometer, a coaxial camera, an overview camera and an in-situ metrology server. The pyrometer is configured to perform a temperature measurement on each of the melt pools on the powder bed during a fabrication of each of the workpiece products, thereby obtaining a temperature of each of the melt pools of each of the workpiece products. The camera is configured to perform photography on each of the melt pools on the powder bed during the fabrication of each of the workpiece products, thereby obtaining an image of each of the melt pools of each of the workpiece products. The overview camera is configured to perform photography on each of the powder layers after the each of the powder layers is placed on the powder bed and before the energy beam is applied to the each of the powder layers, thereby obtaining plural workspace images of the powder layers of each workpiece product during the fabrication of the each workpiece product. The in-situ metrology server includes a workspace image processing device, an image-feature extraction device and a melt-pool feature processing device. The image-feature extraction device is configured to extract a length and a width of each of the melt pools from the image of each of the melt pools, and to correspond the length and the width to the temperature. The melt-pool feature processing device is configured to convert the length, the width and the temperature of each of the melt pools to a melt-pool length feature, a melt-pool width feature and a melt-pool temperature feature of each of the workpiece products. The workspace image processing device is configured to first convert each of the workspace images to a gray level co-occurrence matrix (GLCM); and then calculate a homogeneity index of each powder layer of each workpiece product from the GLCM. The virtual metrology system is configured to use plural sets of process data and the actual metrology values of the workpiece products to predict a virtual metrology value of a next workpiece product processed by the AM tool in accordance with a prediction algorithm after the workpiece products have been fabricated by the AM tool, in which the sets of process data includes the homogeneity index of each powder layer of each of the workpiece products, the melt-pool length feature, the melt-pool width feature and the melt-pool temperature feature of each of the workpiece products.

In some embodiments, the AM system includes a simulator, a compensator, a track planner and a controller. The simulator is configured to perform a simulation operation based on the sets of process data and/or the actual metrology values of the workpiece products, thereby generating a set of suggested parameter ranges. The compensator is configured to generate a set of process-parameter adjusted values based on the virtual metrology value. The track planner is configured to generate a set of process-parameter tracking values based on the set of process-parameter adjusted values, the set of suggested parameter ranges and a set of parameter design values. The controller is configured to control and adjust the AM tool to process the next workpiece product in accordance with the set of process-parameter tracking values.

According to the aforementioned object, another aspect of the disclosure is to provide an AM feature extraction method. In the AM feature extraction method, a temperature measurement is performed on each of melt pools formed on each of powder layers stacked on a powder bed during a fabrication of a workpiece product, thereby obtaining a temperature of each of the melt pools of the workpiece product; photograph is performed on each of the melt pools on the powder bed during the fabrication of the workpiece product, thereby obtaining images of the melt pools of the workpiece product; and photography is performed on each of the powder layers after the each of the powder layers is placed on the powder bed and before the energy beam is applied to the each of the powder layers, thereby obtaining plural workspace images of the powder layers of each workpiece product during the fabrication of the each workpiece product. Then, each of the workspace images is first converted to a gray level co-occurrence matrix (GLCM); and then a homogeneity index of each powder layer of each workpiece product is calculated based on the GLCM. Meanwhile, a length and a width of each of the melt pools are extracted from the images. Thereafter, a melt-pool feature processing operation is performed to convert the length, the width and the temperature of each of the melt pools to a melt-pool length feature, a melt-pool width feature and a melt-pool temperature feature of the workpiece product.

In some embodiments, the aforementioned melt-pool length feature, melt-pool width feature and melt-pool temperature feature include a maximum value, a minimum value, a mean value, a variance, a standard deviation, a skewness of statistic distribution, a kurtosis of statistic distribution, a full distance and/or a set of quantile of lengths of the melt pools in each of the at least one predetermined area; a maximum value, a minimum value, a mean value, a variance, a standard deviation, a skewness of statistic distribution, a kurtosis of statistic distribution, a full distance and/or a set of quantile of widths of the melt pools in each of the at least one predetermined area; and a maximum value, a minimum value, a mean value, a variance, a standard deviation, a skewness of statistic distribution, a kurtosis of statistic distribution, a full distance and/or a set of quantiles of temperatures of the melt pools in each of the at least one predetermined area.

In some embodiments, the aforementioned AM feature extraction method further includes extracting a central location of each of the melt pools from the image of each of the melt pools; and performing the melt-pool feature processing operation to convert the central location of each of the melt pools to a central-location feature of the workpiece product.

According to the aforementioned object, another aspect of the disclosure is to provide an AM method. In the AM method, an AM tool is used to fabricate workpiece products, wherein the workpiece products are divided into first workpiece products and a second workpiece product, and the second workpiece product is fabricated after the first workpiece products. An operation of fabricating each of the workpiece products includes placing powder layers layer by layer on a powder bed; and directing an energy beam to powder bodies on each of the powder layers sequentially after the each of the powder layers is placed on the powder bed to melt powder bodies to form melt pools. Then, qualities of the first workpiece products are measured respectively after the first workpiece products are completely fabricated, thereby obtaining actual metrology values of the first workpiece products. A temperature measurement is performed on each of the melt pools on the powder bed during a fabrication of each of the workpiece products, thereby obtaining a temperature of each of the melt pools of each of the workpiece products; photography is performed on each of the melt pools on the powder bed during the fabrication of each of the workpiece products, thereby obtaining an image of each of the melt pools of each of the workpiece products; and photography is performed on each of the powder layers after the each of the powder layers is placed on the powder bed and before the energy beam is applied to the each of the powder layers, thereby obtaining a workspace image of each powder layer of each workpiece product during the fabrication of the each workpiece product. Then, each of the workspace images is first converted to a gray level co-occurrence matrix (GLCM); and then a homogeneity index of each powder layer of each workpiece product is calculated based on the GLCM. Meanwhile, a length and a width of each of the melt pools are extracted from the image of each of the melt pools. Then, a melt-pool feature processing operation is performed to convert the length, the width and the temperature of each of the melt pools to a melt-pool length feature, a melt-pool width feature and a melt-pool temperature feature of each of the workpiece products. Then, a conjecture model is built by using plural sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm, in which the sets of first process data include the melt-pool length feature, the melt-pool width feature and the melt-pool temperature feature of each of the first workpiece products. Thereafter, a virtual metrology value of the second workpiece product is predicted by using the conjecture model based on a set of second process data, in which the set of second process data includes the melt-pool length feature, the melt-pool width feature and the melt-pool temperature feature of the second workpiece product.

In some embodiments, the AM further includes performing a simulation operation based on the sets of process data and/or the actual metrology values of the workpiece products, thereby generating a set of suggested parameter ranges; generating a set of process-parameter adjusted values based on the virtual metrology value; generating a set of process-parameter tracking values based on the set of process-parameter adjusted values, the set of suggested parameter ranges and a set of parameter design values; and controlling and adjusting the AM tool to process the second workpiece product in accordance with the set of process-parameter tracking values.

Thus, with the applications of the embodiments of the disclosure, the quality of an end product or a product that is being processed layer by layer can be obtained in time, and thus process parameters of an AM tool can be adjusted on a production line, thereby increasing yield.

It is to be understood that both the foregoing general description and the following detailed description are by examples, and are intended to provide further explanation of the invention as claimed.

