Device manufacturing cycle time reduction using machine learning techniques

Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

FIELD

The field relates generally to information processing systems, and more particularly to techniques for processing resource information in such systems.

BACKGROUND

Commonly, the manufacturing process for computing devices such as laptop and/or desktop computers includes stacking manufactured items after a building stage, organizing the stacked items for software installations, and de-stacking the items for testing and packaging. However, conventional manufacturing management approaches face challenges including, for example, efficiently identifying empty shelf space for stacking manufactured items. Such challenges result in delays in the overall manufacturing cycle time, inefficient use of human labor, and non-optimal usage of manufacturing resources.

SUMMARY

Illustrative embodiments of the disclosure provide techniques for device manufacturing cycle time reduction using machine learning techniques. An exemplary computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment, determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input, and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Illustrative embodiments can provide significant advantages relative to conventional manufacturing management approaches. For example, challenges associated with delays in manufacturing cycle time, inefficient use of human labor, and non-optimal usage of manufacturing resources are overcome in one or more embodiments through determining manufacturing resource availability in real-time using machine learning models and other machine learning techniques.

DETAILED DESCRIPTION

Illustrative embodiments will be described herein with reference to exemplary information processing system and associated processing platforms, and other types of processing devices. It is to be appreciated, however, that the invention is not restricted to use with the particular illustrative information processing system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, any system comprising multiple networked processing devices.

FIG. 1shows an information processing system100(which can, for example, be associated with at least one manufacturing environment) configured in accordance with an illustrative embodiment. The information processing system100comprises a plurality of camera devices102-1,102-2, . . .102-M, collectively referred to herein as camera devices102, and a plurality of user devices142-1,142-2, . . .142-N, collectively referred to herein as user devices142. The camera devices102and user devices142are coupled to a network, where the network in this embodiment is assumed to represent a sub-network or other related portion of the information processing system100. Also coupled to such a network is processing platform103.

The user devices142may comprise, for example, mobile telephones, laptop computers, tablet computers, desktop computers or other types of computing devices. Such devices are examples of what are more generally referred to herein as “processing devices.” Some of these processing devices are also generally referred to herein as “computers.”

The user devices142in some embodiments comprise respective computers associated with a particular company, organization or other enterprise. In addition, at least portions of the information processing system100may also be referred to herein as collectively comprising an “enterprise network.” Numerous other operating scenarios involving a wide variety of different types and arrangements of processing devices and networks are possible, as will be appreciated by those skilled in the art.

Additionally, the processing platform103can have an associated database configured to store data pertaining to manufacturing resources. A database in such an embodiment can be implemented using one or more storage systems associated with the processing platform103. Such storage systems can comprise any of a variety of different types of storage including network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage.

Also associated with the processing platform103, in one or more embodiments, are input-output devices, which illustratively comprise keyboards, displays or other types of input-output devices in any combination. Such input-output devices can be used, for example, to support one or more user interfaces to the processing platform103, as well as to support communication between the processing103and other related systems and devices not explicitly shown.

Additionally, the processing platform103in theFIG. 1embodiment is assumed to be implemented using at least one processing device. Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of the processing platform103.

More particularly, the processing platform103in this embodiment each can comprise a processor coupled to a memory and a network interface.

One or more embodiments include articles of manufacture, such as computer-readable storage media. Examples of an article of manufacture include, without limitation, a storage device such as a storage disk, a storage array or an integrated circuit containing memory, as well as a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals.

The network interface allows the processing platform103to communicate over the network with the camera devices102and the user devices142, and illustratively comprises one or more conventional transceivers.

As also depicted inFIG. 1, the processing platform103further comprises a manufacturing resource status determination component105, a stream-processing component115, and a web user interface (UI)125. As illustrated, the manufacturing resource status determination component105includes an image processing component107and a machine learning model109, while the stream-processing component115includes a stream-processing production module117and a web UI updating component119. In at least one embodiment, video and/or image input from the camera devices102is obtained by and/or provided to the image processing component107, which processes the input in conjunction with the machine learning model109(as further detailed herein). The resulting output generated by the machine learning model109is provided to the stream-processing production module117of stream-processing component115. As further described herein, the stream-processing production module117processes the machine learning model output, and based at least in part on that processing, provides an input to the web UI updating component119, which uses that input to make one or more updates to the web UI125. The web UI125is then utilized to communicate with one or more of the user devices142(for example, via an application installed on the user devices142).

It is to be appreciated that this particular arrangement of elements105,115and125illustrated in the processing platform103of theFIG. 1embodiment is presented by way of example only, and alternative arrangements can be used in other embodiments. For example, the functionality associated with the elements105,115and125in other embodiments can be combined into a single module, or separated across a larger number of modules. As another example, multiple distinct processors can be used to implement different ones of the elements105,115and125or portions thereof.

Additionally, at least portions of the manufacturing resource status determination component105and the stream-processing component115, for example, may be implemented at least in part in the form of software that is stored in memory and executed by a processor.

It is to be understood that the particular set of elements shown inFIG. 1for device manufacturing cycle time reduction using machine learning techniques involving information processing system100is presented by way of illustrative example only, and in other embodiments additional or alternative elements may be used. Thus, another embodiment includes additional or alternative systems, devices and other network entities, as well as different arrangements of modules and other components.

An exemplary process utilizing manufacturing resource status determination component105, stream-processing component115, and web UI125of an example processing platform103in information processing system100will be described in more detail with reference to the flow diagram ofFIG. 7.

As such, at least one embodiment of the invention includes determining the status of rack shelves (that is, whether the rack shelves are empty, full, or partially full) through image analysis using machine learning techniques (e.g., one or more deep learning algorithms). Additionally, such an embodiment includes outputting the status determination via, for example, displaying the rack availability information on one or more user devices (e.g., mobile devices) associated with one or more users within a manufacturing facility. Accordingly, one or more embodiments include empty rack detection using artificial intelligence (AI) and/or machine learning techniques.

By way of illustration, and as further detailed below,FIG. 2throughFIG. 6provide example pseudocode for carrying out multiple functions of processing platform103.

FIG. 2shows example pseudocode for converting video inputs to image frames in an illustrative embodiment. In such an embodiment, pseudocode200is executed by or under the control of a processing platform, such as processing platform103, or another type of processing platform. For example, the pseudocode200may be viewed as comprising a portion of a software implementation of at least part of manufacturing resource status determination component105of theFIG. 1embodiment.

The pseudocode200illustrates example steps for converting video inputs to frames. Specifically, for training purposes, a model can require a video of a shelf for a certain amount of time (depending, for example, on the frequency of filling of racks). The model would take the frequency as input, convert the video frames to images, and store the images in a directory. A user can then be prompted for the images to be labelled, and the resulting labelled images can be stored in a folder system. It is to be appreciated that this particular pseudocode shows just one example implementation of a process for converting video inputs to frames, and alternative implementations of the process can be used in other embodiments.

FIG. 3shows example pseudocode for identifying areas of interest in image frames in an illustrative embodiment. In such an embodiment, pseudocode300is executed by or under the control of a processing platform, such as processing platform103, or another type of processing platform. For example, the pseudocode300may be viewed as comprising a portion of a software implementation of at least part of manufacturing resource status determination component105of theFIG. 1embodiment.

The pseudocode300illustrates example steps for identifying areas of interest in image frames. Specifically, the images obtained from the video input will contain one or more racks/shelves along with portions of the surrounding/background environment. As shown in the example pseudocode300, a shelf is extracted from the background environment via a configurable step wherein the user has an option of cropping the shelf using a graphical user interface (GUI) or implementing a model to extract the shelf using a median filter applied to the image(s) followed by application of a canny edge detection technique, and subsequently identifying and/or determining contour information to obtain/extract the shelf. It is to be appreciated that this particular pseudocode shows just one example implementation of a process for identifying areas of interest in image frames, and alternative implementations of the process can be used in other embodiments.

FIG. 4shows example pseudocode for determining coordinates of manufacturing resources (for example, racks and/or shelves) in an illustrative embodiment. In such an embodiment, pseudocode400is executed by or under the control of a processing platform, such as processing platform103, or another type of processing platform. For example, the pseudocode400may be viewed as comprising a portion of a software implementation of at least part of manufacturing resource status determination component105of theFIG. 1embodiment.

The pseudocode400illustrates example steps for identifying coordinates of each extracted rack/shelf. Specifically, after extracting the rack/shelf from the surrounding environment (as detailed above in connection withFIG. 3), at least one embodiment includes determining and/or identifying the coordinates of each extracted rack/shelf. For this task, the labelled images are utilized (as described, for example, in connection withFIG. 2). By way merely of example, assume a shelf has four racks. To determine the coordinates of rack-1, a mask with rack-1filled labelled images and a mask with empty rack images for rack-2, rack-3and rack-4are utilized. The resulting image provides a region of interest (ROI) associated with rack-1. Additionally, contour detection is carried out on the resulting image, and based at least in part on that contour detection, coordinates of rack-1are determined. It is to be appreciated that this particular pseudocode shows just one example implementation of a process for determining coordinates of extracted manufacturing resources, and alternative implementations of the process can be used in other embodiments.

FIG. 5shows example pseudocode for extracting an area of interest from identified coordinates in an image in an illustrative embodiment. In such an embodiment, pseudocode500is executed by or under the control of a processing platform, such as processing platform103, or another type of processing platform. For example, the pseudocode500may be viewed as comprising a portion of a software implementation of at least part of manufacturing resource status determination component105of theFIG. 1embodiment.

The pseudocode500illustrates example steps for extracting the ROI from identified coordinates. A ROI, as used in this context, includes a subset of an image or a dataset identified for a particular purpose. Specifically, for the purpose of generating one or more resource status-related predictions, at least one example embodiment includes extracting individual racks from a shelf using the coordinates determined, for example, via pseudocode400. As detailed in pseudocode500, such an embodiment can include using a crop function with one or more bounding box parameters. After determining the ROI associated with one or more individual racks, such information is provided as input to a CNN model for training and/or implementation (as detailed, for example, in connection withFIG. 6below). It is to be appreciated that this particular pseudocode shows just one example implementation of a process for extracting an area of interest from identified coordinates in an image, and alternative implementations of the process can be used in other embodiments.

FIG. 6shows example pseudocode600for applying a CNN model on an extracted area of interest to determine manufacturing resource availability and example pseudocode601for performing a CNN model evaluation in an illustrative embodiment. In such an embodiment, pseudocode600and601are executed by or under the control of a processing platform, such as processing platform103, or another type of processing platform. For example, the pseudocode600and601may be viewed as comprising a portion of a software implementation of at least part of manufacturing resource status determination component105of theFIG. 1embodiment.

The pseudocode600illustrates example steps for applying a CNN model to data pertaining to a determined ROI to predict whether the corresponding rack/shelf is empty or not. As referred to herein, a CNN is a specific type of artificial neural network that uses at least one machine learning unit algorithm for supervised learning to analyze data. In one or more embodiments, the CNN can be configured dynamically by a user. Additionally, in such an embodiment, the CNN is a combination of convolutional, dense, pooling and optimizing layers, and further includes a sigmoid layer for binary classification. The CNN can also be modified (by the user) by adding and/or deleting one or more intermediate layers to fit at least one user and/or enterprise need.

The pseudocode601illustrates example steps for evaluating the CNN model. Specifically, once the model is trained, two graphs (one for accuracy and one for loss) can be generated. The user can evaluate the loss and accuracy values, and retrain the model with one or more changes by adding and/or removing layers to and/or from the model until the user is satisfied.

It is to be appreciated that this particular pseudocode shows just one example implementation of a process for utilizing and evaluating a CNN model, and alternative implementations of the process can be used in other embodiments.

Accordingly, as detailed herein, at least one embodiment includes determining manufacturing resource (e.g., rack) coordinates by reading an image as a gray scale, resizing the images, applying a median blur filter to the resized image, applying one or more other image processing noise reduction methods (if necessary and/or desired by a user), applying a canny edge detection method to the filtered image, and determining the contours and coordinates of the resulting image. In such an embodiment, using a canny edge detection method enables removal of noise in an image, and facilitates detection of the edges in a noisy state by applying a thresholding method.

As also detailed herein, such an embodiment can additionally include implementing a CNN model to determine and/or predict whether the manufacturing resource (e.g., rack) is empty, full, or partially full. In such an embodiment, an image is passed through a series of convolutional, nonlinear, pooling and fully connected layers of the CNN model, which then generates an output including a prediction of whether the manufacturing resource in the input image is empty, full, or partially full. In at least one embodiment, for example, the CNN model includes eleven layers: two convolutional layers, four activation layers, two pooling layers, two dense layers, and a sigmoid layer.

FIG. 7is a flow diagram of a process for device manufacturing cycle time reduction using machine learning techniques in an illustrative embodiment. It is to be understood that this particular process is only an example, and additional or alternative processes can be carried out in other embodiments.

In this embodiment, the process includes steps700through704. These steps are assumed to be performed by the processor platform103.

Step700includes obtaining video input related to one or more manufacturing resources in a manufacturing environment.

Step702includes determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input. In at least one embodiment, the one or more machine learning models include one or more convolutional neural network models. Also, in such an embodiment, applying the one or more convolutional neural network models to the obtained video input includes passing the obtained video input through a series of one or more convolutional, nonlinear, pooling layers and one or more fully connected layers.

Also, in at least one embodiment, determining the availability status information includes converting the video input to one or more image frames. In such an embodiment, determining the availability status information also includes identifying at least one area of interest in the one or more image frames, and determining coordinates of the at least one manufacturing resource in the at least identified area of interest. Further, in such an embodiment, determining the coordinates of the at least one manufacturing resource in the at least identified area of interest includes reading the one or more image frames in gray scale, resizing the one or more image frames read in gray scale, applying a blur filter to the one or more resized image frames, applying at least one edge detection technique to the one or more filtered image frames, and determining the coordinates of the at least one manufacturing resource in the at least identified area of interest based at least in part on the application of the at least one edge detection technique to the one or more filtered image frames.

Additionally, in such an embodiment, determining the availability status information also includes extracting the at least one area of interest, in accordance with the determined coordinates, from the one or more image frames, as well as applying the one or more machine learning models to the at least one extracted area of interest.

Step704includes outputting the determined availability status information to at least one user device associated with the manufacturing environment. In at least one embodiment, outputting the determined availability status information includes displaying the determined availability status information on at least one mobile device associated with at least one user within the manufacturing environment.

The above-described illustrative embodiments provide significant advantages relative to conventional approaches. For example, some embodiments are configured to implement device management techniques using machine learning algorithms. These and other embodiments can facilitate increasing production capacity at manufacturing facilities.

Illustrative embodiments of processing platforms will now be described in greater detail with reference toFIGS. 8 and 9. Although described in the context of information processing system100, these platforms may also be used to implement at least portions of other information processing systems in other embodiments.

FIG. 8shows an example processing platform comprising cloud infrastructure800. The cloud infrastructure800comprises a combination of physical and virtual processing resources that are utilized to implement at least a portion of the information processing system100. The cloud infrastructure800comprises multiple virtual machines (VMs) and/or container sets802-1,802-2, . . .802-L implemented using virtualization infrastructure804. The virtualization infrastructure804runs on physical infrastructure805, and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system.

The cloud infrastructure800further comprises sets of applications810-1,810-2, . . .810-L running on respective ones of the VMs/container sets802-1,802-2, . . .802-L under the control of the virtualization infrastructure804. The VMs/container sets802comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs. In some implementations of theFIG. 8embodiment, the VMs/container sets802comprise respective VMs implemented using virtualization infrastructure804that comprises at least one hypervisor.

A hypervisor platform may be used to implement a hypervisor within the virtualization infrastructure804, wherein the hypervisor platform has an associated virtual infrastructure management system. The underlying physical machines comprise one or more distributed processing platforms that include one or more storage systems.

In other implementations of theFIG. 8embodiment, the VMs/container sets802comprise respective containers implemented using virtualization infrastructure804that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system.

The processing platform900in this embodiment comprises a portion of information processing system100and includes a plurality of processing devices, denoted902-1,902-2,902-3, . . .902-K, which communicate with one another over a network904.

The processing device902-1in the processing platform900comprises a processor910coupled to a memory912.

The memory912comprises random access memory (RAM), read-only memory (ROM) or other types of memory, in any combination. The memory912and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs.

Also included in the processing device902-1is network interface circuitry914, which is used to interface the processing device with the network904and other system components, and may comprise conventional transceivers.

The other processing devices902of the processing platform900are assumed to be configured in a manner similar to that shown for processing device902-1in the figure.

Again, the particular processing platform900shown in the figure is presented by way of example only, and information processing system100may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices.

For example, particular types of storage products that can be used in implementing a given storage system of a distributed processing system in an illustrative embodiment include all-flash and hybrid flash storage arrays, scale-out all-flash storage arrays, scale-out NAS clusters, or other types of storage arrays. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment.