Posts on Oct 2017

Preventive Maintenance Feature of Injection Molding Machine Using AI (Deep Learning)

FANUC CORPORATION (hereinafter, FANUC) and Preferred Networks, Inc. (hereinafter, PFN) have jointly developed AI Backflow Monitor that performs preventive maintenance on FANUC’s electric injection molding machine ROBOSHOT α-SiA series. This is the latest example of our joint initiative to apply deep learning to machine tools.

AI Backflow Monitor uses deep learning to evaluate and predict the wear state of ROBOSHOTs consumable parts (non-return valve) to let operators know before a part starts to malfunction. A conventional method requires that operators visually check waveform data for shape changes that indicate backflow of resin to assess the wear and estimate the replacement timing of the valve. The new feature utilizes the deep learning techniques to effectively analyze the waveform and digitize the wear amount, enabling it to notify operators of the best timing to replace the valve.

Additionally, AI Backflow Monitor takes advantage of its Edge Heavy feature to process data mainly on ROBOSHOT-LINKi, not in the cloud.

AI Backflow Monitor will be provided as an optional feature that can improve the operating rate of ROBOSHOT through preventive maintenance. (FANUC plans to begin taking orders in January next year.)

ROBOSHOT with this new feature will be exhibited at International Plastic Fair 2017, which will be held in Makuhari Messe from Oct. 24-28.

FANUC and PFN will continue to work together and make steady progress, step by step, towards realizing innovative and advanced manufacturing fields using AI.

ROBOSHOT α-SiA series:

High-level system structure

AI (Machine Learning) Improves Wire-cut EDM Accuracy

FANUC CORPORATION (hereinafter, FANUC) in collaboration with Preferred Networks, Inc. (hereinafter, PFN) has developed an AI thermal displacement compensation function which will improve the machining accuracy of ROBOCUT α-CiB series, FANUC’s wire-cut electric discharge machine (see Note 1 below). ROBOCUT with this function will be the first product using AI since FANUC and PFN began collaborating.


FANUC and PFN formed an R&D alliance*1 in June 2015, followed by a capital alliance*2 in August of the same year to promote a joint development of AI functions for the manufacturing industry that can efficiently improve the performance and operation rates of FANUC products. The newly developed function utilizes machine-learning (ML) technology to predict and control the variable machining accuracy caused by ROBOCUT’s temperature fluctuations, with 30% more accurate compensation than existing method. The new function is applicable from small to large workpieces.

The AI thermal displacement compensation function will be provided as an optional function to ROBOCUT, and FANUC plans to start accepting orders in November of this year. FANUC will also display the ROBOCUT with this new function at Mechatronics Technology Japan, which will be held in Port Messe Nagoya on Oct. 18-21, 2017.

ROBOCUT α-CiB series

FANUC is also developing a similar function for the ROBODRILL series that utilizes ML and expect to release it in the near future.
FANUC and PFN will continue making gradual but steady progress towards realizing innovative manufacturing fields through AI.

“It is my pleasure to announce our first product based on the machine-learning technology since the tie-up with FANUC. Through this product, we can demonstrate using ML is effective in optimizing control parameters, which is one of the most important issues facing the manufacturing industry. PFN will continue to contribute to the intelligence of machine tools and robots by applying machine learning and deep learning techniques.”
Toru Nishikawa, Chief Executive Officer of PFN


*1 Announcement for R&D alliance with FANUC Corporation
*2 Announcement for capital tie-up between FANUC and PFN


Note 1. Wire-cut EDM is a precision and fine shape machining tool that uses discharge phenomenon between the ultrathin wire electrode and the metal workpiece (electric conductor).


Preferred Networks released open source deep learning framework Chainer v3 and NVIDIA GPU array calculation library CuPy v2

Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has released Chainer v3, a major update of the open source deep learning framework Chainer(R), as well as NVIDIA(R) GPU array calculation library CuPy™ v2.

We release a major upgrade of Chainer every three months that quickly incorporates the results of the latest deep learning research. The newly released Chainer v3 will run without the need to change most of your code.


Main features of Chainer v3 and CuPy v2 include:

1.  Automatic differentiation of second and higher order derivatives

Chainer now supports automatic differentiation of second order and higher derivatives in many functions. This will enable users to easily implement deep learning methods that require second order differentiation as per equations written in papers.


2. Improved CuPy memory allocation

In many neural nets, memory efficiency when using GPUs will improve significantly, and reallocation of memory will be reduced in some cases, increasing speed.


3. Sparse matrix support has been added to CuPy

Large-scale graph analysis and natural language processing, which have previously been highly costly to implement on GPUs, can now be implemented more easily thanks to sparse matrix calculation being available on the GPU.

◆ Chainer ReleaseNote:

Chainer v3 has taken in a number of development results from external contributors as its previous versions did. PFN will continue working with supporting companies and the OSS community to promote the development and popularization of Chainer.


◆ About the Chainer Open Source Deep Learning Framework

Chainer is a Python-based deep learning framework developed by PFN, which has unique features and powerful performance that enables users to easily and intuitively design complex neural networks, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.

Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field. (

*Chainer(R) and CuPyTM are the trademark or the registered trademark of Preferred Networks, Inc. in Japan and other countries.