FA: AI Servo Monitor (Level 4: Deep Learning)

A mechanical breakdown caused by sudden malfunction of the spindle axes or feed　axes of a machine tool could lead to major problems such as a long-term suspension of a machining line. In order to prevent this, it is necessary to detect signs of anomaly in the spindle axes or feed axes before a malfunction occurs.

FANUC and PFN have developed a new AI function called AI Servo Monitor which collects control data of feed axes and spindle axes of machines with high-speed sampling. It applies deep learning to the collected data and shows the anomaly score based on the current state of the machine components.

AI Servo Monitor trains a model using torque data from motors as input while the machine is operating normally. The trained model has extracted features of the torque data and can represent its normal state. During the machine’s actual operation, AI Servo Monitor takes the torque data as input and compares it with the normal state to calculate and display the anomaly score. By monitoring this, machine operators can observe a symptom indicative of malfunction in the feed axes or spindle axes as they work with the machine tool.

AI Servo Monitor notifies operators before failure relating the feed axes or spindle axes, allowing for maintenance to be performed. This will contribute to improved availability of machines.

Scheduled date to start shipping: July in 2019 *
*Updated on Sept. 19, 2019: We started to provide AI Servo Monitor in August 2019 as a proof of concept.

Robot: AI Error Proofing (Level3: Machine Learning)

FANUC and Preferred Networks, Inc. (PFN) has introduced a new AI Error Proofing function designed for part inspection using machine learning technology.

FANUC robots equipped with the new function can check and determine whether a part is good or bad, based on example images of good and bad parts. AI Error Proofing does not require an external PC because it is implemented directly on the FANUC robot controller as part of FANUC’s integrated vision system – iRVision. The new function is ideal for various inspection processes in manufacturing, such as checking presence of assembled parts or weld nuts, and part orientation verification.

For conventional machine vision to check whether a part is present or not, it would make decisions based on being able to or not being able to detect a pre-taught part’s shape and position. However, this method is often affected by spatter or soot on parts, halation of images due to reflection of metal surfaces, which could lead to false results and require skilled expertise to optimize vision settings.

FANUC’s AI Error Proofing does not try to detect a part’s shape or position, but utilizes machine learning to determine whether the image itself is good or not, enabling a much more robust inspection against fluctuations of the environment or halation. It also allows for higher accuracy inspections by providing several to a few dozen image data sets and teaching it which should pass and which should fail – all without having to do detailed vision parameter tuning.

FANUC’s AI Error Proofing will be available in August 2019.

投稿 FANUC’s new AI functions that utilize machine learning and deep learning は Preferred Networks, Inc. に最初に表示されました。

FA：AI Servo Tuning (Machine Learning)

FANUC has developed AI Feed Forward as the first to come out of its development efforts in a group of AI Servo Tuning functions that realize high-speed, high-precision, high-quality machining. It utilizes machine learning to easily tune parameters for controlling servo motors in a sophisticated manner. AI Feed Forward is a feed-forward controller based on a high-dimensional model that represents mechanical characteristics more accurately. This model has too many parameters to tune manually as has been done up to now. Therefore, machine learning is used in the process to determine parameters for this advanced feed-forward control. AI Feed Forward offers high-quality machining as it reduces mechanical vibration caused when servo motors accelerate or decelerate.

Shipment estimated to start in April 2018

ROBOT：AI Bin Picking (Deep Learning/FIELD System Application)

 FANUC released AI Bin Picking FIELD application with 3D object scoring function to identify suitable picking order with higher success rate. This Deep Learning based application enables FANUC Robot Bin Picking system to learn the picking order automatically, and reduces robot user’s burden of the lengthy manual setup process. Also, this function makes FANUC Robot to pick up the object with higher success rate, which had only been possible with detail parameter tuning by experienced operators. Picking success rate can be even improved by creating Deep Learning trained model for each workpiece type. 

Left: FIELD BASE Pro (with NVIDIA GPU)

Right: Picking robot system with sensor (Demo unit）

Shipment started in April 2018

ROBOMACHINE：AI Thermal Displacement Compensation (Machine Learning)

FANUC has developed and begun selling an AI thermal displacement compensation function for FANUC’s ROBODRILL series, following the release of the same AI function for its wire-cut electric discharge machine ROBOCUT in November last year. The second AI function is for ROBOMACHINE and utilizes machine-learning technology to predict and compensate for the thermal displacement caused by temperature fluctuations, which are detected by the thermal sensors measuring ambient temperatures as well as ROBODRILL’s temperature rise while in motion. Machining accuracy has improved by about 40%, compared with an existing function. Furthermore, the optimal placement of the thermal sensors and the effective use of thermal data enable it to continue to perform optimal compensation without interrupting machining work even if there is sensor malfunction.

ROBOCUT with the first AI thermal displacement compensation function (released on November 2017)

ROBODRILL with the second AI thermal displacement compensation function

Shipment started in March 2018 (already released)

Comment from Toru Nishikawa,
President & CEO of Preferred Networks

“We have been working with FANUC on the AI Bin Picking project since the commencement of our R&D alliance in 2015. I feel it is of great significance that we announce its release today as the first product to apply deep learning to robots. We will continue to bring a new value to manufacturing floors by stepping up our efforts to introduce to the market smart robots and machine tools that utilize deep learning in a broader field.”

投稿 FANUC’s new AI functions utilizing machine learning and deep learning は Preferred Networks, Inc. に最初に表示されました。

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: http://www.fanuc.co.jp/en/product/roboshot/index.html

High-level system structure

投稿 Preventive Maintenance Feature of Injection Molding Machine Using AI (Deep Learning) は Preferred Networks, Inc. に最初に表示されました。

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
https://www.preferred.jp/en/news/8731
＊2 Announcement for capital tie-up between FANUC and PFN
http://www.fanuc.co.jp/en/profile/pr/newsrelease/notice20150821.html

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).

投稿 AI (Machine Learning) Improves Wire-cut EDM Accuracy は Preferred Networks, Inc. に最初に表示されました。