FA Application Package iQ Monozukuri Tool Wear Diagnosis for Machine Tools Global Player e-F@ctory

e-F@ctory is a concept for a further step on "Monozukuri", which reduces the total cost for development, production, and maintenance, and continuously supports improvement activities of the customer by utilizing the FA technology and IT technology.

Information In the increasingly complex manufacturing sites, coordination between "Man" and "Machine" through the best use of information from the production site is a key concept.

Productivity and quality can be improved not only with the information obtained from the devices at the production site, but the improvement triggered by on-site notice and flexible human actions. Similarly, automatic adjustment of equipment based on Coordination between man the information recognized by human is indispensable for the and machine through the promotion of automation. use of information

We have realized the "Next-generation manufacturing" through the use of the "e-F@ctory" information proposed by Mitsubishi Electric, the effective and flexible manufacturing realized through the coordination between man and machine, and the optimization of the production site, and the entire supply chain and engineering chain. Machine Man

iQ Monozukuri 2-3 Many issues exist with the varying-type/varying-volume production of machine tools. Feedback expressing concern from the shop floor includes “I focus so much on quality, I do not know the appropriate the time of tool change,” “I can’t prevent quality defects caused by sudden tool abnormalities,” and “It’s difficult to collect data from a wide-variety of machine tools, and I don’t know how to analyze it.”

With this package, IoT data are collected and analyzed using Mitsubishi Electric’s independent technology to optimize tool operation control and support the easy detection of quality defects. Five Use Cases Solving Tool Wear Issues

Use Case 1 Page.6

Use Case 2 Page.7

Use Case 3 Page.8

Use Case 4 Page.9

Use Case 5 Page.9

Page.10

iQ Monozukuri 4-5 Use Case 1

Reduction in Annual Tool Costs and Labor Required for Tool Change Work By Reducing the Frequency of Tool Change

We change tools based on tool usage time/count, but I am unsure of the appropriate tool change timing in the case of varying-type/varying-volume production.

Have you ever experienced this?

Assume the same tool is used to machine Product A and Product B. When a high volume of Product A is being manufactured, there were no product defects when tool change was carried out as it had been until now. However, when the production percentage of new Product B was increased, product defects began occurring, therefore the operator had no choice but to revise the setting for the regular tool change timing for a shorter period referring to the count when defects occurred.

Predict optimal tool change timing even in the case of varying-type/varying-volume production. Reduce tool change count as well as tool cost and labor!

Simultaneous collection of IoT machining data and machining conditions from machine tools to visualize changes in trends with identical machining conditions.

● Trends with regular tool change ● Trends when tools are replaced at usage limit (wear)

In the past, TBM (tool usage count) was the basis for tool change, The optimal tool life in varying-type/varying-volume production was determined however it became apparent that tools were being changed prior to life and tool maintenance was improved by switching from TBM (tool usage count) expiration despite the potential to be used close to the maximum load to condition-based maintenance CBM (wear state). for tool change results. As a result, tool usage count increased by 170%!

1420 4600 1420 4600 Tool deterioration condition 1400 4540 1400 4540 Previous maximum Predicts the number of times Tool life time for each 1380 deterioration condition 480 1380 4480 for the relevant a tool can be used before machining condition 1360 machining 4420 1360 reaching the set threshold 4420 based on past data results. 1340 4360 1340 Estimates appropriate tool life. 4360 Difference in 1320 deterioration rise 4300 1320 4300 angle due to varying 1300 type/varying-volume 4240 1300 Conventional Post-introduction 4240 production 1280 4180 1280 4180

1260 4120 1260 4120 Tool change every 50 uses 1240 4060 1240 4060 Machining feature value (machining load) Machining feature value (machining load)

1220 4000 1220 4000 Machining order Machining count (tool usage count) Machining order Machining count (tool usage count)

● Annual tool cost by tool optimal tool change (example)

New tool price Re-grinding price Annual tool cost Conventional 1.29 mill yen (15,000 yen ×51 tools） 1.04 mill yen（6,000 yen ×102 tools） 2.33 mill yen/tool Cut by 950,000 yen 765,000 yen 612,000 yen Post-introduction （15,000 yen × 86 tools） （6，000 yen ×174 tools） 1.38 mill/tool Use Case 2

Reducing Loss Cost by Preventing Leakage of Products with Quality Defects Due to Machining Faults

Quality defects occur due to sudden tool abnormalities or manufacturing abnormalities in the upstream process.

Have you ever experienced this?

Sudden tool defects create a large volume of defective products up until quality inspection. A large volume of defective products is produced due to mold deformation in the upstream process (casting).

Immediately after machining, detects abnormalities by identifying things that “Differ to the norm!” based on change from machining feature value in normal times.

Capture changes with machining feature value during normal machining, to support extraction of diagnosis thresholds for judging abnormal machining.

● Detection of tool breakage (example) ● Detection of machining abnormalities due to mold

Detects sudden reductions in machining feature value caused deformation (example) by blade breakage. Detects sudden increases in machining feature value caused by machining faults.

Detects signs of machining abnormalities Machining feature value Machining feature value

Detects blade breakage

Machining order Machining order Blade breakage detection Machining abnormality detection

iQ Monozukuri 6-7 Use Case 3

Productivity Increase by 10% or Higher Due to Shorter Breakage Detection Time

I want to eliminate tool breakage detection (breakage detection) time, which is a waste of operating time.

Have you ever experienced this?

It takes breakage detection time for each machining, each affecting the cycle time.

By leveraging IoT machining data, cycle time was significantly improved!

Able to detect tool breakages using IoT machining data only, making sensor-less tool breakage possible.

● Average load/workload trend ● Occurring alarm

Detect tool breakage in real-time When a tool breakage is detected, an alarm message is outputted and the signal tower *1 illuminates! *1: If external abnormality input terminal has been prepared on the machine tools

11/29 11:55:56 11/29 11:55:56

Threshold

● Cycle time improvement with tool diagnosis (example)

With breakage detection Program Program Program Program Program Program Total machining time Conventional ・・・ 1 2 3 28 29 30 12 minutes

Sensor-free tool breakage diagnosis Reduced by Program Program Program Program Program Program 90 seconds Post-introduction ・・・ 1 2 3 3 3 3 Achievement of sensor-less tool breakage detection 10 min 30 sec Use Case Use Case 4 5

Centralized Management of Machining Data Contributing to Traceability

Centralized management Diagnosis Data of machining data results display collection/diagnosis Quality values are out of spec but the ・IoT machining data reason is unknown. ・Product serial number ・Machining program (model type) GOT2000 MELSEC iQ-R ・Machining start/finish times

Sending of collected data

Machining dimension ● Clarify abnormal machining by superimposing waveform data of identical machining! Machine tools inspection device (Identify cause of excessive cutting in Achieve better traceability based on upstream process (rough machining)) centrally-managed IoT machining ・・・ Normal data/Product serial number/machining ・・・ Abnormal program (model type), and machining start/finish timing. Also enables simultaneous collection of data Rough machining Finishing Inspection pertaining to machining, such as cutting oil temperature and cutting oil discharge pressure. Can identify that “abnormality exists on upstream process”

Capable of Connection with Various Machine Tools – Both New and Old

Data collection is difficult as the production line contains both new and old machine models by various machine tools GOT2000 MELSEC iQ-R manufacturers.

Machine tools

Protocol Protocol Protocol Protocol Protocol Protocol converter converter converter converter converter converter

Supporting the CNC communication protocol of various companies, it is possible to collect machining data for up to 10 machines, Mitsubishi CNC Company A’s CNC Company B’s CNC Company C’s CNC Mitsubishi CNC Company A’s CNC covering all process from rough machining (old) (old) (old) (old) (new) (new) to finishing. Protocol Protocol Protocol Protocol This enables standardized tool change converter converter converter converter operation across various machine tools!

Supports the CNC communication protocol of various companies!

Connects with up to 10 machines! For details on connectable CNC models, please contact your local branch or dealer.

iQ Monozukuri 8-9 Wide-array of Analysis Technology Leveraging Data to Achieve Reliable Tool Diagnosis

Automatic Detection of Machining Load

Real-time diagnosis of tools and machining quality in various Continuous collection of machine tools data ・・・ Unnecessary data machining types, such as rough machining and finishing. Data cleansing Data cleansing Data cleansing Data cleansing Various forms of analog and digital data are collected, and data Serial A Serial B Serial C Serial D machining machining machining machining relating to cutting load based on collection waveform is automatically extracted (data cleansing). Furthermore, 11：23 11：37 11：39 11：53 15：55 16：09 16：11 16：25 （Time） machining feature value is automatically calculated from the Machining load current extracted waveform, a statistical population (learning subject) Machining Faults Machining abnorm for diagnosis is generated, and extraction of the diagnosis alities can be threshold not relying on experience is supported. Extract only the current instantly detected machining data excluding by comparing High-accuracy diagnosis is achieved, as the adequacy of unnecessary data Serial D differences between Serial C Machining load current settings such as data collection conditions, can be confirmed Serial B various data! using statistical methods. Serial A

Optimization of Tool Change Timing

By making “models” from machining program number and tool ・・・ Identical tool usage time ・・・ number combinations, tool life can be diagnosed in relation to Tool change Wear differs various machining conditions incorporated in the model, such Slow tool wear depending on tool Fast tool wear as machined material, workpiece shape, spindle speed, cutting depth, and feed. As such, even if one type of tool is used for production under various machining conditions, tool life for individual models can be stipulated, and tool diagnosis in varying-type/varying-volume production is possible by predicting deterioration to suit the progress of tool wear. Machining feature value Machining feature value Machining feature value

Machining order Machining order Machining order

Diagnose optimal tool life to suit various tool wear!

Tool Load Pattern Learning

Based on machining feature value (equivalent to cutting load) ・・・ Identical tool usage time ・・・ trends at time of tool change based on the conventional tool Tool change counter, it is possible to determine tool life by individual models with accumulated patterns on cutting performance by tool and Tool wear deterioration. Through determining the tool life by life individual model, the number of times the tool can be used until it reaches life can be predicted from post-tool change deterioration trends, making it possible to use the tool right up Conventional Optimization of

Machining feature value usage time Machining feature value usage time until the end of its life in line with wear status (CBM). Machining order Machining order ［TBM（tool usage count）］ Tool is replaced based on the number of times it ［CBM（wear status）］ has been used, regardless of its condition, therefore Now possible to use tools until the height of the peaks varies → tool isn’t being the end of their life used until the end of its life on themachinetools,etc. output canbesentexternallyalso,enablingabnormalitydisplay which axishasthe greatest variance. possible toprovidedatawhichtheuser canassesstoconfirm operations, andlookingatdeviation value changetrends,itis deviation for feature value by a set number of machining variance infeaturevalue. As such,byfinding thestandard collected waveformwouldnotstabilize, andtherewouldbe system, rotationalcontrolwillbecome unstable,thereforethe If abnormalitiesexistonthemachinetoolsspindleorfeeddrive predictive diagnosisonmachinetools. statistically analyzinglong-termtrends,itispossibletoperform short-term trendsofthecollecteddatafeaturevalue,then By assessing tool and machining abnormalities based on Machining DataUtilization * user-friendliness. enhance to count remaining predicted on based “warning” Able to set abnormality output in the two stages of “caution” and machining untilitreachesitslife. perform can tool a times more many how show to count usage production, displays predicted remaining count as available performance andthestatusofvarying-type/varying-volume Based on post-tool changedifferences in tool cutting and outputalarms. equipment changes,machinedmaterials(different workpiece), including changesinupstreamprocesses(basetreatment), This makesitpossibletodetectthingsthatdiffer tothenorm, determining abnormalmachining. air-cutting, and supports extraction of diagnosis threshold for results fromidenticalmodelsfornormalmachiningand Statistically learns machining load/no-load based on past Detection ofMachiningQuality Abnormalities themachinetools 1 Ifexternalabnormalityinputterminalhasbeenpreparedon Present toollifewiththeavailableusagecount

If anabnormalityoccurs, * 1

Deviation after every 600 machining operations Feature value predicted remainingcount! defect andworkpiece Histogram forthe600 MachiningWorkpiece mounting feature value Spindle deteriorationtrend quality defect Learns “loadupper/lowerlimitexceeded”basedonpastresultsofidenticalmodels Display toollifewith increases Sudden load - Diagnoses short-termchangesofcollected machiningdata σ μ σ 600 σ th wrpeeHistogramforthe1200 workpiece Deviation graduallyincreases duetomechanical“play” Machining order Feature value Manage eachtool’s usagestatusinonelist! - σ μ σ 1200 σ Machining feature value th wrpee Histogramforthe1800 workpiece

Feature value iQ Monozukuri breakage decreases Sudden ・・・・・・tool load - Tool change Identical toolusagetime σ μ σ 1800 Machining order σ th （times） workpiece 10 - 11 "Visualization, Analyzation, Optimization" that accurately grasps the tool status and support efficient cycle time improvement.

Visualization Visualization of machining data waveform collection, machine condition/diagnosis results

Collect machining data of each machine tools in real-time for storage or comparison purposes. （compare differences between deteriorated tools and new tools）

Swiftly confirm machining status of machine tools for diagnosis on a machine status screen.

Analyzation Diagnosing tool status from changes in trend data

Automatically calculate feature value based on machining data and diagnose tool status from trend data. Predicted remaining count display Predicts tool life from wear status and notifies user of the Predicts predicted remaining count for each tool deterioration Detects blade breakage

Immediately detects tool abnormalities and prevents machining defects.

Automatically calculates each feature value using statistical analysis to enable display and diagnosis of trend data.

Optimization Supports tool change operation where tools are used for full life

Set tool change condition and confirm tool usage status for each machine. （notification of tool change timing using alarms）

Electronic conversion of diagnosis alarms and tool change history, as well as support of timely tool change operation. Improvement Improvement of cycle time and tool life through optimization of machining conditions

By comparing machining condition for the same tool between machining programs, optimizes machining conditions such as cutting speed, feed amount, cutting depth, etc. to support improvement of cycle time.

Optimal machining conditions can be confirmed by comparison of changes in time load is placed on tool.

Settings Various settings for various needs

Communication means with machine tools and data cleansing conditions can be set for each individual machine to enable accurate collection of machining condition data.

Set models (machining conditions combining tools and machining programs), and select feature value to be targeted by diagnosis.

Tool information can be set for each machine.

Threshold of feature value to be targeted by diagnosis can be set for each model.

iQ Monozukuri 12-13 System Configuration Ethernet Analog output cable CC-Link IE Field Network I/O module output cable RS-232C communication cable

To FTP server ① PLC CPU ② High speed data logger module Protocol Analog-digital Extension analog-digital I/O module ① ② ③ converter converter module converter module ③ CC-Link IE Field Network master/local module Collection Alarm interface external output RS-232C communication cable Analog output cable I/O module output cable

GOT

Control module ※ Drive units Remote I/O module HUB for CC-Link IE Machine （CNC） （Servo/Inverter） （Machine tool contact I/O） HUB Field Network tools

※If connecting to another company’s CNC, please contact a Mitsubishi Electric branch.

System Specifications

*1：Limitations exist depending on diagnosis conditions Contents of Package

Software

※Software used for startup.

Device

*1：Please contact a Mitsubishi Electric branch. *2：Necessary for the CNC machine individually. *3：As optional, when connecting with external I/O devices, please select unit type and quantity for I/O points to be used. *4：please select module type and quantity for I/O points to be used.

iQ Monozukuri 14-15 FA Application Package

＊１： Maximum no. of connections supported by license

License Key Registration Process

Refer to the “License Key Application Procedures” enclosed with the product.

Enter the “Product ID” enclosed with the product and the Serial Number of the PLC you will use.

This will be emailed to you in approximately one business day.

From the “License Key Registration” window in GOT, register license key.

The issued license key can only be used with the hardware belonging to the serial number entered at the time of application. (Please note it cannot be used with other hardware) Partner Products (protocol converter for CNC connection)

FANUC CNC compatible gateway for overseas Mitsubishi Electric CNC compatible gateway

iQ Monozukuri 16-17 Pre-introduction Flow

● Select the device, machining type, tool for diagnosis ● Determine system configuration, secure installation environment

● Install, wire up equipment ● Set device parameters (communication means, data collection conditions, etc.), register diagnosis model ● Conduct trial operation check for data collection preparation ● Data collection to determine diagnosis threshold (data collection including 5 to 10 tool exchanges: Approx. 1 month)

● Confirm adequacy of settings from collection data ● Calculate diagnosis threshold from trend data ● Set calculation threshold

● Commence operation (check machine condition) ● Tool wear diagnosis, tool breakage diagnosis ● Tool change in line with diagnosis results/alarms ● Revise thresholds ● Optimization of machining programs using a cycle time improvement support function Explanation of Terminology

Trademarks

e-F@ctory, iQ Monozukuri, MELSEC, MELSOFT, GOT, and CC-Link IE are trademarks and/or registered trademarks of Mitsubishi Electric Corporation in Japan and overseas. Ethernet is the registered trademark of Fuji Xerox Co., Ltd. The company names, system names, product names, etc. appearing in this document are generally trademarks and/or registered trademarks of individual companies. There are cases in this document where trademark symbols（™, ®）are not specified.

iQ Monozukuri 18-19 Country/RegionSales office Tel/Fax USA MITSUBISHI ELECTRIC AUTOMATION, INC. Tel : +1-847-478-2100 500 Corporate Woods Parkway, Vernon Hills, IL 60061, U.S.A. Fax : +1-847-478-2253 Mexico MITSUBISHI ELECTRIC AUTOMATION, INC. Mexico Branch Tel : +52-55-3067-7512 Boulevard Miguel de Cervantes Saavedra 301, Torre Norte Piso 5, Ampliacion Granada, Miguel Hidalgo, Ciudad de Mexico, Mexico, C.P.115200 Brazil MITSUBISHI ELECTRIC DO BRASIL COMERCIO E SERVICOS LTDA. Tel : +55-11-4689-3000 Avenida Adelino Cardana, 293, 21 andar, Bethaville, Barueri SP, Brasil Fax : +55-11-4689-3016 Germany MITSUBISHI ELECTRIC EUROPE B.V. German Branch Tel : +49-2102-486-0 Mitsubishi-Electric-Platz 1, 40882 Ratingen, Germany Fax : +49-2102-486-7780 UK MITSUBISHI ELECTRIC EUROPE B.V. UK Branch Tel : +44-1707-28-8780 Travellers Lane, UK-Hatfield, Hertfordshire, AL10 8XB, U.K. Fax : +44-1707-27-8695 Ireland MITSUBISHI ELECTRIC EUROPE B.V. Irish Branch Tel : +353-1-4198800 Westgate Business Park, Ballymount, Dublin 24, Ireland Fax : +353-1-4198890 Italy MITSUBISHI ELECTRIC EUROPE B.V. Italian Branch Tel : +39-039-60531 Centro Direzionale Colleoni - Palazzo Sirio, Viale Colleoni 7, 20864 Agrate Brianza (MB), Italy Fax : +39-039-6053-312 Spain MITSUBISHI ELECTRIC EUROPE, B.V. Spanish Branch Tel : +34-935-65-3131 Carretera de Rubi, 76-80-Apdo. 420, E-08190 Sant Cugat del Valles (Barcelona), Spain Fax : +34-935-89-1579 France MITSUBISHI ELECTRIC EUROPE B.V. French Branch Tel : +33-1-55-68-55-68 25, Boulevard des Bouvets, 92741 Nanterre Cedex, France Fax : +33-1-55-68-57-57 Czech Republic MITSUBISHI ELECTRIC EUROPE B.V. Czech Branch, Prague Office Tel : +420-255-719-200 Pekarska 621/7, 155 00 Praha 5, Czech Republic Poland MITSUBISHI ELECTRIC EUROPE B.V. Polish Branch Tel : +48-12-347-65-00 ul. Krakowska 48, 32-083 Balice, Poland Sweden MITSUBISHI ELECTRIC EUROPE B.V. (Scandinavia) Tel : +46-8-625-10-00 Hedvig Mollersgata 6, 223 55 Lund, Sweden Fax : +46-46-39-70-18 Russia MITSUBISHI ELECTRIC (RUSSIA) LLC St. Petersburg Branch Tel : +7-812-633-3497 Piskarevsky pr. 2, bld 2, lit “Sch”, BC “Benua”, office 720; 195027 St. Petersburg, Russia Fax : +7-812-633-3499 Turkey MITSUBISHI ELECTRIC TURKEY A.S Umraniye Branch Tel : +90-216-526-3990 Serifali Mahallesi Nutuk Sokak No:5, TR-34775 Umraniye/Istanbul, Turkey Fax : +90-216-526-3995 UAE MITSUBISHI ELECTRIC EUROPE B.V. Dubai Branch Tel : +971-4-3724716 Dubai Silicon Oasis, P.O.BOX 341241, Dubai, U.A.E. Fax : +971-4-3724721 South Africa ADROIT TECHNOLOGIES Tel : +27-11-658-8100 20 Waterford Office Park, 189 Witkoppen Road, Fourways, South Africa Fax : +27-11-658-8101 China MITSUBISHI ELECTRIC AUTOMATION (CHINA) LTD. Tel : +86-21-2322-3030 Mitsubishi Electric Automation Center, No.1386 Hongqiao Road, Shanghai, China Fax : +86-21-2322-3000 Taiwan SETSUYO ENTERPRISE CO., LTD. Tel : +886-2-2299-2499 6F, No.105, Wugong 3rd Road, Wugu District, New Taipei City 24889, Taiwan Fax : +886-2-2299-2509 Korea MITSUBISHI ELECTRIC AUTOMATION KOREA CO., LTD. Tel : +82-2-3660-9569 7F to 9F, Gangseo Hangang Xi-tower A, 401, Yangcheon-ro, Gangseo-Gu, Seoul 07528, Korea Fax : +82-2-3664-8372 Singapore MITSUBISHI ELECTRIC ASIA PTE. LTD. Tel : +65-6473-2308 307 Alexandra Road, Mitsubishi Electric Building, Singapore 159943 Fax : +65-6476-7439 Thailand MITSUBISHI ELECTRIC FACTORY AUTOMATION (THAILAND) CO., LTD. Tel : +66-2682-6522 12th Floor, SV.City Building, Office Tower 1, No. 896/19 and 20 Rama 3 Road, Fax : +66-2682-6020 Kwaeng Bangpongpang, Khet Yannawa, Bangkok 10120, Thailand Vietnam MITSUBISHI ELECTRIC VIETNAM COMPANY LIMITED Tel : +84-28-3910-5945 Unit 01-04, 10th Floor, Vincom Center, 72 Le Thanh Ton Street, District 1, Ho Chi Minh City, Vietnam Fax : +84-28-3910-5947 Indonesia PT. MITSUBISHI ELECTRIC INDONESIA Tel : +62-21-31926461 Gedung Jaya 8th Floor, JL. MH. Thamrin No.12, Jakarta Pusat 10340, Indonesia Fax : +62-21-31923942 India MITSUBISHI ELECTRIC INDIA PVT. LTD. Pune Branch Tel : +91-20-2710-2000 Emerald House, EL-3, J Block, M.I.D.C., Bhosari, Pune-411026, Maharashtra, India Fax : +91-20-2710-2100 Australia MITSUBISHI ELECTRIC AUSTRALIA PTY. LTD. Tel : +61-2-9684-7777 348 Victoria Road, P.O. Box 11, Rydalmere, N.S.W 2116, Australia Fax : +61-2-9684-7245

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New publication, effective Mar. 2020. L（NA）16071ENG-A 2003（IP） Specifications are subject to change without notice.