©2021 TechSolve Inc. All rights reserved. Confidential business and proprietary information of TechSolve, may not be copied or distributed to anyone without the express written permission of TechSolve.

Exploring Opportunities and Challenges for PHM 

Technology Adoption by Small Manufacturers

A Hybrid Numeric Data & TLP-based Approach

Radu Pavel, Ph.D.

Vice President, Chief Technology Officer

1

2021 Model-Based Enterprise Summit / TLP COI
 Since 1982, TechSolve has been focused 

on manufacturing and its emerging 

technologies

 Privately owned, not-for-profit 501(c)(3)

 State and Federal Manufacturing 

Extension Partnership (MEP) Center

 Broad Network of Academia and Industry

 Fully instrumented Smart Manufacturing 

Innovation Lab

 Digital manufacturing

 Machine and process monitoring

 IIoT connectivity (MTConnect®)

 Advanced analytics

 Process modeling and 

simulation

 Smart machine technologies

 Predictive maintenance

 Automation, robotics

 Cybersecurity for IT & OT

 Additive manufacturing

Smart Manufacturing 

Expertise

 MTConnect® Adapters

 Assessments & Installation

 Data Acquisition Solutions

 Protocol Translation Adapters

 Data Visualization

 Machinability Evaluation & 

Testing

 Manufacturing Process 

Development

 Problem Diagnosis & Resolution

 Process 

Optimization

 Growth Strategy

Manufacturing Process Solutions IIoT Solutions Business Advisory

TechSolve's

Highlights

 Cybersecurity for IT & OT

 Plan of Action & 

Milestone Development

 Incident Response 

Planning

Cybersecurity
TechSolve Equipment Instrumentation & Data Acquisition

 Instrumented CNC machines

 3, 4, and 5 axes CNC machining 

centers

 Mill-turn machine

 CNC Lathe

 CNC Grinding machine

 Spindle test-bed

 Feed-axis test-bed

 Manual lathe, mill and drilling machines

 Collaborative Robots (UR3, UR10)

 3D printers (CI SAAM, CreatBot)

 Laser scanner

 Coordinate Measuring Machine (CMM)

 Digital & optical microscopes

 Computer-based Tool Setter

 Minimum quantity lubrication (MQL) systems

 Scientific ovens

 Hardness tester

 Multiple metrology gages

 Sensors:

 Force dynamometers for turning, milling, drilling and 

grinding (Kistler)

 Power sensors with IIoT connectivity

 Current amperage sensors

 Vibration sensors

 Acoustic emission sensors and data acquisition system

 Temperature sensors

 Flowmeters

 Pressure sensors

 Precision measurement microphone

 Custom built data acquisition and analysis systems 

(LabView & Matlab)

 Bar code readers

 Tool condition monitoring systems

 MTConnect adapters and related software and hardware

 MTConnect to OPC UA converter

 All machine-tools connected to IIoT
©2021 TechSolve, Inc. All rights reserved. Confidential business and proprietary information of TechSolve, may not be copied or distributed to anyone without the express written permission of TechSolve.

Assessment of Opportunities, 

Challenges and Performance 

Metrics for PHM Technology 

Adoption by Manufacturers

4
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Project Scope

Conduct assessments and collect information/data associated with

equipment and/or process health and performance at small and

medium-size manufacturers, to

1) Understand and inform on the opportunities, challenges and

performance metrics for PHM technology adoption

2) Explore the possibility of augmenting numeric data from sensors

and control systems with human input, by integrating documented

natural language reports with data collection technology
©2021 TechSolve, Inc. All rights reserved. Confidential business and proprietary information of TechSolve, may not be copied or distributed to anyone without the express written permission of TechSolve.

Opportunities, Challenges 

and Performance Metrics

6
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Assessment Survey

7

• What maintenance practices do you employ and in what percentage?

• Who is running the Maintenance in your factory ? 

• Do you keep records of maintenance activities (text based)?

• Why do you capture maintenance work order (MWO) data?

• Do you process maintenance log data to extract analytics ? 

• Do you monitor any manufacturing assets and what is the main goal ?

• What data do you use to determine your maintenance strategy?

• What could improve your day-to-day maintenance tasks?

• How would you want to improve your maintenance long term?
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Performance Metrics

• Uptime

• Downtime

• Overall equipment effectiveness

• Repair cost

• Maintenance costs

• Failure frequency
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Challenges

• The culture of some of the SMMs can create a barrier

• Insufficient awareness and education regarding the current digitalization 

trends and benefits of PHM technologies

• Limited guidelines/standards regarding the adoption and use of the PHM 

technologies 

• Amount of management and tending for some of the PHM technologies 

on the market

• Complexity and cost of PHM systems vs. perceived benefits

• Lack of use cases and examples

• No rules or regulations requiring or motivating the manufacturers to 

collect data
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Opportunities

• Green field for awareness creation, education and deployment of pilot 

solutions that could build momentum for accelerated adoption

• Genuine interest for guidelines and standards that would simplify the 

efforts in adopting more advanced practices and technologies supporting 

the maintenance area

• Openness to implementing advanced technologies that would align with 

current digitalization trends, providing the cost - benefits ratio justifies 

the investment
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Technical Language 

Processing Experiment

11
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Experiment

Scope –explore the capabilities and limitations of integrating and extracting

actionable information from numeric data collected from manufacturing

equipment, and the maintenance work orders (text-based maintenance logs)

associated with that equipment.

Goal – explore and validate the relationship between observers’ natural

language, quantified sensed values, and some ground truth knowledge

about the state of the degrading component (i.e. the cutting tool).

Approach – limited and controlled accelerated aging setup where human

observations were recorded at regular intervals alongside streaming sensor

data.
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Strategy

• Create a machining program that enables a long duration cut

• Modify the program such that some of the cuts exhibit chatter or higher

than normal vibration – to create controlled and ‘random’ process failures

• While the CNC program is running, collect data from sensors and control,

and have a technician, come to observe the test periodically (e.g. 15 min)

to take notes relative to the status of the cut, tool and machine
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Experimental Setup

4”

6”

Machine: Milltronics HMC35, instrumented with

sensors and data acquisition system

Cutting tools: carbide end mill with 4 flutes, 0.5”

diameter

Metalworking fluid: Water-based (Trimsol 206)

Workpiece: 4140 steel block of 6” x 4” x 4”

sample
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Human Recorded Process Characteristics

• Test No. and Workpiece No.

• Various Test and Observation

Times

• Test Status

• Layer No.

• Tool Gauge length (in)

• Type of observation

• Process condition

• Tool condition

• Tool flank wear - END (in)

• Tool flank wear - LATERAL (in)

• Tool wear - RAKE FACE (in)

• Surface finish - face (μ in)

• Surface finish -lateral (μ in)

• Did you hear chatter?

• Anything out of ordinary?

• Out of ordinary description

• General Notes/Comments
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Concluding Remarks

• This study has been revealing with regard to the current state of

adoption of PHM technologies by the small manufacturers

• Opportunities and challenges for PHM technology adoption have been

identified on a sample of small manufacturers

• The interactions with manufacturers were leveraged to create

awareness / educate about PHM technologies and their benefits

• A first exploratory step focused on integrating data coming directly from

the equipment and the human-generated text data has been made

• The findings are useful for future standards development activities

associated with PHM for Manufacturing
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Thank you !

17

Radu Pavel, Ph.D.

Vice President, Chief Technology Officer

TechSolve, Inc.

6705 Steger Drive, Cincinnati, OH 45237, U.S.A.

pavel@techsolve.org