PROJECT LEADS: West Virginia University

PARTNERS: Clemson University, ITAMCO

PROBLEM STATEMENT: Grinding is the machining process with the highest specific energyconsumption (SEC). Grinding is omnipresent in industry and essential for precise processing of hard and brittle parts. The total grinding time for ITAMCO’s End Gear 170 (see right) accumulates to ~11 hours and energy is responsible for 33% of the total manufacturing cost of a gear 170.

PROJECT GOAL: The principal goal is to reduce the extremely high energy consumption of grinding processes for gear manufacturing by at least 15% through hybrid modeling of the grinding system holistically.

TECHNICAL APPROACH: To achieve this goal, we will develop novel hybrid modeling methods that combine multi-physics​ equation-based models with data-driven machine learning models. The hybrid model input includes​ both machine tool parameters and sensor data as well as data from ERP and tool management​ systems. The hybrid model’s output provides grinding process parameters (wheel speed, depth of cut,​ infeed duration) as well as grinding tool reconditioning schedule and parameters (dressing and​ sharpening) that reduce the overall grinding system’s SEC. The model will be implemented in a Smart​ Manufacturing App on the CESMII SM platform. The industrial testbed will be located on-premise at​ ITAMCO, a leading US gear manufacturer and SME.

KEY TASKS AND MILESTONES:

T1.1: Machine Connectivity & Data Flow
T1.2: Data Model & Data Management
T1.3: Data Validity & Data Suitability

T2.1: Physics-based Griding Model Component Development
T2.2: Data-driven Grinding Model Component Development
T2.3: Hybrid Model Composition
T2.4: Validation of Hybrid Model

T3.1: Hybrid Model SM App Development
T3.2: Hybrid Model SM App Deployment on CESMII Platform
T3.3: Test Run during live production
T3.4: Evaluation of Energy Efficiency

T4.1: Project Management
T4.2: Education & Outreach

POTENTIAL IMPACT: The US demand for gears is expected to grow by 6.4% to $40 billion in sales. Grinding will remain the core technology to produce large-scale, high-quality gear components. The novel, scalable, and generalizable hybrid modeling approach and its deployment in the CESMII SM platform environment will provide a blueprint for other use cases to reduce the energy consumption of the US grinding industry.

BENEFITS:

  • The project showcases rapid recovery of Smart Manufacturing adoption cost through energy savings and productivity increases in an industry with energy intensive processes. The project creates an opportunity to scale its impact for other interested CESMII members across industries (automotive, aerospace, medical, etc.) and applications (milling, turning, etc.) within the larger CESMII network.
Member % Cost Share CESMII % Cost Share Duration
50.08% 49.92% 18 Months

 

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