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Improve Machining Productivity through Dynamic Analysis and Machine Learning

Training Provider: SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY
Course Reference: TGS-2018502777
S$1,350
Original: S$4,500
Save S$3,150

About This Course

• Perform virtual machining simulation
• Validate NC code before real machining
• Reduce operation errors and enhance safety using virtual CNC training
• Identify the causes of a machining chatter
• Select a right cutting tool for minimization of machining vibration
• Analyze the dynamic characteristics of the toolings using modal tests
• Identify characteristics of workpiece material which affect machining chatter
• Analyze machining stability lobes to prevent a machining chatter
• Configure the procedure of how to use a machining dynamic toolkit for optimizing machining process
• Analyze machining units and generate stability lobes using a machining dynamic toolkit
• Develop dynamic database of machine tools and toolings for high productivity
• Demonstrate how to improve material removal rate using dynamic analysis and stability lobe
• Demonstrate the methodology to improve machining process with high productivity using a machining dynamic toolkit

What You'll Learn

Machining dynamic analysis and simulation is a key and advanced technology to high productivity on precision machined components, which are of great economic importance in almost all precision industries including component machining, machine tool, mold-die, aerospace, computer, defense, etc. Basically, the mechanical components can be manufactured using various machining processes. However, the surface roughness regulation has been tightened and the demand of the precision parts with complex shape has been increasing. In this course, the know-how, skills, and practical technology to achieve high productivity will be lectured and demonstrated in this course.

Entry Requirements

Diploma with experience or Degree

Course Details

Duration 48 hours
Language English
Training Commitment Part Time
Total Enrolled New course
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