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Improve Machining Productivity through Dynamic Analysis and Machine Learning (55 HOURS)

Dynamic Analysis and Simulation_Banner

Milling and Turning dynamics analysis and simulation are critical for achieving high productivity from precision machined components, which are of great economic importance to any precision industry including component machining, machine tool, mould-die, aerospace, computer and defence. This course provides participants with practical and systematic training on machining dynamics analysis and simulation technologies that can be used to achieve high productivity with good surface quality, as well as high geometrical accuracy and efficiency.

Why This Course

On completion of this unit, participants will have the knowledge and application skills required to improve machining productivity through dynamics analysis and simulation.

These skills include:

  • Scientific transformation in machining industry
  • Turning vibration tests and process optimisation: Analysis of insert, tool holder, machine configuration
  • Control of machining parameters against turning vibration Identifying the causes of a machining chatter
  • Selecting a right cutting tool for minimisation of machining vibration
  • Analysing the dynamic characteristics of toolings using modal tests Identifying characteristics of workpiece material that affect machining chatter
  • Analysing machining stability lobes to prevent machining chatter
  • Configuring the procedure for using a machining dynamic toolkit for optimising a machining process
  • Analysing machining units and generating stability lobes using a machining dynamics toolkit
  • Developing dynamics databases of machine tools and toolings for high productivity
  • Demonstrating how to improve material removal rate using dynamics analysis and stability lobe
  • Demonstrating the methodology for improving machining processes with high productivity using a machining dynamics toolkit
Quick Machining -01

Who Should Attend

The roles that this unit would be relevant to include, but are not limited to:
  • Operations managers/ manufacturing managers/engineers
  • Production planning engineers/materials engineers/mechanical engineers
  • Production engineers, foremen, and skilled operators
  • Quality control managers/engineers, materials purchasing engineers
  • Laboratory managers and engineers
  • University students specialising in materials science/mechanical engineering
  • Companies that do general machining jobs
  • Companies in the manufacturing industry that have metal machining as an in-house process, such as companies in the precision engineering, aerospace, automotive and electronics sectors



What You Will Learn

What you will learn

This course module aims at providing participants with practical and systematic training in machining dynamics analysis and simulation technologies that can be used to achieve high productivity with good surface quality, high geometrical accuracy, and high efficiency. The program scope covers virtual machining simulations, CNC verification, virtual training labs, fundamentals of machining dynamics, influences of tooling’s geometry on machining stability, dynamics analysis of machining units, dynamics characteristics of work material, generation of machining stability lobes, operation of machining dynamics toolkit, know-how to improve material removal rate, and how to improve machining productivity using dynamics toolkit. Real case studies and demonstrations will be performed at each participant’s shopfloor, showcasing productivity improvements on actual products in various machine tool structures.




download brochure OR register of interest for course

Improve Machining Productivity through Dynamic Analysis and Simulation

Please register your interest for this course under 1. Modular Programmes > 2. Improve Machining Productivity through Dynamics Analysis and Machine Learning in the online form.

Note: SIMTech will contact you once registration for the next run of the course you are interested in opens.

Upon Completion of This Course

Upon Completion of this Course

Electronic Statement of Attainment (SOA) certificates will be issued by SkillsFuture Singapore (SSG) to participants who have attended and attained competency in the Singapore Skills Framework training modules. 

Participants must also meet the following criteria to receive their SOAs: 

  • Achieve at least 75% course attendance;
  • Take all assessments; and
  • Pass the course.

Click here to learn more about the WSQ SOA Certificate.




Statement of Attainment:SSG SOA

Pre-Requisites, Full and Nett Course Fees

Pre-Requisites

  • Applicants should possess a degree in any discipline or a diploma with a minimum of 3 years of related working experience.
  • Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.
  • Proficiency in written and spoken English.

Full Course Fee

The full course fee for this module is $4,500 before funding and prevailing GST.

Nett Course Fee

International
Participants
Singapore Citizens
(39 yrs old or younger), SPRs or LTVP+ Holders
Singapore Citizens
(40 yrs or older)²
Enhanced Training Support
for SMEs¹
$4,815.00 $1,444.50 $544.50 $544.50
All fees are inclusive of prevailing GST.

Please note that fees and funding amount are subject to change.

  • Long Term Visit Pass Plus (LTVP+) Holders
    The LTVP+ scheme applies to lawful foreign spouses of Singapore Citizens with (i) at least one Singapore Citizen child or are expecting one from the marriage, or at least three years of marriage, and (ii) where the Singapore Citizen sponsor is able to support the family.

    All LTVP+ holders can be identified with their green visit pass cards, with the word ‘PLUS’ printed on the back of the card.

  • Singaporeans aged 25 years old and above are eligible for SkillsFuture Credit which can be used to offset course fees (for self-sponsored registrations only).

    For more information on the SkillsFuture funding schemes you are eligible for, please visit www.ssg.gov.sg




    How course fees are calculated

    fees breakdown

    Eligible for SkillsFuture Enterprise Credit (SFEC)

    SkillsFuture Enterprise Credit (SFEC)

    The SkillsFuture Enterprise Credit (SFEC) was first introduced during the Singapore’s Budget 2020 and then recently expanded to a larger group of employers in Budget 2022. It encourages employers to invest in enterprise transformation and capabilities of their employees. Eligible employers will receive a one-off S$10,000 credit to cover up to 90% of out-of-pocket expenses on qualifying costs for supportable initiatives, over and above the support levels of existing schemes.

    Submission of SFEC Claim

    Employers are required to submit their SFEC claims manually via its microsite if they fulfil any of the following scenarios:

    • Employers have sent their foreign employees (excluding foreign employees who hold Long-Term Visit Pass Plus) for training on a SFEC-eligible course;

    Employers can only submit the claims for SFEC after their employees have completed the training course(s). Click here to download the SFEC Claim e-guide.
    NOTE: You will require a Corppass account with "EPJS_User" role assigned prior to login to your account to manage your claims.

    If you are unsure whether your company is eligible for SFEC, you may submit an enquiry to Enterprise Singapore (ESG) via here for their response.




    Contact Us




    download brochure OR register of interest for course

    Improve Machining Productivity through Dynamic Analysis and Simulation

    Please register your interest for this course under 1. Modular Programmes > 2. Improve Machining Productivity through Dynamics Analysis and Machine Learning in the online form.

    Note: SIMTech will contact you once registration for the next run of the course you are interested in opens.



    Schedule

    Module
    Skills Course Reference Number Training Period
    (Click on dates to view schedule)
    Registration Status
    Improve Machining Productivity through Dynamics Analysis and Machine Learning (55 hours)

    Note: The training venue for this module is held at NTU Valley Block.
    TGS-2018502777

    Sessions
    FD
    : Full Day  AM
    : Morning PM
    : Afternoon EVE
    : Evening