In the era of digitalisation, manufacturing plants worldwide have progressively transformed and no longer operate in the old traditional way. Demanding customers are setting up a higher expectation of end products with higher accuracy and excellent surface ﬁnishing yet lower cost. This only can be done through smart machining and digitalisation with less human intervention.
Companies have to expand their business beyond Singapore; this means more travelling yet at the same time being able to manage and monitor their plant offsite. Thus, it is essential to transform themselves towards smart digitalisation, and high-value precision manufacturing technology couple with Industry 4.0.
Given this trend and to align with the government drive towards digitalisation and a smart nation, this course is specially tailored-made to equip you with the essential knowledge and skill set ready to serve your future employer effectively to lessen the machine downtime through Equipment Condition Monitoring; and to reduce operation costs with Overall Equipment Eﬀectiveness; also to manage Operation Excellence and stay competitive in producing superior quality parts with knowledge transfer touches on Data Mining for Correlation Analysis and Dimensional Measurements & Metrology.
* Trainees who are concurrently receiving COVID-19 Support Grant (CSG) or Self-Employed Person Income Relief Scheme (SIRS) payouts will receive a lower training allowance as they are already receiving income relief.
There are 10 modules for completion under the SGUS for Smart Machining and Digitalisation course.
Participants will be awarded with a certificate for each individual module, if they meet the following criteria:
This module introduces participants to the fundamentals of mechanical surface enhancement processes including rolling, peening, burnishing, mechanical ﬁnishing processes (eg. vibratory bowl ﬁnishing, drag ﬁnishing, abrasive ﬂow machining, magneto-rheological ﬁnishing and grinding), and laser surface texturing, marking, peening and annealing for functionalities. Participants will gain a solid understanding of the surface enhancement and ﬁnishing processes, and the issues of surface treatment eﬀect on the work substrates. Participants will also be guided through hands-on project-based sessions on real-world industrial topics using SIMTech equipment and instruments to further enhance their understanding of surface enhancement and ﬁnishing technologies for production implementation.
This module introduces the fundamental concept and theoretical knowledge of precision dimensional measurement and nanoscale measurement technologies for applications in the Precision Engineering industry, and as well as other related industrial sectors. It covers the fundamentals of measurement errors, error analysis and uncertainty, precision measurement systems for dimensional measurements (CMM) and surface roughness measurements, precision machine tool calibration by a laser interferometer, scanning near-field optical microscopy and atomic force microscopy for nanoscale dimensional measurements. Participants will be guided by SIMTech mentors through hands-on and practical sessions using the learned knowledge and skills to address practical measurement problems.
This module is designed to provide participants with practical and systematic training on machining dynamics analysis and simulation technologies being used to achieve high productivity with good surface quality, high geometrical accuracy, and high efficiency. It covers virtual machining simulations, CNC verification, virtual training labs, machining dynamics fundamentals, machining stability, dynamics analysis of machining units, dynamics characteristics of work material, generation of machining stability lobes, know-how to improve material removal rate, and how to improve machining productivity using machining dynamics toolkit. Actual cases and practises will be conducted at the participants’ workplaces to demonstrate the productivity improvement on actual productions.
This module is structured to equip the participants with the fundamentals and state-of-the-art on the latest developments in smart machining technology including tool condition monitoring, machining dynamics, adaptive machining, machine learning for machining, quality assurance & surface integrity, and remanufacturing for industrial applications. Participants will be guided by SIMTech mentors through hands-on and practise sessions using the learned knowledge and skills to address practical problems from their workplace. The knowledge gained during this course benefits both the participants who stand to upgrade their expertise and their companies with the opportunity to improve their productivity and competitiveness.
This module covers a set of methodologies of conducting problem-solving using the data mining approach, which includes data collection, pre-process data from multiple sources, cleaning of the data, and ﬁnally using data mining techniques to analyse the data and ﬁnd out hidden correlations among various parameters involved in processes. Through extensive hands-on and discussion of successful case studies, the course will equip participants with the ability to use data mining techniques to solve actual industrial problems.
Machine breakdowns and unplanned downtime affects equipment availability and interrupts the delivery of services. Monitoring equipment’s condition and alerting its impending failure can help to minimise disruptions and costly repairs. This course provides participants with training in implementing an equipment condition and alert system using Industrial Internet-of-Things (IIoT) devices for remote monitoring of machine conditions. This course is specifically developed for local industry needs and taught by industry practitioners in the field. Case studies are discussed to highlight the applications in industry.
This module provides participants with the fundamentals of applying Overall Equipment Effectiveness (OEE) to improve machine productivity. It covers the latest knowledge on OEE in Industry 4.0 including OEE concepts with model factory setup, OEE fundamentals and hands-on calculation, data collection for analysis and reporting, and productivity improvement techniques. This course is specifically developed for local industry needs and taught by industry practitioners in the field. Case studies are discussed to highlight the applications in industry.
This module is designed to provide extensive hands-on practise training of basic skills on machine operation and software programming, geometrical measurement and metrology methods, and process digitalisation technologies used to embark on Industry 4.0. The module consists of 40 sessions (120 hours in total) which will allow the participants to operate the processing equipment, measurement and analytical instruments in SIMTech labs to enhance their knowledge learned from the other technical modules.
Under the guidance of the experienced trainers, the participants are expected to do hands-on practise in these three categories:
Machine Operation and Software Programming - Participants will be trained on different types of machine operations including CNC machines, laser processing machines, ultra-precision machining (UPM) centre and abrasive processing machines; CAD/CAM programming and simulation, UPM tool path generation and correction, and machining dynamics analysis.
Geometrical Measurement and Metrology Methods - Hands-on experience with measurements of hardness, strength, surface roughness and profile, CMM, stylus profilometer and white-light interferometer.
Process Digitalisation Technologies - Participants will be guided to connect the sensors, dynamometer and IIoT-based devices with machines to monitor the processing, and collect data to analyse the overall equipment eﬀectiveness and correlations among the process parameters and quality output.
The project-based modules provide opportunities for the participants to embark on an industry project either in-house at SIMTech or work with a participating company.
For more information about the SkillsFuture Credit, please visit its webpage here.
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