This Automotive Company Revs Up Machine Learning To Turbocharge Engine Quality Posted on : Oct 28 - 2021

Automotive manufacturer Mahindra Heavy Engines Limited (MHEL) has been building powerful diesel engines for more than 70 years. But changing market demands forced this venerable company to face a 21st century dilemma.

MHEL needed an extended quality testing program for internal combustion engines in order to reduce cost while accelerating the product manufacturing lifecycle.  And there was no time to waste.

But because of the technical resources required, and the fact that the data was staged in multiple stand-alone servers, the process was slow and costs high.

So, the Mumbai-based company took a radical step: replace its outmoded physical methods with virtual testing – using artificial intelligence/machine learning (AI/ML) while incorporating data from a variety of sources.

How it’s done

To understand MHEL’s challenges and objectives, we need to take a brief glimpse at how things work there.

Currently, quality testing accounts for one percent of engine manufacturing costs. In the final phase of quality testing, the engines undergo what are known as “cold” and “hot/load” tests to identify defects and ensure quality.

What that means: in a cold test, the engine’s crankshaft is rotated with an electric motor, while software analyzes data from different sensors.

Tests take approximately 140 seconds for each engine.

Engines that fail then undergo hot/load testing – which requires the engine to be fired and take two to three minutes.

Then come the load tests, which can go as long as 12 minutes.

Many engines don’t need the hot/load tests. MHEL’s challenge was eliminating these unnecessary steps without compromising quality. View more