A cost-effective process that yields solid welds for long-term performance, resistance spot welding (RSW) remains a top joining method for a variety of structural parts.

Historically, manufacturing processes have often involved substantial waste. From the early days of industrialization, companies have prioritized production speed and volume over efficient resource use.

The manufacturing sector is currently facing a number of challenges.

We’re all familiar with photos of Ford’s production lines in 1920. But would we recognize them today? As part of a broader trend referred to as “Industry 4.0,” systems in many factories have modernized considerably in recent years.

Imagine grasping a heavy object, like a pipe wrench, with one hand. You would likely grab the wrench using your entire fingers, not just your fingertips.

Over the past decade, one of the biggest advances in enterprise resource planning (ERP) has been the ability to communicate and integrate with machines and external software programs to lower costs and increase efficiency.

A single atom-thick sheet of carbon known as graphene has remarkable properties on its own. But things can get even more interesting when you stack up multiple sheets.

It’s no secret the automotive sector is racing to find ways of tapping the potential of generative artificial intelligence (GenAI) to design and build the next generation of vehicles.

Robots do monotonous workflows and less pleasant, repetitive tasks with brilliance. Combined with image processing, they become “seeing” and reliable supporters of humans.

Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation.