The Journey to Industry 4.0 Starts with Small Steps

Written by Jon Reneberg, Lead Digital Manufacturing Specialist

Industry 4.0 is a widely discussed topic in manufacturing, generating excitement and curiosity in equal measure. But what exactly is it? Let's break it down and make it clear.

• First Industrial Revolution – Mechanization with steam and waterpower. This was a very mechanical way to create repeatable processes.
• Second Industrial Revolution – Electricity and mass production with assembly lines. Large banks of relays were often used to carry out logical decisions toward the later years.
• Third Industrial Revolution – Use of computers to automate machines and production processes. Yes, this includes PLCs.
• Fourth Industrial Revolution (or Industry 4.0) – Smart factories, connected equipment, and data-driven automated adjustments.

While headlines might paint a picture of robots replacing everyone, AI in manufacturing is actually about creating powerful tools. Machine learning, a type of AI, is a great example. Imagine a computer program that learns from human input to identify what's good and bad in a process. This is called supervised learning. It excels at tasks like monitoring data for potential issues and even automatically adjusting the process to prevent undesired events altogether.

Building the Foundation: Data Collection for Machine Learning

Machine learning thrives on data. To get started, you'll need a data collection system. This typically involves:

• Sensors: These capture data points relevant to your machine's performance, like vibration, temperature, or production rate.
• Data acquisition system (DAQ): This device acts as the "listener" that gathers sensor data and converts it into a usable format.
• Data storage: Choose a reliable platform to store your collected data for easy access and analysis.

What Data to Collect?

The key is to identify what adjustments you want to make on the machine and then measure everything that might influence those adjustments. This includes:

• Machine parameters: Motor speed, pressure levels, etc.
• Environmental factors: Temperature, humidity, vibration.
• Process variables: Material type, feed rate, etc.

Customizing for Success

While some generic data collection solutions exist, you might need custom setups for specific equipment to capture the most relevant data points.

Don't feel overwhelmed! Here's how to get started:

• Start with small wins: Look for repetitive tasks that can be automated. Consider part counting, bad product redirection, or low product infeed alerts using stack lights. These simple automations can yield quick results.
• Leverage pre-built solutions: Affordable vision systems can identify defects, and the best trainers are often your own experienced employees. Collaborative robots (cobots) are becoming cost-effective and can operate safely alongside humans.
• Invest in your people: Equipping your employees with relevant skills is the most valuable long-term strategy. PLC programming, network infrastructure, cybersecurity, and basic robotics knowledge are all valuable for automation implementation. Consider online or in-person courses to help them "skill up."

Every manufacturing journey is unique. We're here to help you navigate yours! Reach out to Purdue MEP at mepsupport@purdue.edu to discuss your specific needs and explore the best next steps for implementing automation in your facility.

Writer: Jon Reneberg, 317-275-6810, jreneber@purdue.edu