Manufacturing Engineering Series Part 8: Manufacturing Automation & Robotics

Industrial Robotics

Industrial robots are programmable, multi-axis manipulators used for welding, painting, assembly, material handling, and machining. Over 4 million industrial robots are deployed globally (IFR 2024), with automotive, electronics, and metal fabrication as the largest users.

Trajectory Planning & Control

Trajectory planning generates the path a robot follows between waypoints — specifying position, velocity, and acceleration at every time step. The two fundamental types:

import numpy as np

# Robot Forward Kinematics - 2-Link Planar Arm
# Demonstrates FK calculation from joint angles to end-effector position

# Link parameters
L1 = 0.5    # meters (link 1 length — upper arm)
L2 = 0.4    # meters (link 2 length — forearm)

# Joint angles in degrees
theta1_deg = 45    # shoulder joint
theta2_deg = 30    # elbow joint (relative to link 1)

# Convert to radians
theta1 = np.radians(theta1_deg)
theta2 = np.radians(theta2_deg)

# Forward Kinematics
# Elbow position
x_elbow = L1 * np.cos(theta1)
y_elbow = L1 * np.sin(theta1)

# End-effector position
x_ee = L1 * np.cos(theta1) + L2 * np.cos(theta1 + theta2)
y_ee = L1 * np.sin(theta1) + L2 * np.sin(theta1 + theta2)

print("2-Link Planar Robot — Forward Kinematics")
print("=" * 50)
print(f"Link lengths: L1 = {L1}m, L2 = {L2}m")
print(f"Joint angles: θ1 = {theta1_deg}°, θ2 = {theta2_deg}°")
print(f"\nElbow position:       ({x_elbow:.4f}, {y_elbow:.4f}) m")
print(f"End-effector position: ({x_ee:.4f}, {y_ee:.4f}) m")

# Inverse Kinematics — given target, find joint angles
target_x, target_y = 0.6, 0.3

# Using geometric approach
D = (target_x**2 + target_y**2 - L1**2 - L2**2) / (2 * L1 * L2)
if abs(D) <= 1:
    theta2_ik = np.arctan2(np.sqrt(1 - D**2), D)  # elbow-up solution
    theta1_ik = np.arctan2(target_y, target_x) - np.arctan2(
        L2 * np.sin(theta2_ik), L1 + L2 * np.cos(theta2_ik))
    
    print(f"\nInverse Kinematics for target ({target_x}, {target_y}):")
    print(f"  θ1 = {np.degrees(theta1_ik):.2f}°")
    print(f"  θ2 = {np.degrees(theta2_ik):.2f}°")
    
    # Verify
    x_verify = L1*np.cos(theta1_ik) + L2*np.cos(theta1_ik + theta2_ik)
    y_verify = L1*np.sin(theta1_ik) + L2*np.sin(theta1_ik + theta2_ik)
    print(f"  Verification: ({x_verify:.4f}, {y_verify:.4f}) m ✓")
else:
    print(f"\nTarget ({target_x}, {target_y}) is UNREACHABLE!")
    print(f"Max reach: {L1+L2:.2f}m, Distance: {np.sqrt(target_x**2+target_y**2):.2f}m")

End-Effector & Gripper Design

The end-effector (EOAT — End of Arm Tooling) is where the robot interacts with the workpiece. Choosing the right gripper can make or break a robotic cell:

PLC, SCADA & Control Systems

A Programmable Logic Controller (PLC) is the industrial workhorse — a ruggedized computer that executes control programs in real-time, scanning inputs (sensors, switches), executing logic, and updating outputs (motors, valves, lights) every 1-10 milliseconds. PLCs replaced hardwired relay panels in the 1970s and now control everything from individual machines to entire production lines.

SCADA & HMI Systems

SCADA (Supervisory Control and Data Acquisition) provides centralized monitoring and control of entire factory systems — collecting data from hundreds of PLCs, displaying real-time dashboards on HMI (Human Machine Interface) screens, triggering alarms, logging historical data, and enabling remote control.

Sensors, Actuators & Servo Control

Industrial automation relies on sensors to perceive the physical world and actuators to act on it. The control loop (sensor → controller → actuator) runs continuously:

Cobots & Vision Systems

Collaborative robots (cobots) are designed to work safely alongside humans without safety fences. The market grew from $100M in 2015 to $2B+ in 2024 (Universal Robots, FANUC CRX, ABB GoFa lead). Cobots address a critical gap: tasks too complex for full automation but too ergonomically harmful or repetitive for humans.

Machine Vision & Inspection

Machine vision gives robots "eyes" — cameras and algorithms that enable part identification, quality inspection, robot guidance, and measurement. Modern vision systems use deep learning (CNNs) to achieve human-level defect detection at 100× the speed.

Safety Standards (ISO 10218/15066)

Robot safety is governed by a hierarchy of standards. Every robotic cell must have a documented risk assessment before commissioning — identifying all hazards (pinch points, crush zones, ejected parts, electrical, pneumatic) and implementing adequate safeguards:

AI-Driven & Autonomous Robotics

ROS (Robot Operating System) is an open-source middleware framework that provides tools, libraries, and conventions for building robot software. ROS2 (the production-grade version) uses DDS (Data Distribution Service) for real-time publish-subscribe communication between nodes — each node handles a specific function (vision, planning, control, localization).

AI & ML in Manufacturing Robotics

AI transforms robots from programmed machines (repeating fixed motions) to adaptive systems that learn, perceive, and decide:

Autonomous Robotic Swarms

Robotic swarms coordinate hundreds of simple robots to accomplish tasks that no single robot can handle. Inspired by ant colonies and bee swarms, manufacturing swarm systems enable reconfigurable production — robots dynamically form assembly stations, transport routes, and inspection formations based on production demand.