TIAMA: Reinforcing Glass Inspection with Data Analytics

TIAMA: Reinforcing Glass Inspection with Data Analytics

Even as glass packaging exudes an aura of premium packaging, the art of defect-free glass making isn’t always an easy task. Amidst scores of near-miss manufacturing defects, bird swing, wire edges, and blisters that are often small and go undetected are in fact capable of breaking a glass container in the filling line, marring the appearance or even causing an injury to name a few. This invariably tarnishes the reputation of the glass manufacturer. “Our state-of-the-art machine vision solutions go beyond the normal scope of inspection to ensure high-quality of glass and gather data across the manufacturing line in real-time to improve production process efficiency,” says Max Hodeau, CEO of TIAMA.

With over five decades of experience and a client roster comprising over 600 leading glass manufacturers such as Owens-Illinois and Ardagh Group among others, TIAMA’s machine vision solutions leave no stone unturned in detecting the flaws in glass manufacturing.

TIAMA’s best-in-class visual inspection infrastructure, TIAMA hot systems use infrared machines to monitor glass temperature, weight, and distribution to highlight the critical and cosmetic defects and dimensions of the forming container. After passing through the forming machine and the annealing lehr containers are inspected by TIAMA’s quality control systems located before the palletizer machine to detect and segregate the flawless containers from the defective ones. The TIAMA inspection equipment captures 5-megapixel images of the container at a maximum speed of 500 bottles per minute, along the production line to provide visual data to the inspection personnel.

Along with the visuals and measurements, TIAMA’s unique value proposition lies in its data analysis capability that provides detailed data analysis across the plant for customers to make an informed business decision.

TIAMA’s solution enables the machines to distinguish and measure the sizes of defects in order to segregate the good commercial products from the bad ones

“Our intelligent data analysis tool, TIAMA IQ enables glass manufacturers to gather data across the production line and displays insightful recommendations for the management on a single platform,” says Hodeau.

It is imperative to remember that fabrication of glass is an empirical process and today the performance of a glass plant is around 83 percent. “Our goal through data analysis is to understand the production process better by making it more statistical in order to reduce the gap between 83 and 100 percent,” says Hodeau.

Hodeau recollects a particular incident where a beer company had to recall containers costing millions of dollars due to wire edge defects as small as 0.1 millimeter or less. Even though most glass inspecting companies in the market possess the equipment to distinguish the big defects, the smaller ones oftentimes pass undetected. In fact, the visible wire defects were rejected outright but the small defects were seen as false rejects due to improper visuals. The TIAMA team resolved this predicament with their innovative inspection solution that enables the manufacturers to measure the sizes of defects in order to segregate the good commercial products from the bad ones.

Hodeau provides unique insights into the company’s team of experts that are constantly innovating new services, preventive methods, curative models, and training sessions for customers to implement TIAMA’s solutions seamlessly. TIAMA also maintains native engineers across various nations to help companies better leverage their solutions.

Forging a successful path ahead, TIAMA is on the process of developing a subscription-based model for its online data processing systems given the ever-changing customer trends.“We are absolutely sure that tomorrow customers will ask for a subscription-based system. We want to be prepared for that with data to help our customers make the right decision on time, meet the quality requirements, and improve productivity across the production line,” concludes Hodeau.