Capturing ROI through Industry 4.0 Initiatives
Visual Inspection
Why Tesla Needed The Giga Press
Acoustic Monitoring
Assembly Line
Sparks fly as BAE Systems brings innovation to welding
Funded by the U.S. Government, BAE Systems engineers collaborated with the U.S. Army Research Laboratory and Wolf Robotics to develop an Agile Manufacturing Robotic Welding Cell customized for aluminum structures that comprise the combat vehicle’s hull.
Prior to welding automation, large aluminum pieces that form the hull were hand-welded together, requiring numerous weld passes at each seam to build the hull. Hand welding requires the welder to hold the weld gun with both hands, pull the trigger to feed wire into the weld joint that creates an arc. The gun is then moved over the metal slowly to create a weld. The number of weld starts and stops in a single seam is based on the length and reach of the welder’s arms. The further a welder can reach, the less he or she needs to stop and start again.
Cable-path optimization method for industrial robot arms
The production line engineer’s task of designing the external path for cables feeding electricity, air, and other resources to robot arms is a labor-intensive one. As the motions of robot arms are complex, the manual task of designing their cable path is a time-consuming and continuous trial-and-error process. Herein, we propose an automatic optimization method for planning the cable paths for industrial robot arms. The proposed method applies current physics simulation techniques for reducing the person–hours involved in cable path design. Our method yields an optimal parameter vector (PV) that specifies the cable length and cable-guide configuration via filtering the candidate PV set through a cable-geometry simulation based on the mass–spring model.
How to calculate digital transformation ROI
To simplify and prioritize the digital vision, first consider how digital transformation for manufacturing integrates three key business components:
- Supervisory control and data acquisition (SCADA), programmable logic controller (PLC) and control (machine automation)
- Manufacturing execution system (MES), which includes: (part traceability, machine monitoring and machine management, i.e., recipes and so on)
- Enterprise resource planning (ERP), which includes: (AP/AR, raw materials, purchase orders, inventory, scheduling and tracking).
Achieving large profitability and competitive gains requires seamless integration of three business components. However, it is important to begin at the machine automation level, then incorporate the MES and finally the ERP. The reason for following this path is based on data requirements but also because it is the easiest path for development.
Fabs Drive Deeper Into Machine Learning
For the past couple decades, semiconductor manufacturers have relied on computer vision, which is one of the earliest applications of machine learning in semiconductor manufacturing. Referred to as Automated Optical Inspection (AOI), these systems use signal processing algorithms to identify macro and micro physical deformations.
Defect detection provides a feedback loop for fab processing steps. Wafer test results produce bin maps (good or bad die), which also can be analyzed as images. Their data granularity is significantly larger than the pixelated data from an optical inspection tool. Yet test results from wafer maps can match the splatters generated during lithography and scratches produced from handling that AOI systems can miss. Thus, wafer test maps give useful feedback to the fab.
Robotic Inspection for Aboveground Storage Tanks
Aboveground Storage Tanks (AST) are vital assets for many industries including, power, paper and pulp, oil and gas, chemical, and even beverage production. Routine inspection of external and internal tank components is beneficial for understanding its condition and is required by federal and local laws and regulations. Robot-enabled ultrasonic testing (UT) offers a unique solution to AST inspections because they save plant operators valuable resources while providing more asset coverage and actionable data.
Pfizer’s Edge in the COVID-19 Vaccine Race: Data Science
Pfizer dominated news headlines and family dinner conversations last December when it became the first company to bring a COVID-19 vaccine to the U.S. market. The pharma giant accomplished the feat in record time: less than a year after the disease was first identified.
Integral to that effort was the work of Pfizer’s informatics and digital technology team for its vaccine R&D business. Led by Frank DePierro, this group of researchers crunched and chronicled all of the clinical trial data that led to a green light from the U.S. Food and Drug Administration (FDA), and a safeguard for millions of people.
Are my (bio)pharmaceutical assay performances reliable? Only probability of success counts!
Gage R&R studies are often conducted in the industry to determine the operating performance of a measurement system and determine if it is capable to monitor a manufacturing process. Several metrics are commonly associated with Gage R&R studies, such as the precision-to-tolerance ratio (P/T), the precision-to-total-variation ratio (%RR), the Signal to noise ratio (SNR), the %Reproducibility and the %Repeatability. While these metrics may suit well the overall industry, they could be problematic once applied in drug manufacturing sector for several reasons, (1) (bio)pharmaceutical assays are often more variable than common physico-chemical measurement systems and the usual criteria are too restrictive for the pharma industry, (2) analytical methods cannot always be improved once qualified, and (3) measurements are usually costly and time consuming, which makes difficult to have enough data to estimate all sources of variance with high precision.
Reducing Energy Costs by 8% by Optimizing Autogenous Mills
The grinding process alone accounts for 80% of the energy consumption. It consists of pulverizing limestone blocks to obtain the calcium carbonate used as a mineral filler in paper pulp.
Mills are the plant’s main equipment:
- 5 x 355 kW autogenous mills operating without prior crushing;
- 20 electric mills of various powers between 250 and 355 kW.
The case presented concerns only the autogenous mills, which are the most energy-consuming.
Surge Demand
The semiconductor chip shortage goes deeper within the supply chain due to a shortage of substrates leading to additional automotive production cuts. Tesla pushes the design of their custom AI chip for its Autopilot self-driving system while Ford poaches the executive in charge of the Apple Car project leader. Toyota to spend billions on battery production facilities for electric cars. NASA recently completed welding on the Artemis III Orion pressure vessel.