The combination of a healthy construction market and a solid economy are keeping work pipelines flowing and bottom lines strong for metalworking operations. However, even the biggest business boom can bring its own set of unique challenges. According to a report by Grainger, 59% of metalworking firms are having a difficult time finding and retaining qualified employees, and 45% are struggling with competency levels in their workforce. As unemployment rates remain low and companies often compete for the same candidates, it is critical to find solutions for staffing problems.
Scrap handling systems, fluid recycling equipment, and industrial water and wastewater treatment solutions offer automation opportunities that address the staffing issue and benefit the bottom line.
Automated conveying equipment transports metal scrap from production through load-out with minimal employee involvement. Conveyors also:
Reduce need for lift truck operators
Eliminate strenuous manual tasks
Improve workplace safety
Improve employee attraction, retention
Lower labor-related costs
Chip processing systems automate the reduction of turnings and bushy wads to flowable, shovel-grade chips while separating scrap from fluid. Vertical axis crushers, for example, maximize labor allocation by providing continuous, positive feed operations and automatically remove solids to prevent equipment damage. These systems:
Reduce potential contact with sharp metal material
Minimize environmental risks
Maximize value from scrap metal recycling, reusing spent coolant
Tramp oil separators automatically remove free-floating and mechanically dispersed tramp oils, bacteria, slime, and inverted emulsions from individual machine sumps, central systems, and wash tanks. This equipment:
Eliminates need to manually vacuum oil from rinse tanks
Can reduce tramp oil to less than 1% in a single pass
Reduces new fluid purchase costs up to 75%
Reduces hazardous waste volumes up to 90%
Mechanical or automatic hydraulic dumpers simplify cart unloading with efficient one-person operation that uses a handheld control to operate equipment.
Load-out systems complete scrap handling by moving metal scrap to distribution bins for haul-away to the recycler. These systems provide efficient, automated filling, maximizing container fill and value from the recycler.
Modern equipment in conveying, scrap handling, fluid recycling, and water/wastewater treatment also improves uptime with low maintenance, eliminating overstaffing to ensure that employees are as productive as possible.
Productivity improvements in today’s manufacturing plants and machine shops are typically derived from evaluating machining equipment, operating procedures, and labor allocations associated with process-side activity. Continuous improvement in this area should also include waste streams, which offer several opportunities to address the problem of staffing a plant with a skilled workforce. Removing the human element whenever possible keeps a production line flowing safely and efficiently – not to put people out of work but to retain qualified employees and minimize the impact of a shrinking workforce.
Ineffective processes that are labor-intensive and require constant attention inhibit an operation as business continues to ramp up. Working with an experienced equipment and systems provider to automate systems can help metalworking plants thrive in an environment where attracting and retaining qualified employees continues to be a challenge.
About the author: Mike Hook is sales & marketing director at PRAB, maker of engineered conveyors and equipment for processing turnings, chips, and metalworking fluids. He can be reached at firstname.lastname@example.org.
High-quality sensor data for Industry 4.0
Features - Automation / Industry 4.0 Target Guide
Knowing what types of sensors to use and what data to collect can drive connectivity and process efficiency.
Sensor technology is the prerequisite for implementing Industry 4.0. Sensors collect data on process and machine statuses, making it available for process-relevant information services and workflows. However, sensor costs and the variety of possible applications often make it difficult for users to appreciate the economic benefit.
“Sensors are the links between the digital and the real world and therefore one of the most important factors in the implementation of Industry 4.0. All higher-level data interpretation systems are blind without the right sensors,” says professor Jürgen Fleischer, one of the guide’s main contributors.
Ball screw lubrication
Fleischer says KIT projects show how information can be usefully captured and processed using sensors.
“Data can be captured in the drive components of machine tools to monitor their condition and optimize operation,” Fleischer says. “In ball screws, for example, the axial force and the friction torque on the ball screw nut can be measured. The exact lubrication requirement can then be determined by comparing the results with a model for friction behavior.”
He adds that KIT researchers developed an adaptive lubrication system that uses sensor data to significantly increase the service life of ball screws in tests. And in addition to lubrication controls, different drive components can be monitored by capturing structure-borne noise.
“These signals change throughout a component’s lifetime and allow conclusions to be drawn on the state of wear. The goal is predictive, condition-based maintenance, also known as predictive maintenance,” Fleischer says.
The software detects surface defects accurately in real-time using optical sensors (such as multi-camera systems). The data can then be fed back to the production system to enable a quick response if process parameters are breached.
Further examples of real-time capture of sensor data at the IWU include pressing, punching, and cutting forces recorded in forming machine tools.
Real-time sensor data
Whether or not to capture sensor data in real-time depends on the application.
“You have to identify the point up to which real-time capture makes sense, how the data is synchronized, and which sampling rates are necessary to obtain an accurate process description,” says Dr. Jörg Stahlmann, managing director of Consenses, an industrial measurement technology and digitalization solutions company. “We use 3D step models to understand our customers’ designs and to classify sensor data – such as the expected force and temperature flows – and kinematics correctly.”
Simulations can also be beneficial to making sense of sensor data.
“Simulations of components, assemblies, and machines give us a better understanding of the mechanical effects encountered in production plants,” Fleischer says. “We use this knowledge to make targeted use of sensors and to interpret the captured data more efficiently.”
However, not every application requires real-time capture, and there are certain instances where real-time data is not the most efficient.
“Real-time data is often provided by control units which originally collected it to control certain machine actions,” Stahlmann says. This goal does not always overlap with the requirements for the sensor data. Before far-reaching analyses or decisions are derived from this data, it is important to understand which signal is generated in each individual case. For example, in condition-based maintenance, real-time recording is superfluous.
“Condition-based maintenance does not require a rapid response to the collected data. The results of the data evaluation may even be delivered several hours after the data has been entered. Recorded data can be stored in a buffer so that it can be aggregated and evaluated at a later point in time, and the evaluation can be outsourced to a powerful server,” Fleischer says.
“If there is no economic justification, there is no need for real time,” says Dr. Thomas Päßler, forming machines group manager at IWU. “Real-time capture is not necessary for trend analyses conducted over a longer period. It is not necessary to keep all the data; only individual parameters should be generated and archived. In addition, there is little to be gained from capturing data required for management purposes in real-time, including parameters related to the economic efficiency of production, such as how many components of a particular type were produced on one plant.”
The German Academic Association for Production Technology (WGP) also addressed the question of meaningful and appropriate automation in its Industriearbeitsplatz 2025 paper, concluding that “all technical possibilities should be exploited in the economic value creation process, but maximum automation is not always necessary or useful.”
However, real-time sensor data is inevitably necessary for machine, tool, or workpiece protection or process stability.
“Real-time data capture is indispensable when it’s the only way to prevent damage,” Päßler says. “This applies in the case of tool breakage or excessive stress on assemblies such as bearings or frame components. In order to preclude the possibility of any rejects, it makes sense to capture the material properties in real-time with the appropriate sensors.”
Real-time detection can also help prevent damage to workpieces during production and rectify any errors made during setup.
“Errors made during the setup of machine tools or in the NC program can lead to collisions,” Fleischer says. “If these are detected quickly enough, the machine can be stopped, and material damage reduced.”
Linked sensor data
Scientists at the IWU use real-time monitoring of forces, paths, and stretching on forming presses. Rather than being evaluated individually, these different types of data are fed into Smart Stamp, a software-based analysis module, where they are merged and analysed. By combining data, manufacturers can know if the press is working in its normal range, if the tool is wearing too quickly, or if the ram mounted on the upper tool has a critical tilt that could mar the workpiece.
However, there are points on the machine where it is not possible to mount real sensors, as they would be difficult to access, or installation would be too complicated and expensive. There may be no relevant data available for particular processes and machine statuses. The IWU solution is to use virtual sensors.
Real sensors, mounted at different points on the machine, serve as the basis of this technology, and a digital twin in the form of a virtual sensor is created from their measured values. This calculates the values that a real sensor would record at a relevant but inaccessible location.
AMRs use sensors, artificial intelligence (AI), 2D vision systems, and 3D vision systems to navigate in complex work environments.
Autonomous guided vehicles (AGVs) are mobile but use wires or tracks, moving along narrowly defined routes.
Common AMR uses
Warehouse fleet management
Manufacturing work cells
Tugger, trolley replacement
First used by NASA for space exploration, autonomous mobile robots (AMRs) are one of the fastest-growing categories in the automation world, capable of guiding themselves across factories to perform work in multiple locations.
Industries using AMRs
Reasons for service robot deployment
IDC’s 2018 Commercial Service Robotics Survey: More than 90% of companies surveyed plan commercial service robot use; led by retail, wholesale/distribution.
Expanding production, not replacing producers
Departments - Welcome
Manufacturing technology providers offer automation as a way to handle an ongoing worker shortage, not eliminate human interaction on the shop floor.
On the political campaign trail, automation discussions sound familiar. Fears of machines replacing people have abounded since the first Industrial Revolution, and during the fourth massive change to industrial production (Industry 4.0), those cries are particularly noticeable.
Decades from now, those concerns will likely seem quaint. Rather than eliminating humans from manufacturing, embracing the steam engine created countless new opportunities and careers. Electrification had a similar impact, replacing jobs for coal-shoveling brute labor with new positions operating equipment. Computer numeric control (CNC) technology eliminated some hand-crafted metal cutting, but it created opportunities for programmers, engineers, and machinists willing to learn a new way of doing things.
Connected, automated equipment brings up fears of robots loading machining centers 24/7 with humans stopping by occasionally to perform maintenance. However, the reality provides hope for human interaction. Even with artificial intelligence (AI), deep learning, and neural networks, modern machinery can’t replace the experience, creativity, and adaptability of workers. Tesla Motors, for example, couldn’t hit production targets when it relied on robotic assembly, getting the Model 3 electric car to the masses only after hiring more people to staff assembly lines.
In this Automation/Industry 4.0 Target Guide, manufacturing technology providers offer automation as a way to handle an ongoing worker shortage, not eliminate human interaction on the shop floor.
Certainly, automating functions will eliminate specific jobs. However, Industry 4.0 technologies enable greater flexibility and efficiency. Companies should be able to use the excess capacity created by increased production to expand into new markets and products – something that happened with each of the previous Industrial Revolutions.
The way forward requires new skills, new ways of approaching challenges, and new tools – many of which are covered in these pages. As with Industries 1.0 through 3.0, the latest wave is happening, despite some politicians’ resistance, making it vital to embrace that change and prepare for it.
Elizabeth, Robert, Eric, & Michelle
Gold nanoparticles fight infectious biofilms
Departments - Medical Innovations
Researchers at ICFO collaborate with medical device and pharmaceutical company B. Braun Surgical to improve surgical medical meshes.
Surgical medical meshes are key in recovery procedures for damaged-tissue surgeries – their flexible and conformable design holds muscles tight and reduces recovery time compared to sewing and stitching methods. However, these meshes carry the risk of bacterial contamination during surgery and formation of an infectious biofilm over mesh surfaces.
Biofilms impede antibiotic agents from reaching and attacking bacteria on the film, meaning antibiotic therapies could fail against resistant bacteria.
Post-surgery aseptic protocols have been implemented in the past, but none have completely eliminated bacteria.
Institute of Photonic Sciences (ICFO) researchers Dr. Ignacio de Miguel and Arantxa Albornoz, led by Catalan Institution for Research and Advanced Studies (ICREA) professor at ICFO Romain Quidant, collaborated with researchers Irene Prieto, Dr. Vanesa Sanz, Dr. Christine Weis, and Dr. Pau Turon from medical device and pharmaceutical device maker B. Braun Surgical to devise a technique that uses nanotechnology and photonics to combat biofilms. Researchers developed a medical mesh with a chemically modified surface, allowing the mesh to anchor millions of gold nanoparticles that efficiently convert light into heat at localized regions.
In the in-vitro experiment, the surgical mesh was coated with millions of gold nanoparticles – using a scanning electron microscope (SEM) to observe a homogenous distribution of nanoparticles – and tested to ensure long-term stability of particles, non-degradation of material, and non-detachment or release of nanoparticles into the surrounding environment. Once the modified mesh was ready, it was exposed to S. aureus bacteria for 24 hours until a biofilm formed on the surface. It was then exposed to short, intense pulses of near-infrared light (800nm) for 30 seconds to ensure thermal equilibrium was reached. This treatment was repeated 20 times with 4 seconds of rest between each pulse. Discoveries included:
Illuminating the mesh at the specific frequency induced localized surface plasmon resonances in the nanoparticles – a mode that results in the efficient conversion of light into heat, burning the bacteria at the surface.
Biofilm bacteria that remained alive became planktonic cells, recovering their sensitivity or weakness toward antibiotic therapy and immune system response.
Increasing the amount of light delivered to the mesh’s surface caused dead bacteria to lose adherence and peel off.
Operating at near infrared light ranges was compatible with in-vivo settings, meaning that such a technique would likely not damage the surrounding healthy tissue.
Recurrent heating of the mesh did not affect its conversion efficiency capabilities.
“The study’s results have paved the way toward using plasmon nanotechnologies to prevent the formation of bacterial biofilm at the surface of surgical implants,” Quidant says. “Several issues still need to be addressed, but it is important to emphasize that this technique will signify a radical change in operation procedures and further patient post recovery.”
The researchers hope to eventually extend this technology to other areas where infectious biofilms pose a problem.