Like many other industries, the woodworking sector faces a variety of challenges. These include quality assurance, untapped efficiency potential, and a shortage of skilled workers. One possible response is further automation. Automation can reduce errors, improve quality, and increase efficiency. In addition, production can be increased and accelerated because machines can operate 24/7 and perform quality control faster and more precisely than humans. Finally, the increasingly scarce human resources can be deployed more purposefully by automating monotonous and physically demanding tasks.

HOMAG Bohrsysteme has developed an automated solution that addresses these challenges. The company is part of the HOMAG Group and provides customers in the woodworking industry with a wide range of support options through its high-tech machines and systems. Its product portfolio includes CNC machining centres, through-feed drilling machines, drilling and dowel-insertion machines, as well as machines for drilling and fitting insertion technology.

The newly developed solution focuses on fully automated loading of a vertical CNC machining centre. At the literal centre of the system is a robot that picks wooden workpieces from a stack, feeds them into the CNC machine, and removes and places them after processing. The key feature is that the workpieces are all individual and their shape and size are not known in advance. In addition, they are arranged chaotically on the stack. Furthermore, not only are the workpieces different from one another, but each must also be drilled individually. The relevant information is stored in a barcode on the workpiece.

Machine vision enables processing to take place completely autonomously despite these challenges. With the help of the machine vision software MVTec HALCON, the robot can recognise the different workpieces and grasp them safely. The software executes numerous algorithms and also reads the barcode information on the workpieces, forwarding it to the CNC machine. Based on this information, the required, different drilling operations are carried out.

Fully automating a labour-intensive process step

Such a fully automated cell developed by HOMAG is in operation at the carpentry workshop of MAB Möbel AG. The company from Muotathal, Switzerland, has been producing quality furniture since 1951 based on ecological and design-oriented principles.

“We want to continue developing with solutions that truly make sense. The further development of the cell with laser scanning and chaotic stacking was the function we had been waiting for. This allows the cell to meet our goal of batch size 1 production – and only then does automation make sense for us,” explains Luca Zingg, member of the management board responsible for corporate development at MAB.

Until now, an employee handled the loading of the CNC machining centre. This involved picking up the workpieces, scanning the attached barcode, placing them into the CNC machine, and depositing them on another pallet after processing. After several hours, this monotonous task becomes physically demanding and is not particularly efficient in terms of profitability.

Tobias Schwarz, Senior Director Product Development at HOMAG Bohrsysteme, explains the goal of the automation: “MAB has set itself the objective of increasing productivity, deploying employees more effectively – and above all in less physically demanding workplaces – and thereby reducing costs.

Another advantage of a fully automated production process is that the workpieces no longer need to be sorted before processing, since the application can also handle chaotically arranged stacks. This saves time in the upstream process step, which further increases productivity.”

The challenge during implementation was to develop a completely new solution, as nothing like this previously existed on the market. The task is also not easy for the machine vision system. This is due, firstly, to the enormous variety of workpieces – different surface decors must also be processed. Secondly, the image processing must function under ambient light. Because not every area is fully illuminated, less powerful vision systems may have difficulty determining the exact position of the workpieces. Finally, it is technically demanding to separate the surfaces of relatively flat boards.

“Despite these challenges, it was clear that such a fully automated solution had to be based on machine vision. We had to teach the robot to see. With other technologies, such as sensors, practical implementation would be virtually impossible – particularly in terms of speed,” explains Schwarz.

Enabling the robot to recognise individual workpieces

The application consists of several hardware components. At its core is a six-axis robot. A vacuum surface gripper system is used as an end effector. A 3D laser scanner is also mounted on the robot’s gripper arm. The drilling operations take place in the DRILLTEQ V-310 CNC machining centre from HOMAG. The machining centre offers a wide range of options for precise processing of wooden workpieces.

For the machine vision software, HOMAG chose MVTec HALCON. “We have been working with MVTec’s software for some time. HALCON has a huge pool of machine vision operators that allow virtually all machine vision applications to be implemented robustly. In addition, the software is flexible when it comes to combining different hardware components. And if technical questions arise, you can simply contact MVTec’s customer service,” explains Schwarz regarding the decision.

At MAB, the production process proceeds as follows: An employee places wooden workpieces onto an unknown and chaotic stack in the work area. The robot then moves over the stack so that the 3D laser scanner can scan it from above. The laser scanner then creates a 3D point cloud – a highly precise three-dimensional representation of objects consisting of numerous individual data points.

After image acquisition, the machine vision software MVTec HALCON extracts the top layer of wooden workpieces from the 3D point cloud and determines the spatial position of each individual workpiece. A stacking algorithm then calculates the optimal order in which the robot should remove the workpieces. This is an important detail because an unevenly unloaded stack could collapse. The robot then begins its work, removing the wooden workpieces according to the calculated order and transferring them to the CNC machining center. Before this, the 3D laser scanner captures a 2D image of the code. MVTec HALCON reads the code and transmits the information to the machine.

The workpiece is then processed according to this information. Afterward, the robot picks up the workpiece again and places it on the target stack.

Multiple image processing tasks

“We are seeing machine vision becoming increasingly popular in the woodworking industry and among carpentry workshops. Our software, MVTec HALCON, offers numerous methods – for example for inspection tasks or for collaboration with robots – that can sustainably support automation and digitalisation in this sector,” says Jan Gärtner, Product Manager HALCON at MVTec.

For the robot in the MAB system to work autonomously and grasp the workpieces precisely, the machine vision software must perform several tasks. First, MVTec HALCON converts the 3D point cloud into information for further processing. For this purpose, HALCON uses 3D object models. This central container forms the starting point for creating a coordinate system within the machine vision software, which is then transmitted to the robot.

Various HALCON operators first determine the distance from the gripper to the pallet, then calculate the top layer of workpieces, and finally determine the precise position of each individual workpiece. These positions are integrated into the coordinate system of the HALCON machine vision software and transferred to the robot.

During the 3D scanner’s capture of the top layer of the pallet, it also records 2D images. HALCON uses these images to read the information from the barcode attached to each workpiece. The challenge here is that the captured image is quite large, while the barcode region is correspondingly small. Reading such small barcodes is a major challenge for any industrial image processing software.

“The image-processing part of the implementation was not entirely trivial. Because of the flat boards, we had to combine 2D and 3D methods. This was possible with HALCON and significantly simplified the implementation,” explains Schwarz.

The system went into operation at MAB Möbel AG in summer 2025. “Thanks to the close coordination with the partners involved, we were able to achieve very good results right from commissioning. The system is now operating very reliably, which makes us very satisfied and gives us confidence for the future,” explains Luca Zingg.

“The increased level of automation significantly relieves MAB, as the employees who previously carried out this task can now focus on other, more important activities. At the same time, this solution represents an important development for us, because it allows us to significantly increase the automation level of our core machines and thus offer customers additional added value,” adds Tobias Schwarz, continuing: “Machine vision plays an important role here, because the technology acts as an automation enabler. In our collaboration with MVTec, we see the opportunity to offer our customers first-class and reliable solutions.”

The customer’s Rauma mill is Finland’s largest ever sawmill investment at €260 million and uses machine vision, artificial intelligence (AI) and integrated information systems for consistent quality and cost efficiency.

Building on a long-term relationship with parent company Metsä Group, ABB provided Extended Operator Workplace (EOW) tables for maximum ergonomics, visualisation and communication, as well as electrical power distribution, transformers and robots.

Since the technologies were commissioned a year ago the sawmill has turned out 40 logs per minute on a 130-metre sawline with a maximum sawing speed of 250 metres per minute, all managed from an ABB-designed central control room. The sawn pine timber is exported worldwide and can be used in construction – including windows, doors and general joinery, woodworking, furniture and packaging.

“With the support of partners, including ABB, we are achieving productivity that is at least three times higher than what is possible across the rest of Europe,” said Jaakko Vierola, technical director of the Rauma sawmill project, Metsä Fibre. “The durability of components and the service ABB provides exactly when we need it are crucial.”

The electrical distribution system with medium voltage and distribution transformers was designed and delivered by ABB. ABB’s robots, motors and drives were connected to other equipment, with 1,000 variable speed drives considered to be crucial to automation optimisation. The overall speed of the process is also due to a group of six robots equipped with machine vision.

Metsä Fibre and ABB specialists have worked together to maximise automated data collection through a third-party automation system. The strategy has been to use high levels of monitoring for quality and performance, minimise wood waste through sawing and sizing optimisation and eliminate manual handling through robots and automatic loading at the 24/7 mill. The sawmill’s by-products such as wood chips, sawdust and bark are used to produce pulp and bioenergy.

“The Rauma sawmill operates much like a process industry plant, where equipment cannot be maintained at night or on weekends, but electrical devices must always be reliable,” said Esa Kivioja, industry segment manager for ABB in Finland. “Here, we implemented the sawmill’s power distribution with the same quality standards as we did in Metsä Fibre’s bioproduct mills in Äänekoski and Kemi. This is a whole new concept in mechanical forest industry facilities.

“The energy efficiency of motors and other electrical equipment also plays a role. The impact of ABB’s deliveries is even more evident in the fact that production can continue without disruptions, and there is no idle time for the equipment.”

To support the Metsä Fibre team and the installed technologies on site, ABB also offers its spare parts and spare equipment services, as well as technical support.

Production is fully automatic and highly rational. To ensure quality of the hand brush woods, Lessmann has been relying on classic image processing for many years. But now the company has implemented an image processing system from the Bavarian system house Simon IBV that uses robust IDS industrial cameras and SIMAVIS H image processing software to detect even barely perceptible tolerance deviations particularly reliably.

The brush woods, which are milled fully automatically at a production rate of 1500 pieces per hour, are removed from the milling machine by a timed circulating chain with quiver-shaped receptacles and pushed onto a longitudinal conveyor belt. A multi-camera system is installed on the conveyor belt, which checks the 2 to 6-row hand-brushed timbers for defects such as cracks, splintering and size.

“The testing task is particularly demanding because the untreated copper beech varies greatly in its colour and grain. For example, cracks cannot always be clearly distinguished from dark grains,” explains Daniel Simon, authorised signatory at Simon IBV. But the choice of wood type has good reasons: on the one hand, red beech is recommended for the production of hand brushes due to its excellent properties, such as a special degree of hardness. On the other hand, sustainability plays a major role. Lessmann can source the brush wood from the surrounding area and thus both support regional forestry and avoid transport routes.

While the timbers pass through production on a conveyor belt, a total of four IDS cameras of the type GigE uEye FA are triggered by a so-called incremental encoder. This sensor reacts to the belt position so that any change in position of the brush body is detected by the belt movement. The image capture is offset by 2.5 mm per camera, so each camera takes a new image every 10 mm. The captured images are discarded until the first camera detects that there is a wooden body in the field of view. From this point on, the other three cameras are activated and up to 35 pictures are taken per camera. The number of images is limited by camera 1, as it outputs as soon as no more brush body is visible.

The images captured by the IDS cameras are pre-processed and composited simultaneously with the image capture. Thus, during the time of evaluation, the image acquisition as well as the pre-processing of the next brush can already take place. The individual images of the same situation from the four offset cameras are cropped, scaled and merged into one overall image by the software. The brush bodies are evaluated with differently weighted criteria for each camera. The weighting is done via the test sequence of the evaluation criteria. In a first step, rough geometric factors such as length, width, height, symmetry and shape deviations are evaluated.

The holes in the brush body are checked for position and overlap, followed by step-by-step surface inspection. “First, dark areas are segmented and evaluated according to setpoint specifications,” explains Matthias Eimer, system integrator at Simon IBV. “After that, deviating discolourations are searched for, singled out and evaluated according to setpoint specifications.”

Even the tolerances for rough spots can be set in the target values and are subsequently evaluated. Only in the last step of the frame-by-frame evaluation do the cameras search for cracks. Finally, the overall result is formed and merged from the individual evaluations of the views. The system checks a total of 32 setpoints, 27 of which alone for compliance with precisely defined tolerance specifications.

The uEye cameras used from the FA family are particularly robust and so ideal for use in harsh environments such as the brush factory. Camera housings, lens tubes and the screwable connectors meet the requirements of protection class IP65/67. They are also optimally suited for the multi-camera operation required here due to the integrated image memory, as this decouples the image capture from the image transmission and enables the buffering of images before transmission in this application.

The GV-5280FA industrial cameras with GigE Vision firmware are equipped with Sony’s IMX264 2/3in global shutter CMOS sensor, which also provides excellent image quality, high light sensitivity and an exceptionally high dynamic range. The four CMOS cameras used deliver almost noise-free, very high-contrast 5 MP images. With exactly these features, the camera model recommended itself for use in demanding brush testing. “The camera has the right resolution, the Sony sensor is very good and the protection class is met,” says Daniel Simon, summing up the selection criteria.

SIMAVIS H is an image processing software with which complete solutions can be quickly assembled. This machine vision software is based on ProVision (former Siemens development) and HALCON, a comprehensive standard software for industrial image processing with integrated development environment. This allows individual adaptation to the requirements of testing the wooden brush bodies: “The check for defects was programmed by us individually by hand, we have a lot of experience in the field of wooden surfaces. The tolerance thresholds can be set by the many setpoints depending on the feature,” Daniel Simon emphasises.

SIMAVIS H offers an intuitive user interface for the operating personnel of the finished system, including manual and automatic operation with type management, setpoint menu, authorisation concept, language switching, statistics module and much more.

“Through the control, automatic further processing is possible. With the new imaging solution, wood defects can now be detected more reliably. The proportion of defective wood bodies classified as good has fallen from around 2 per cent to less than 1 per cent,” explains managing director Jürgen Lessmann.

The subsequent filling of the brush bodies with wire is already fully automatic, and the packaging of the finished hand brushes can be carried out in future with the help of robotics. Due to the improved control of the wooden bodies, the previously necessary manual quality control of the finished hand brush can be omitted. Before packing, which is carried out by a SCARA robot, only a brief visual inspection is necessary. This relieves the machine personnel and increases productivity.

In this application, the use of artificial intelligence also offers further potential in the future. “AI will probably further improve the inspection result, which will allow further automation of production, as manual inspection operations can be eliminated,” Jürgen Lessmann predicts. And not only for brushes made of good wood: the multi-camera testing system can be adapted for countless products and materials.

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“The mission to combine traditional production with ´go digital strategies´ puts robots in a pole position,” says Dr Susanne Bieller, general secretary of the IFR.

Robots learn new tricks: Artificial intelligence software in combination with vision and other sensing systems, allow robots to master difficult tasks. One such task is bin picking, that in the past was only feasible for a human hand. New generations of robots are easier to install and program and they are connectable. Advances in communication protocols integrate robots seamlessly into automation and Industry 4.0 strategies.

Robots work in smart factories: The automotive industry pioneered smart factory solutions utilizing industrial robots throughout assembly lines that have dominated traditional automobile production for more than 100 years. The future belongs to networked interaction of robots and autonomous guided vehicles – or rather autonomous mobile robots (AMRs). Equipped with the latest navigation technology, these mobile robots are much more flexible compared to traditional production lines.

Car bodies are conveyed on driverless transport systems. They can be decoupled from the assembly line flow and redirected to assembly stations where individually equipped variants can be assembled. When models are changed completely, it is only necessary to reprogram the robots and AMRs rather than to dismantle the entire production line. With the integration of human-robot collaboration workstations picking up momentum, robot suppliers report robots working hand-in-hand with humans without fencing.

Robots enter new markets: The connectivity breakthroughs contribute to increased robot adoption in manufacturing sectors that have only recently turned to automation, such as food and beverage, textiles, wood products and plastics. Ongoing digital transformation will lead to completely new business models, because producers can diversify more easily than ever. In the smart factory, different products are assembled subsequently by the same equipment – the traditional production line no longer exists.

Robots reduce carbon footprint: Investments in modern robot technology will also be driven by the requirement for a smaller carbon footprint. Modern robots are energy-efficient, thus directly reducing energy consumption of production. Through higher precision, they also produce fewer rejections and substandard goods, which has a positive impact on the ratio of resource input over output. In addition, robots help in the cost-efficient production of renewable energy equipment, such as photovoltaics or hydrogen fuel cells.

Robots help to secure supply chains: The pandemic situation has made the weakness of globalised supply chains visible. Manufacturers have the opportunity to rethink supply with a completely different outlook. When productivity is levelled through automation, manufacturers have increased flexibility that may not have been available in high-wage countries like most of the European Union, North America, Japan or the Republic of Korea. Robotic automation offers productivity, flexibility and security.

“Advances in robot technologies are contributing to increased robot adoption,” says Dr Bieller. “The Covid-19 pandemic hasn`t started any new trends but it accelerated the use of robotics beyond established practice. In this respect, the pandemic has proven to be the biggest single driver for change in industry.”

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The investment in automation represents the latest phase of the company’s high-tech design and manufacturing system, which has been developed by managing director Nicholas Showan.

Nicholas, an experienced engineer, who established the award-winning business in Barham in 1990, sought the expertise of Halesowen-based KUKA Automation + Robotics to help him shape a solution that complemented his lean manufacturing philosophy. “While I approached other robotics companies with my plans, KUKA was the most keen to discuss my ideas. Everyone involved in the project has been very helpful,” he commented.

The factory already boasts an array of automated machinery that is linked to a sophisticated online product design system devised by Nicholas to create an efficient, low-cost production operation from order placement to despatch.

“Jali has one of the highest productivity to staff ratios in the industry. I believe there’s more ground to be made by using automation instead of decades-old manufacturing methods,” said Nicholas. He plans to invest in additional automation solutions in the future, which may include a pick-and-place robotic application for a panel location racking system.

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The Decorative Panels Group (DPG) is Britain’s largest paper foil laminator supplying laminated board and furniture panels throughout Europe. Already using automated systems to cut panels to size, DPG had been investigating the use of robots for some time when two FANUC Robotics M710i robots became available to them through a company acquisition.

The robots are being used to load and unload Panels up to 3m in length into a Laminate edging machine. Panels, which can weigh up to 18.2kg are picked up by a robot at the input end using a vacuum gripper and placed onto a feeder which automatically takes the panel into the edging machine. A sensor in the gripper identifies stack height and the panel is delivered to within 5mm of the feeder table before being released. The process is reversed at the output end of the process.

Before the robot system was installed the process was manually loaded and unloaded usingthree operators per shift. The system is now operated by one operator who feeds stacks into and removes stacks from the system. Malcolm Forward, Managing Director, Decorative Panels, explains: “Its traditional to use portal frame or gantry type handling units in our industry but the space taken up by them is very prohibitive. The robots we’ve installed are ideal for confined space which in our case is at a premium. In addition by installing the robots we immediately remove the need for manual handling of heavy components and halve the labour requirement.”

As the robots were previously used for a different purpose, DPG were uncertain of their suitability. FANUC Robotics UK were contacted to make a thorough assessment of the requirement and to refurbish the two FANUC Robotics M710 robots.

Malcolm Forward explains: “Having these robots available made this a fantastic opportunity for us to try out robots – we didn’t know if they were suitable and FANUC personnel were very good. They carried out a site survey and undertook trials at their Coventry facility. This gave us all the confidence we needed to proceed with FANUC Robotics to refurbish the robots and to engineer the system.

“In summary the key benefits we have gained are saving space over traditional dedicated automated machines, saving labour costs and being able to adapt automation to an existing piece of machinery. So successful has the project been that we are now looking at the installation of additional robots.”

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