Category Archives: Automation

The BotsAndUs stand at IntraLogisteX was a hive of activity all day on the second and final day of the countrys leading intralogistics exhibition, taking place at the Coventry Building Society Arena on 29th and 30th March 2022.

Oana Jinga, Co-Founder, took time out to explain the rationale behind the companys robotic solution that captured the attention of so many visitors.

BotsAndUs technology focuses on data capturing and automation through fleets of autonomous robots that work alongside people and machinery in warehouse environments without workflow disruption, she explained. The data-gathering robot accelerates productivity, reduces unnecessary time spent processing and tracking and allows businesses to drive better business decisions. Existing customers include Huboo, MediaMarkt Saturn, British Airways, dnata.

At BotsAndUs, we have created a robot that accelerates productivity, reduces unnecessary time spent processing and tracking and allows businesses to drive better decisions. At the moment, our focus is on how we can enhance Mim (our data Robot) and our platform.

Mim is a fully autonomous mobile and modular robot that measures, tracks and finds goods at every stage of the process (delivery, storage and picking). Mim collects data in real-time on parcels, pallets and products as they pass through the warehouse and provides information on their conditions, volume and dimensions as well as their locations on the shelves.

The robots navigate among people and machinery, without workflow disruption and continuously map and scan the warehouse. The data is easily accessible or integrated straight into the WMS, so it can be analysed instantly from anywhere and allows teams to obtain full inventory visibility.

This has been an exciting time for BotsAndUs at IntraLogisteX as we presented the latest version of our autonomous robot, Mim, and its new use cases in warehouse environments. We were pleased to be able to let visitors meet Mim, and see its various features (pallet scanning and shelves scanning).

Visitors were able to see for themselves the advantages it brings to the table in terms of productivity, stock and order management. Were pleased they were able to see our platform as a solution that makes warehouse workers workdays easier so they can focus on tasks that truly matter, rather than replacing them from operations.

Originally posted here:

Automation captures the imagination at IntraLogisteX 2022 - Logistics Manager

Daniel Seiler, the former managing director of IDS, has been appointed CEO of Automation Technology.

He takes over from co-founder Michael Wandelt, who steps down after 24 years in role. Wandelt remains at the company and will be responsible for infrared product management.

Seiler will lead the Bad Oldesloe-based firm alongside CTO and founder, Dr-Ing Andr Kasper.

Seiler commented: AT's products have enormous growth potential and I am very excited to push further development together with AT's dedicated team. I am particularly fascinated by the fact that our 3D sensors and infrared cameras are used in almost every industry and that we have developed very exciting applications.

Seiler studied electrical engineering with a focus on communications engineering in Darmstadt, and later completed an executive MBA in Frankfurt. He has been working in the image processing industry for many years.

Starting as an application engineer at IDS Imaging Development Systems in 2005, Seiler led the US site in Boston and was then promoted to managing director.

After leaving IDS, he spent two years as CEO at the laser technology start-up Cycle, while at the same time supporting AT as a member of the advisory board.

Wandelt said it was a logical step for Seiler to take over from him as CEO. Wandelt commented: Through his business development in the US, he brings exactly the expertise that we need for our expansion plans.

The management team aims to strengthen Automation Technology and expand its product portfolio. It plans to launch its C6 3D camera series in the summer.

Continued here:

Daniel Seiler named CEO of Automation Technology - Imaging and Machine Vision Europe

North America extended its dominance for industrial automation hiring among power industry companies in the three months ending January.The number of roles in North America made up 57.3% of total industrial automation jobs up from 45.9% in the same quarter last year. That was followed by Asia-Pacific, which saw a -1.9 year-on-year percentage point change in industrial automation roles.

The figures are compiled by GlobalData, which tracks the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

GlobalData's thematic approach to sector activity seeks to group key company information by topic to see which companies are best placed to weather the disruptions coming to their industries.These key themes, which include industrial automation, are chosen to cover "any issue that keeps a CEO awake at night".

By tracking them across job advertisements, it allows us to see which companies are leading the way on specific issues and which are dragging their heels and importantly where the market is expanding and contracting.

The fastest growing country was the US, which saw 41.6% of all industrial automation job adverts in the three months ending January 2021, increasing to 52.2% in the three months ending January this year.That was followed by Mexico (up 1.2 percentage points), India (0.80), and Australia (0.2).

The top country for industrial automation roles in the power industry is the US, which saw 52.2% of all roles advertised in the three months ending January.

Some 2.2% of all power industry industrial automation roles were advertised in Shanghai (China) in the three months ending January.That was followed by Miami (US) with 2.2%, Pune (India) with 1.6%, and Bengaluru (India) with 1.5%.

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North America is seeing a hiring boom in power industry industrial automation roles - Power Technology

As intralogistics industry growth continues to accelerate, Beckhoff Automation is adding resources to enable distribution and fulfillment center, parcel and post operations to meet evolving technology demands. Under the guidance of Global Intralogistics Industry Manager Doug Schuchart, Beckhoff USA has expanded its industry team by hiring Nathan Hibbs and promoting Gilbert Petersen. The company has also grown its New Automation Technology portfolio with solutions targeted to intralogistics applications and taken on additional leadership roles in key industry groups, such as MHI.

Hibbs will serve as a Material Handling & Intralogistics Business Development Manager for Beckhoff USA. He worked at B&R Industrial Automation since 2011, starting as Sales Engineer and working up to Channel Sales Manager with responsibility for the companys southeast region and strategic business partnerships. Hibbs brings deep intralogistics industry expertise, particularly with right-size-packaging technology and other systems that require high degrees of motion control and mechatronics. Based in Greater Atlanta, he holds a bachelors degree in mechanical engineering from the Georgia Institute of Technology.

A longstanding Beckhoff engineering expert, Petersen transitioned into the role of Material Handling & Intralogistics Application Specialist at the beginning of 2022. He started at the company as an application engineer in 2012 and was promoted to System Consultant in 2019. Prior to his tenure at Beckhoff, Petersen worked as Technical Director for Integrated Web Finishing Systems and Application Manager for Baumller LNI. He is based in central Massachusetts.

In addition to the personnel news, Beckhoff became a founding member of the new Scan, Label, Apply, Manifest (SLAM) Industry Group at MHI, the largest material handling, logistics and supply chain association in North America. SLAM launched in February 2022 to provide thought leadership for best practices on the last 100 feet of every warehouse in the world, according to MHI. Beckhoff actively participates in the MHI Solutions Community and Conveyor & Sortation Systems (CSS) Industry Groups, among other intralogistics and material handling organizations.

By expanding our intralogistics solutions portfolio and industry team, Beckhoff is keeping pace with the exponential growth across all areas of intralogistics and is thriving in this industry, Schuchart said. Hibbs is an exciting addition to Beckhoff, and Petersen continues to provide outstanding engineering expertise for our wide-ranging customers. They will help me deliver superior support for equipment OEMs, warehouse integrators and equipment end users across all areas of distribution and fulfillment center, parcel and post operations in the U.S. Together, we will provide guidance for other Beckhoff subsidiaries as we scale up this team globally.

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Beckhoff Expands Intralogistics Industry Sales and Engineering Team - Automation World

By Roberto Michel March 29, 2022

MHS (Booth B7255), a provider of materials handling automation and software solutions, unveiled an integrated, robotic order fulfillment solution developed in tandem with Mujin and HAI Robotics.

The goods-to-robot solution, which is managed by MHSs Helix warehouse execution system (WES), boasts operational efficiency gains of up to 400%. A zero-pressure accumulation conveyor from MHS feeds totes to and from the order fill station. HAIs autonomous case-handling robots (ACRs) transport totes between storage and a Mujin piece-picking robot. The Mujin robot picks each item for the order from a source tote, packs it and signals to Helix that the conveyor can move the completed order to the outbound location.

By bringing together these different automation technologies to work together as a cohesive system, we can help customers not only address labor challenges in workflows like order picking, but improve overall efficiency and consistency too, said Rob Schmit, senior VP, distribution and fulfillment, MHS.

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MHS teams with partners on automated order fulfillment solution - Modern Materials Handling

Summary

Automating factories is the way of the future, but it needs to be completed holistically, thoughtfullyand thoroughly to realize all of the benefits.

Adding automation to factories is a multi-faceted initiative. Both the benefits and the challenges are plentiful. The issues associated with the workforce alone are varied and complex. Additionally, there are processes associated with hidden factories, those that may not be documented, but do, indeed take place to flow value through an operation.

How to characterize the return on investment also requires rational and sometimes creative justification depending on the goal one is hoping to achieve through the automation project(s). In spite of these challenges, the benefits of automation cannot be ignored, and may, indeed, be just what is needed to have a competitive factory.

Hidden Factories":Implementing automation may be one of the only ways to eliminate tribal knowledge and ensure that processes are captured and performed consistently without the intervention or dependence on the workforce members that know how to progress parts through experience and work-arounds. This means that factors creating variation must be dealt with, instead of compensated for.

Product quality:Automated technology will lead to less variation in people-dependent processes, equating to improved quality.For example, older machining equipment typically requires very skilled operators to manually set and check axes multiple times during a process.There may also be some creativity required to properly set up different part numbers.Loading programs into automated equipment requires the operator to choose the right program, but not depend on that operator to set up each and every parameter correctly.This will greatly reduce the incidents in which the dreaded operator error arises in problem-solving meetings in which rework or scrap decisions must be made.

These are but a few of the benefits associated with automating a factory.Based on these alone, it begs the question as to why every company isnt investing in automation now.Or, why werent investments made in previous years when the gap in skilled labor was first recognized.There can be many answers to these questions; however, in our experience it has been a combination of incomplete knowledge on best application, navigating the capital expenditure process, and, at times, adverse reactions among the workforce, concerned about losing their jobs.

Purchasing new automation equipment requires investment, and it competes with other use of limited cash. Unless there is a clear growth path for the business revenue in which automated equipment is a pure capacity increase, extra people are usually generated through the implementation process.If people are not being exited from the business, it can be hard to justify the expenditure.However, if people are being let go, automation projects can be met with resistance.People who remain employed may not have the skills required to maintain or program the automated equipment.Additionally, some of these newly required skillsets are not readily available in the marketplace.It is a difficult position businesses find themselves in.Also, just because the variation of the process is greatly reduced, does not mean that the quality or availability challenges of incoming material are automatically solved. Many times this must be resolved to fully experience the complete benefits of the automation.

Create a long-term plan.Three to five years would be an excellent place to start. Depending on the equipment needed, planning capital projects and acquiring the funds, combined with the lead time of the equipment, could take 18 months or longer in some cases. Planning horizons that exceed five years are subject to change as technology progresses rapidly. Understanding what equipment will be retired and what will replace it, the cost and rough benefit of each will be necessary. We suggest replacing unsafe equipment first, then moving toward equipment with higher customer demand to gain the most benefit.

Examine each piece of equipment and understand which part families could go onto the new automated equipment.Since the cycle times should be significantly less, or the yield significantly better, you may be able to combine demand from multiple pieces of legacy equipment ultimately requiring less machinery and floor space in the new configuration.Perhaps the highest-end, fastest equipment you are considering will ultimately leave too much excess capacity because of the characteristics and demand that can go onto it.In this case, maybe a lower-level piece is a better option?

Understand the impact on people.Again, with faster cycle times and better quality, fewer people should be required to produce the same value.It would be good if freed up human capacity could be trained to perform other functions in the enterprise such as continuous improvement.

These are a few examples of the challenges and benefits that automation can bring to factories.Automating factories is the way of the future, but it needs to be completed holistically, thoughtfullyand thoroughly to realize all of the benefits.

Shannon Karels (pictured on the right) is a senior operations manager who has led multiple lean transformations and run operations for two large publicly traded corporations across various industries and business models. She started her career in supply chain management and progressed through lean and operations leadership roles. Through this journey, Shannon has improved cultures in numerous manufacturing facilities by leading employee empowerment and engagement, and building highly successful teams, with a focus on business results. Shannon holds a Bachelors Degree in Supply Chain Management from Western Michigan University.Kathy Miller(pictured on the left) is a senior operations executive who has held numerous global vice president and director roles both in manufacturing and lean enterprise leadership. Kathy is a Shingo Prize Recipient for Large Businesses as a Plant Manager. She started her career in Operations as a 17-year-old co-op student at a vehicle assembly plant, and progressed through engineering, marketing, lean, and operations leadership roles, working for four large publicly traded corporations in executive roles. Kathy is a transformational leader who consistently delivers impressive business results through team development, process discipline, and continuous improvement. Kathy holds a Bachelors in Industrial Systems Engineering from Kettering University, MBA from Ball State University and a Masters in Applied Positive Psychology from the University of Pennsylvania. Kathy was inducted into the Women in Manufacturing Hall of Fame in 2021.

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Adding Automation to Factories: A Multi-faceted Initiative - Automation.com

Call for change: a wider scope of automation

Self-Optimizing Network (SON) is not a sufficient answer to the new demands:

The Ericsson Intelligent RAN Automation portfolio, shown in Figure 1, features end-to-end network automation that includes centralized and distributed SON solutions and new capabilities that support the transformation to a more open environment enabled for AI/ML, which empowers innovation and support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks.

Figure 1 Transformation of current offerings into a more open environment for innovation

The objective of RAN automation is to boost RAN performance and operational efficiency by replacing the manual work of developing, installing, deploying, managing, optimizing and retiring of RAN functions with automated processes. The AIs role is to unlock more advanced network automation performance to make RAN network functions more autonomous and replace manual processes with intelligent tools that augment humans. Furthermore, it makes both AI/ML powered RAN network functions and tools more robust for deployment in different environments.

Ericsson AI and automation foundations gives service providers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands. The aim to deliver improved network performance, accelerate time to market for new capabilities, target right investment for improved ROI and enhanced operational efficiency.

Ericsson Intelligent RAN Automation solutions provide the right automation where it makes sense, gives most bang for the buck. The Figure 2 illustrates how the task of efficiently operating a RAN to best utilize the deployed resources can be divided into different control loops acting all together according to different time scales and with different scopes. Intelligent RAN Automation solutions utilize AI/ML algorithms interacting and integrating with engineered algorithms and existing processes, where applicable, in all these control loops.

Figure 2 Holistic take on RAN Automation

The two fastest control loops are related to traditional Radio Resource Management (RRM). Examples include link adaptation in the fastest control loop and cell supervision in the second fastest) control loop. Functionality in these control loops is mostly autonomous, although mostly driven by engineered algorithms requiring complex configurations in a timeframe ranging from milliseconds (ms) to several hundred ms. In many cases AI/ML makes it possible to enhance the functionality in the fast control loops to make them more adaptive and robust for deployment in different environments. This, in turn, minimizes the amount of configuration optimization that is needed in the slow control loops.

The slower control loops shown in Figure 2 are related to traditional Network design, optimization and management. Examples include RAN coordination and network power management. In contrast to the two fast control loops these slower loops are today to a large degree manual. The slow control loops encompass the bulk of the manual work that will disappear as a result of RAN automation, which explains why AI/ML is especially attractive in these loops.

In the near term, we expect the AI/ML powered solutions to be more accurate and efficient for certain use-cases or applications than the rule-based powered solutions. In the long-term AI/ML powered solutions will be ubiquitous in the RAN and AI/ML just another tool to achieve best performing and cost-effective network.

Compared to traditional software, AI/ML technology introduces the elements of training, model concept drift, federated learning, and a stronger need for access to data. The life cycle management (LCM) processes set the roles of suppliers, integrators and CSPs in essence, who is responsible for what and who sells what to whom. As an industry, we must adjust the LCM of software to include AI/ML-LCM and technology to reach its potential as it evolves, maintaining a clear separation of concern and, with a minimum of variants, to avoid industry fragmentation. A very high-level AI/ML LCM process is captured in the figure below.

Figure 3 A high-level life cycle management (LCM)

We recognize four main LCM alternatives as shown in the Figure 3:

AI/ML lends itself much better to choosing level of global vs local adaptation in comparison with traditional rule-based solutions. Globalization of AI/ML model can be described, as a model that is trained once, e.g. in vendor environment and deployed in many networks and situations or it has ability to adapt correctly to new previously unseen data, etc. There is also a need to do local adaptations and train or re-train the AI models with unique local data.

Data collection is probably the biggest challenge to scaling the AI/ML. Public data (e.g. performance monitoring) is standardized exposed data available from a product or service supplied by a vendor to CSPs for the purpose of product operations and/or service delivery. Non-public data (e.g. AI model debug trace), on the other hand, is data containing sensitive information relating to Intelligent Property Rights (IPR) and is used by the vendor for innovation, and/or service development, verification and deployment. Generated non-public data is typically hundred thousand times larger in volume than the public data. Ericsson has therefore developed mechanisms to bring out just the data that is needed for the relevant use case from specific network elements.

A simulated environment is often used as our first development step with AI/ML-based algorithms, regardless of whether we use public and non-public field data or simulated data to train the final model.

The AI/ML algorithm may be improved over time, or complemented with other algorithms, to make the predictions more accurate, or by re-training the model with local data in the network where it operates. In a longer perspective, this iterative development may result in centralization of certain AI/ML resources as the system architecture and capabilities evolve. Data-driven development is important components in evolution of life cycle management of RAN SW.

We make a distinction between initial training of AI/ML algorithm, here defined as creating and training an ML algorithm in design phase, or training and re-training in maintenance phase. Once the AI/ML features are identified for the initial training of the AI/ML model, we know what data is needed for re-training when the model starts to drift, which might impact efficiency of network function or a process. The re-training can either be done off-line in data-driven development at Ericsson or within the operators network. In the latter case, the re-training is done with customer-unique data and often with the purpose of adapting to local environment that are hard to generalize with the data available off-line.

The industry has recognized that in order to transition to an industrialization phase and enable mass adoption of AI/ML, industry alignment is required. This results in all the major industry bodies trying to work out how they can leverage the technologies and claim their stake in the AI/ML landscape, leading to multiple and somewhat diverging directions being taken. To accelerate the coming industrialization phase and mass adoption, the industry must do more to align standards between 3GPP, ORAN, ONAP and ETSI by:

Objective of AI is to unlock more advanced network performance and automation and ultimately it is about delivering the value to customers. Data sciences are combined with telecom knowledge to create use-case driven and business driven approach to implementing AI where it makes most sense.

Table 1 Few of the latest use cases being industrialized

AI powered link adaptation is network optimization solution targeting improved spectrum efficiency. The feature introduces a neural network Ericsson compute to enhance link properties giving an improved spectral efficiency and increased throughput. Current link adaptation is optimized for high loaded systems. By utilizing information from adjacent cells, we enhance link adaptation for medium loaded systems with significant improvements in the spectrum efficiency.

AI powered advance cell supervision is network healing solution. Locally executed, self-learning, on RAN Compute, and self-retraining on the Centralized Training System, Machine Learning algorithms allows for continuous full network supervision that continuously looks for anomaly in cells performance. Capable of instantaneous and predictive detection and immediate recovery actions with minimal impact.

This offers Instantaneous detection and recovery of cells with degraded KPI, resulting in improved In-Service Performance (ISP). At model drift RBS triggers AI model re-training and deployment of new model by Centralized Training System in the cloud.

AI powered inter-DU coordination is network deployment solution where NR carrier aggregation (CA) between e.g., low-band/ high-band, provides enhanced peak rate and coverage extension. Selecting and configuring the most optimal DU partners, on a network-wide basis, can be challenging and time-consuming. Advanced RAN Coordination optimizes and automates this task, via a central application for optimal partner selection over the entire network. Machine Learning algorithms are used to predict the cell load to secure an optimal selection removing the need or manual selection and configuration.

Downlink Power Optimization is network optimization solution that uses Deep Reinforcement Learning technology to identify if cell TX power can be reduced without compromising coverage or performance. Equally the solution identifies cells where power increase is required for performance improvement. Power optimization saves energy and allows maximizing radio capacity in markets with strict RF emissions regulations. Continuous closed-loop optimization automatically maintains the optimum settings as the network evolves and traffic distributions change. Resulting in DL power reduction on coverage layer while maintaining traffic volume and improving DL and UL performance.

Ericsson is well on the way to innovate and significantly change the way the automation of RAN is done. Leveraging thought leadership in the intersection between data-driven AI/ML principles and RAN automation expertise, AI/ML is applied in the Ericsson Intelligent RAN Automation solution. The Ericsson Intelligent RAN Automation portfolio, which is the next step in SON, features end-to-end network automation and new capabilities that support the transformation to a more open environment enabled for AI/ML, empowering innovation, support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks. This innovative solution transforms RAN SW life cycle enabling AI operations and provides AI functions where it makes most sense. Ericsson AI and automation foundations give our customers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands. Applying AI in RAN enables to industrialize a wide range of use cases working across various control loop time frames. The use cases will enable our customers to create business value in terms of improved performance, higher efficiency, enhanced customer experience and ultimately create new revenue streams.

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How best to apply AI in the Intelligent RAN Automation - Ericsson

Panasonic Factory Solutions Europe has introduced its NPM G Series, an integrated range of Surface Mounted Technology production systems designed to respond in real-time to customer supply and demand changes through continuous, autonomous updates helping to make the autonomous factory a reality.

The Panasonic NPM G Series offers flexibility and customisable options to address production needs and extend automation in manufacturing.

Many production sites still rely on their employees knowledge and manpower the so called 5M (Human, Machine, Material, Method, Measurement) method. The new Panasonic NPM G Series uses Artificial Intelligence (AI) and automated production to improve the process.

The use of the auto setting feeder (ASF), the NPM-GP / L stencil printer and the NPM-GH pick and place machine enables customers to set-up individual, flexible, efficient, and economical production lines.

Automated material supply is delivered by the ASF smoothly inserting components into the production line, without the requirement for an operator to physically connect the old component reel with the new reel using splice tapes or clips. It automatically peels off the cover tape for feeding surface-mounted components with a width of 4mm to 104mm.

The loading unit then automatically feeds the next tape and refills as required. Existing Panasonic machines from the NPM and NPM-X Series can also be adapted and equipped with the new ASF.

In addition, the NPM-GP / L stencil printer offers high-precision printing and solder performance combined with a high degree of integrated automation. Up to 10 masks can be stored and switched autonomously by a mask changer.

An automatic solder supply and remove function, in combination with autonomous replacement of the underlay pin that supports the PCB, enables efficient and high-speed production.

The printer achieves a printing performance with a mechanical repeat accuracy of 3.8 m and a cycle time of 12 seconds, which includes the cleaning process after each printing operation.

Further in the production line, the compact and lightweight placement head of the NPM-GH pick and place machine delivers high-level productivity (max. 41,000 chips per hour total number of assembled chips per hour under optimal conditions) with outstanding accuracy of 15 m, as well as optional ultra-precise placement at 10 m.

Thanks to simultaneous front and rear operations, operability has been improved. The NPM-GH increases overall quality, output and ultimately boosts automation within the production line.

The entire production process and machines are monitored through feed-forward and feed-back communication technology, called APC-5M. Adaptive Process Control (APC) tracks the correct placement of components based on the solder printing position and transfers feedback to the stencil printer in case of misalignment.

APC-5M detects 5M variations as well as line changes in real-time and ensures a smooth production process without downtime. Thanks to AI, the control system improves and specifies detections and feedback after each production process.

The sequential release of NPM G Series has started with additional solutions being released throughout the year.

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Panasonic launches new factory automation systems - Robotics and Automation News

This DIY Home Automation Market report studies the diverse and growth picture of the DIY Home Automation industry. It is an executive summary of the DIY Home Automation industry highlighting its major trends, other findings, and recommendations. The report studies the DIY Home Automation industry by highlighting the individual companies, investors, producers, distributors, and providers of raw materials. The report details the risks, opportunities, mature segments, and emerging segments in the market. The report forecasts the revenue growth of the global DIY Home Automation market at regional, global, and country level and analyzes the latest trends in the industry in the every sub-segment in the industry.

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The domestic Semiconductor Robotic Automation industry and its vulnerabilities like cheap imports and demand fluctuations are focused in the report.

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Semiconductor Robotic Automation Market Expected To Surge Significantly By 2028: FANUC, KUKA, ABB, Yaskawa, Kawasaki, ChattTenn Sports - ChattTenn...