Top of the best free 2D CAD software for your laser cutting projects

LUCIE GAGET| October 26, 2018
TOP OF THE BEST FREE 2D CAD SOFTWARE FOR YOUR LASER CUTTING PROJECTS
We recently made a top of the best free 3D CAD software (or computer-aided design software) for your 3D printing projects. But what about laser cutting? We decided to make a selection of the best free 2D CAD software to help you with your laser cutting projects. You want to use laser cutting but you don’t know which software you should use? We are going to help you with this!

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What are the Risks that Manufacturing Face in the Current Times?

Article | December 30, 2021

Risk management in manufacturing has always been a top priority for manufacturers to avoid any unfortunate incidents. As a result, it is possible to create a more secure work environment for employees by conducting risk assessments and implementing remedies. “If you don’t invest in risk management, it doesn’t matter what business you’re in, it’s a risky business.” – Gary Cohn, an American Business Leader. As of 2019, the worldwide risk management market was valued at $7.39 billion, and it is expected to rise at a CAGR of 18.7% from 2020 to 2027, according to allied market research. Why is Risk Assessment Critical in Manufacturing? The manufacturing industry must have a credible risk assessment and management plan to defend itself from any breaches. Risk assessment helps firms understand the dangers they face and their implications if their systems are compromised. Hence, risk assessment is very critical in the manufacturing industry. Five Risk Assessment Principles Identify hazards/risks - Employers must examine their workers' health and safety risks. Therefore, an organization must regularly inspect its employee’s physical, mental, chemical, and biological threats. Identify who may be hurt and in what way – Identifying the personnel both full-time and part-time at-risk. Employers must also examine threats to agency and contract personnel, visitors, clients, and other visitors. Assess the risks and act accordingly - Employers must assess the likelihood of each danger causing injury. This will evaluate and lower the chance at the working space. Even with all safeguards, there is always some danger. Therefore, employers must assess if danger is still high, medium, or low risk. Get the Risks Documented - Employers with five or more employees must record the critical findings of the risk assessment in writing. In addition, register any risks identified in the risk assessment and actions to minimize or eliminate risk. This document confirms the evaluation and is used to examine working practices afterward. The risk assessment is a draft. It should be readable. It shouldn't be hidden away. The risk assessment must account for changes in working techniques, new machinery, or higher work objectives. 5 Manufacturing Risks to Consider in 2022 Accidents at Work Even if official safety policies and programs are designed, followed, and enhanced, manufacturers may endure workplace accidents and injuries. Risk assessment for workplace accidents assists in mitigating the negative impact on both employees and the organization. Environmental Mishaps Manufacturers have distinct issues regarding fuel handling and hazardous waste disposal in facilities. Sudden leaks or spills may be extremely costly to clean up and result in fines from state and federal agencies. Risk assessments for such plant accidents assist businesses in mitigating financial losses. Equipment Breakdowns Essential machinery throughout the production process might fail at any time, incurring significant repair or replacement costs. Therefore, it's critical to recognize that business property insurance may not cover mechanical issues. Risk assessment and prepayment solutions protect against equipment failures without interfering with typical company operations. Supply Chain Disruption Dependence on your supply chain may result in unintended consequences that are beyond your control. For example, if you experience downtime on the manufacturing line due to a supplier's failure to supply materials or parts, you risk losing revenue and profitability. If a disturbance to your supply chain poses a hazard, risk management can assist you in managing it more effectively by quickly identifying the risk and providing a suitable response. Operation Temporarily Suspended Depending on the severity of the weather event, a factory might be severely damaged or perhaps utterly wrecked. While major repairs or rebuilding are being undertaken, recouping lost income might be vital to the business's future profitability. Risk assessment in this area enables your organization to budget for overhead expenditures such as rent, payroll, and tax responsibilities during the period of suspension of operations. Final Words Risk management is critical in manufacturing because it enables manufacturers to comprehend and anticipate scenarios and create a well-planned response that avoids unnecessary overhead costs or delays in delivering the production cycle's final result. Manufacturing risks are undoubtedly not limited to the risks listed above and may vary according to the nature of the business and regional environmental conditions. Therefore, create a well-defined strategy to overcome threats in your business and be productive at all times. FAQ How are manufacturing business risks classified? In most cases, the business risk may be categorized into four types: strategic risk, regulatory compliance risks, operational compliance risks, and reputational risks. Why should a manufacturer conduct a risk assessment? Every manufacturing employment has risks for injury or illness. But risk evaluations can significantly minimize workplace injuries and illnesses. In addition, they assist companies in discovering strategies to reduce health and safety risks and enhance knowledge about dangers.

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The Factory of the Future

Article | December 2, 2021

The world of manufacturing is continuously evolving in the 21st century, and companies have to combat competition, altering consumer demands, and unexpected events to be able to deliver in today’s experience. Global connectivity, innovation, and disruption are all reshaping the manufacturing industry, but a world-class business platform can help companies transform operations digitally to keep up with an evermore digitized world. The factory of the future will allow manufacturers to enhance production through the convergence of information technology with factory operations, combining the effectiveness of the virtual world with the materiality of the physical world to lower costs, increase flexibility, and better meet customer expectations. The factory of the future functions on four dimensions: resource planning, manufacturing planning, planning and optimization, and manufacturing operations. Resource planning involves defining and simpulating the plant layout, flow, assets, and resources needed to efficiently develop products in a safe environment. Normal production change requests can be quickly validated by using 3D virtual experience twin technology. This technology could also quickly pivot operations to alternative products in the case of disruptive events. Manufacturing planning enriches the resource and product definition by defining and validating a process plan and creating work instructions that meet production goals. Digital visualization of resource and process changes can also help speed up time-to-production in any scenario no matter the location by leveraging the cloud. Planning and optimization of supply chains across planning horizons will help manufacturers gain visibility with planning and scheduling by having the ability to model, simulate, and optimize alternative supply and production plans to reduce disruptions. Lastly, manufacturing operations management can transform global production operations to attain and maintain operational excellence. Manufacturers can create, manage, and govern operational processes on a global scale while maintaining operational integrity to meet altering demands. For the factory of the future to come about successfully, there needs to be connected technology and shared data. Technology has to be adaptable with robotics and equipment that can be reconstructed to house changes and new products. An AI-powered product demand simulation is necessary to maintain agility and boost productivity. A versatile, cross-functional workforce with the ability to explicate data and function well in AR environments is also required along with smart factory technology such as wearable sensors and virtual prototypes. Through all this, the factory of the future can connect technologies across the product life cycle while optimizing the workforce and increasing sustainability. Although achieving the factory of the future has several benefits, creating a feasible factory of the future plan can be challenging. In 2018, only 12% of companies had a mature factory of the future plan. One of the main challenges that companies face is a lack of internal skills to devise digital solutions. However, this can be combated by carefully considering how you can utilize digital technologies to deliver improved performance, resiliency, and flexibility. It is easier to begin with small steps and to collaborate with a partner who could support your efforts to build toward your desired transformation goal. It is important to always be prepared by evaluating your next steps, industry trends, and progress metrics. It is also crucial to focus on the people, process, and technology you’re using to have a successful transformation journey. Manufacturing with the factory of the future can provide savings in a wide range of categories. For example, it can reduce virtual vehicles build time by 80%, increase on-time performance of industrial equipment by 45%, and reduce modular construction time of construction, cities, and territories by 70%. Leading the transformation of the manufacturing space towards the direction of the factory of the future will allow manufacturers to work smart and better meet the needs of the end consumers.

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Building a Smart Factory is Possible Using Machine Learning

Article | December 7, 2021

Machine learning in manufacturing is becoming more widespread, with businesses like GE, Siemens, Intel, Bosch, NVIDIA, and Microsoft all investing heavily in machine learning-based ways to enhance manufacturing. Machine learning is predicted to expand from $1 billion in 2016 to USD 9 billion by 2022at a compound annual growth rate (CAGR) of 44% throughout the forecast period, according to Markets & Markets. The technology is being utilized to cut labor costs, achieve better transition times, and increase manufacturing speed. “I advocate business leaders get to know more about what AI can do and then leverage AI in proofs of concept.” – Michael Walton, Director and Industry Executive, Microsoft speaking with Media 7 Machine learning can help enhance manufacturing processes at the industrial level. This can be achieved by assessing current manufacturing models and identifying flaws and pain factors. Businesses can rapidly address any difficulties to keep the manufacturing pipeline running smoothly. Let us explore how machine learning is transforming manufacturing operations. How Machine Learning Is Transforming Manufacturing Operations “The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.” - Pedro Domingos Machine learning in manufacturing is revolutionizing manufacturing operations and making them more advanced and result-oriented, so let's have a look at how this is unfolding. Allows for Predictive Maintenance Machine learning provides predictive maintenance by forecasting equipment breakdowns and eliminating wasteful downtime. Manufacturers spend far too much time correcting problems instead of planning upkeep. In addition to enhancing asset dependability and product quality, machine learning systems can forecast equipment breakdown with 92% accuracy. Machine learning and predictive analytics increased overall equipment efficiency from 65% to 85%. Increases Product Inspection and Quality Control Machine learning is also utilized for product inspection. Automated inspection and supervision using ML-based computer vision algorithms can discriminate between excellent and bad products. These algorithms simply need excellent samples to train; therefore a fault library is not required. However, an algorithm that compares samples to the most common errors can be built. Machine learning reduces visual quality control costs in manufacturing. Forbe's says AI-powered quality testing can boost detection rates by up to 80%. Logistics-related Tasks Are Automated To run a production line, industrial companies need considerable logistics skills. The use of machine learning-based solutions can improve logistics efficiency and save expenses. Manual, time-consuming operations like logistics and production-related documentation cost the average US business $171,340 annually. It saves thousands of manual working hours every year to automate these everyday procedures. Using Deep Mind AI, Google was able to lower its data center cooling bill by 40%. Creates More Business Opportunities Machine learning is frequently used in the production process. Substantial data analysis is required to create new items or improve existing products. Collection and analysis of huge amounts of product data can help find hidden defects and new business opportunities. This can help improve existing product designs and provide new revenue streams for the company. With machine learning, companies can reduce product development risks by making smarter decisions with better insights. Protects Company’s Digital Assets On-premise and cloud-based machine learning systems require networks, data, and technological platforms to function. Machine learning can help secure these systems and data by restricting access to vital digital platforms and information. Humans’ access sensitive data, choose applications, and connect to it using machine learning. This can help secure digital assets by immediately recognizing irregularities and taking appropriate action. Harley Davidson's Sales Climbed by 40% Using Albert – The ML & AI-Powered Robot Today, traditional marketing is harder to break through. It's easy to see why Albert (an AI-powered robot) would be a good fit for Harley Davidson NYC. Thanks to machine learning and artificial intelligence, robots are producing news stories, working in hotels, controlling traffic, and even running McDonald's. Albert works well with social media and email marketing. It analyzed which customers are more likely to convert and modifies the personal creative copies on its own for the next process. Harley-Davidson is the only company to employ Albert in its business. The company evaluated customer data to find prior consumers who made purchases and spent more time browsing the website than normal. Albert used this data to categorize customers and scale up test campaigns. Using Albert, Harley-Davidson's sales climbed by 40% and leads increased 2,930%, with half coming from high-converting ‘lookalikes' detected by AI and machine learning. Final Words The groundbreaking benefits of machine learning are the pillars of machine learning applications in manufacturing. Machine learning in manufacturing helps enhance productivity without compromising quality. According to Forbes, Amazon has automated warehouse logistics picking and packing using a machine learning system. With Kiva's help, Amazon's typical ‘click to ship' time dropped from 60-75 minutes to 15 minutes. So, industry leaders are seeing fantastic outcomes, and machine learning in manufacturing is the future. FAQ How is machine learning used in manufacturing? Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process. Which two forms of machine learning are there? Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data. What is a machine learning model? A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How is machine learning used in manufacturing?", "acceptedAnswer": { "@type": "Answer", "text": "Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process." } },{ "@type": "Question", "name": "Which two forms of machine learning are there?", "acceptedAnswer": { "@type": "Answer", "text": "Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data." } },{ "@type": "Question", "name": "What is a machine learning model?", "acceptedAnswer": { "@type": "Answer", "text": "A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm." } }] }

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Manufacturing Production Planning and Control: What, Why, and How?

Article | January 3, 2022

Production planning and control are critical components of any manufacturing organization. It helps organizations with the regular and timely delivery of their goods. Furthermore, it allows manufacturing businesses to increase their plant’s efficiency and reduce production costs. Numerous software and tools for production scheduling and planning are available on the market, including Visual Planning, MaxScheduler, and MRPeasy, which assist manufacturing organizations in planning, scheduling, and controlling their production. According to KBV Research, the manufacturing operations management software market is anticipated to reach $14.6 billion by 2025 globally, expanding at a market growth of 10.2 percent CAGR during the forecast period. So, what exactly is production planning and control? Production planning is an administrative process within a manufacturing business. It ensures that sufficient raw materials, personnel, and other necessary items are procured and prepared to produce finished products according to the specified schedule. Scheduling, dispatch, inspection, quality control, inventory management, supply chain management, and equipment management require production planning. Production control makes sure that the production team meets the required production targets, maximizes resource utilization, manages quality, and saves money. “Manufacturing is more than just putting parts together. It’s coming up with ideas, testing principles and perfecting the engineering, as well as final assembly.” – James Dyson In oversize factories, production planning and control are frequently managed by a production planning department, which comprises production controllers and a production control manager. More significant operations are commonly monitored and controlled from a central location, such as a control room, operations room, or operations control center. Why Should You Consider Production Planning? An efficient production process that meets the needs of both customers and the organization can only be achieved through careful planning in the early stages of production. In addition, it streamlines both customer-dependent and customer-independent processes, such as on-time delivery and production cycle time. A well-designed production plan minimizes lead time, the period between placing an order and its completion and delivery. The definition of lead time varies slightly according to the company and the type of production planning required. For example, in supply chain management, lead time refers to the time required for parts to be shipped from a supplier. Steps in Production Planning and Control Routing The first stage of production planning determines the path that raw materials will take from their source to the finished product. You will use this section to determine the equipment, resources, materials, and sequencing used. Scheduling It is necessary to determine when operations will occur during the second stage of production planning. In this case, the objectives may be to increase throughput, reduce lead time, or increase profits, among other things. Numerous strategies can be employed to create the most efficient schedule. Dispatching The third and final production control stage begins when the manufacturing process is initiated. When the scheduling plan is implemented, materials and work orders are released, and work is flowing down the production line, the production line is considered to be running smoothly. Follow-Up The fourth stage of manufacturing control ascertains whether the process has any bottlenecks or inefficiencies. You can use this stage to compare the predicted run hours and quantities with the actual values reported to see if any improvements can be made to the processes. Production Planning Example Though production planning is classified into several categories, including flow, mass production, process, job, and batch, we will look at a batch production planning example here. Manufacturing products in batches is known as "batch production planning." This method allows for close monitoring at each stage of the process, and quick correction since an error discovered in one batch can be corrected in the next batch. However, batch manufacturing can lead to bottlenecks or delays if some equipment can handle more than others, so it's critical to consider capacity at every stage. Example Consider the following example of batch production planning: Jackson's Baked Goods is in the process of developing a production plan for their new cinnamon bread. To begin with, the head baker determines the batch production time required by the recipe. He then adjusts the bakery's weekly ingredient orders to include the necessary supplies and schedules the weekly cinnamon bread bake during staff downtime. Finally, he creates a list of standards for the bakery staff to check at each production stage, allowing them to quickly identify any substandard materials or other batch errors without wasting processing time on subpar cinnamon bread. Final Words Running a smooth and problem-free manufacturing operation relies heavily on a precise production planner. Many large manufacturing companies already have a strong focus on streamlining their processes and making the most of every manufacturing operation, but small manufacturing companies still have work to do in this area. As a result, plan, schedule, and control a production that will enable you to run your business in order to meet its objectives. FAQ What is the difference between planning and scheduling in production? Production planning and scheduling are remarkably similar. But, it is critical to note that planning determines what operations need to be done and scheduling determines when and who will do the operations. What is a production plan? A product or service's production planning is the process of creating a guide for the design and manufacture of a given product or service. Production planning aims to help organizations make their manufacturing processes as productive as possible.

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Since 1984, Dell has played a critical role in enabling more affordable and accessible technology around the world. As an end-to-end computing solutions company, Dell continues to transform computing and provide high quality solutions that empower people to do more all over the world.

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