詳細(xì)介紹
智能運動增長驅(qū)動力
四個關(guān)鍵增長驅(qū)動因素正在加速智能運動控制：降低能源消耗、敏捷生產(chǎn)、數(shù)字化轉(zhuǎn)型，以及轉(zhuǎn)向基于智能制造中減少停機(jī)時間和提高資產(chǎn)利用率的基于服務(wù)的新業(yè)務(wù)模式。讓我們詳細(xì)了解這四個關(guān)鍵增長驅(qū)動因素中的每一個。
減少能源消耗
工業(yè)消耗的近 70% 的電力用于電動機(jī)系統(tǒng)。智能運動解決方案正在并將繼續(xù)通過將更多應(yīng)用從定速電機(jī)轉(zhuǎn)移到高效電機(jī)和變速驅(qū)動器來顯著降低能耗，部分原因是能源效率法規(guī)。這種能源消耗的減少將使制造更加可持續(xù)。獲得優(yōu)化制造流程的運動洞察力將進(jìn)一步降低智能制造中的能源消耗。
敏捷生產(chǎn)
隨著行業(yè)不斷適應(yīng)消費者需求和不斷變化的購買者行為，需要基于可重構(gòu)生產(chǎn)線的敏捷生產(chǎn)來提供更多定制化和更快的周轉(zhuǎn)時間。消費者需求正在推動從低混合、大批量制造向高混合、小批量制造的轉(zhuǎn)變，這要求工廠車間具有更大的靈活性。
工業(yè)機(jī)器人現(xiàn)在可以執(zhí)行復(fù)雜、重復(fù)且通常很危險的任務(wù)，從而提高產(chǎn)量和生產(chǎn)力。敏捷生產(chǎn)提高了中斷時期的彈性，并能夠更快地響應(yīng)不斷變化的客戶需求。
數(shù)字化轉(zhuǎn)型
到 2023 年，全球數(shù)字化轉(zhuǎn)型支出將達(dá)到 6.8 萬億美元。2 變速驅(qū)動器和伺服驅(qū)動器使用來自電壓、電流、位置、溫度、功率、能耗的數(shù)據(jù)以及用于監(jiān)測振動和其他過程變量的外部傳感器。借助融合的信息技術(shù)/運營技術(shù) (IT/OT) 以太網(wǎng)，運動應(yīng)用程序可以聯(lián)網(wǎng)在一起，傳遞數(shù)據(jù)和洞察力。運動數(shù)據(jù)和洞察力現(xiàn)在更易于訪問，并且可以通過強(qiáng)大的云計算和人工智能進(jìn)行分析，以優(yōu)化制造流程并監(jiān)控整個裝置中資產(chǎn)的當(dāng)前健康狀況。
以下是我司【主營產(chǎn)品】，有需要可以發(fā)來幫您對比下價格哦!
主營：世界品牌的PLC 、DCS 系統(tǒng)備件 模塊
①Allen-Bradley(美國AB)系列產(chǎn)品》
②Schneider(施耐德電氣)系列產(chǎn)品》
③General electric(通用電氣)系列產(chǎn)品》
④Westinghouse(美國西屋)系列產(chǎn)品》
⑤SIEMENS(西門子系列產(chǎn)品)》
⑥銷售ABB Robots. FANUC Robots、YASKAWA Robots、KUKA Robots、Mitsubishi Robots、OTC Robots、Panasonic Robots、MOTOMAN Robots。
⑦estinghouse(西屋)： OVATION系統(tǒng)、WDPF系統(tǒng)、MAX0系統(tǒng)備件。
⑧Invensys Foxboro(福克斯波羅)：I/A Series系統(tǒng)，F(xiàn)BM(現(xiàn)場輸入/輸出模塊)順序控制、梯形邏輯控制、事故追憶處理、數(shù)模轉(zhuǎn)換、輸入/輸出信號處理、數(shù)據(jù)通信及處理等。Invensys Triconex: 冗余容錯控制系統(tǒng)、基于三重模件冗余(TMR)結(jié)構(gòu)的現(xiàn)代化的容錯控制器。
⑨Siemens(西門子)：Siemens MOORE， Siemens Simatic C1，Siemens數(shù)控系統(tǒng)等。
⑩Bosch Rexroth(博世力士樂)：Indramat，I/O模塊,PLC控制器,驅(qū)動模塊等。
◆Motorola(摩托)：MVME 162、MVME 167、MVME1772、MVME177等系列。
PLC模塊，可編程控制器，CPU模塊，IO模塊，DO模塊，AI模塊，DI模塊，網(wǎng)通信模塊，
以太網(wǎng)模塊，運動控制模塊，模擬量輸入模塊，模擬量輸出模塊，數(shù)字輸入模塊，數(shù)字輸出
模塊，冗余模塊，電源模塊，繼電器輸出模塊，繼電器輸入模塊，處理器模塊。
Intelligent motion control is being accelerated by four key growth drivers: reduced energy consumption, agile production, digital transformation, and the move toward new service-based business models based on reducing downtime and increasing asset utilization in smart manufacturing. Let’s look at each of these four key growth drivers in detail.
Reduced energy consumption
Almost 70% of electricity consumed by industry is used by electric motor systems. Intelligent motion solutions are delivering and will continue to deliver significant reductions in energy consumption by moving more applications from fixed speed motors to high efficiency motors and variable speed drives, in part driven by energy efficiency regulations. This reduction in energy consumption will enable more sustainable manufacturing. Access to motion insights that optimize a manufacturing flow will further reduce energy consumption in smart manufacturing.
Agile production
As industries are adapting to keep up with consumer demand and changing buyer behaviors, agile production, based on reconfigurable production lines, is required to deliver more customization and faster turn-around times. Consumer demand is driving a shift away from low mix, high volume manufacturing toward high mix, low volume manufacturing, which demands greater flexibility on the factory floor.
Complex, repetitive, and often dangerous tasks can now be performed by industrial robots, leading to higher throughput and increased productivity. Agile production increases resilience in a time of disruption and enables a faster response to changing customer demands.
Digital transformation
Global spending on digital transformation will reach $6.8 trillion globally by 2023.2 Variable speed drives and servo drives use data from voltages, currents, position, temperature, power, energy consumption combined with external sensors for monitoring vibration, and other process variables. With a converged information technology/operating technology (IT/OT) Ethernet network, motion applications are networked together communicating data and insights. Motion data and insights are now more accessible and can be analyzed by powerful cloud computing and AI to optimize manufacturing flows and monitor the current state of health of the assets across the entire installation.
