our approach aimed at maximizing productivity & efficiency.
Yize.G
Founder, Lonseek Inc.
Customization Noise reduction room
Due to the wide variety of machinery and equipment, as well as varying sizes of sites, the use of standard soundproof rooms often proves to be less adaptable. Therefore, non-standard soundproof rooms are typically designed and manufactured based on the actual on-site conditions. For instance, soundproof rooms are specifically designed for various types of fans, air compressors, diesel generator sets, electric motors, water pumps, coal mills (ball mills), ultrasonic cleaning machines, crushers, abrasive machines, and so on.
Assembled lightweight steel soundproof enclosure
The assembled soundproof enclosure is primarily composed of soundproof components such as base plate steel, joists, soundproof boards, soundproof boards with soundproof doors, and soundproof boards with soundproof skylights. The sound insulation level of the assembled lightweight steel soundproof enclosure ranges from 20 to 40 dB(A).
noise control in industrial enterprises
20 years of experience in noise control,Our noise reduction technology and products has always been at the forefront of the world!
Expert in muffler manufacturing
We are the ‘guardians of tranquility’ by your side.
We build quality products
We Are fully committed to international manufacturing standards ISO 9001.
service related FAQ’s
Lean manufacturing is a systematic approach aimed at minimizing waste while maximizing productivity and efficiency.
Factories can improve product quality by implementing quality control measures, using advanced technology, providing training to employees, and listening to customer feedback.
TQM is a management approach focused on continuous improvement, customer satisfaction, and employee involvement in quality control processes.
IoT connects devices and equipment in factories to collect and exchange data, enabling real-time monitoring, remote control, and predictive maintenance.
Predictive maintenance uses data analysis and machine learning to predict when equipment failure is likely to occur, allowing for maintenance to be performed before issues arise.
