This allows monitoring and control (usually by humans) the process in real time. For industrial use in manufacturing process control non-destructive characterization is the most important. Specific sensors capable of measuring and characterizing wood, including destructive and non-destructive testing, have been developed. However, the human capacity has limits and to use sensors and computers is needed to help online decision-making.įor decades, multiple sensors for generic physical variables such as pressure, speed, or temperature have been developed. Traditionally, human experience has been able to control many variables and maintain the operating system. The “wood material” adds an additional complexity degree. In industrial systems, productivity and product quality are affected by multiple variables. In manufacturing processes, process variables must be managed to achieve quality and productivity standards. Wood mechanical or chemical transformation processes are affected. Īs a biological material, wood is variable in its physical and anatomical properties. These represent 38.1% of wood-based products worldwide. In recent years, manufactured wood products for construction have had special relevance. Wood industry is key for sustainability and an important economic activity in many countries.

The adoption of these technologies (by developing countries) can foster inclusive and sustainable industrial development and the achievement of the Sustainable Development Goals. Digital manufacturing (Artificial Intelligence, bigdata analytics, cloud computing, among others) is changing the nature of manufacturing production. Manufacturing became the main engine of the economic growth in the 19th century, spreading manufacturing production technologies to other countries”. “Great Britain was the first industrializer and became the technological leader of the world economy. In industrialized nations, manufacturing has become a key growth factor.

Sensors, decision support systems and intelligent algorithms use are reviewed. This paper presents trends and opportunities provided by Industry 4.0 components. Real-time actions can be achieved by learning from data. Today, robust sensors, computing capacity, communications and intelligent algorithms permit to manage wood variability. Scanners have been developed to measure variables and outcomes, but decisions are made yet by humans. For example, in the veneer drying, density and anatomical structure impact the product quality. Wood is a material of biological origin and generates variabilities over the manufacturing processes. For the manufacturing wood processes, Industry 4.0 is a great opportunity. However, algorithms are highly sensitive of the problem and his study to decide on which work is critical. Sensors and machine learning techniques allow intelligent analysis of data. Phenomena difficult to model with conventional techniques are turned possible with algorithms based on artificial intelligence. In a new fourth industrial revolution, Industry 4.0, data engineering permits efficient decisions making. In a competitive scenario, assets availability is critical to achieve higher productivity. In manufacturing plants, wood variability turns operation management more complex.

Last wood research